In the ever-evolving era of Artificial Intelligence (AI), there is a constant ingredient which is always required for the success of AI applications - ‘good quality data’. Generating accurate data and managing it effectively is extremely important for all the companies hence it necessitates the need to have access to good data annotation platforms. According to an analysis report by Grand View Research in 2023, the market size of data annotation tools was USD 1.02 billion and it is expected to grow at a rate of 26.5% from 2023 to 2030. Additionally, it is mentioned that nearly 45% of the surveyed companies switched more than 4 data annotation tools in a year as one tool could not suffice all their requirements. Therefore, it is imperative to study various tools available in the market and make the best choice based on one’s requirements. Let’s begin. Lightly AI Lightly AI is a data labeling and ML pipeline building tool which optimizes deployment cycles and data labeling costs by selecting the most relevant data. It builds active learning loops for computer vision. Although Lightly AI has been a notable player, the capabilities it offers currently are not upto the mark based on current requirements of the industry.  Some of the challenges are listed below. Challenges Supported Data Types: It doesn’t support a wide variety of data types including multi-modal, DICOM, etc.   Automated Labeling: It doesn’t provide pretrained ML models to assist with data labeling pipelines.  Integrations: Extensive integrations with various services that provide cloud storage and integrations are not offered.  However, the AI landscape is rich with alternatives, each bringing its own strengths to the forefront with their varied capabilities and cost effective solutions. In this article we will delve into the best Lightly AI alternatives, that specialize in various aspects of developing and optimizing AI lifecycles. Let’s dive right in. 1. Encord Active  Encord Active is a powerful platform which expertises in model evaluation and observability. It improves the model performance by surfacing, curating, and prioritizing the most valuable data for training and fine-tuning. Additionally, its seamless model testing features makes the models robust and ready for production.  Key Features AI Lifecycle: Trusted by pioneering AI teams such as Stanford Medicine and Tractable, Encord Active accelerates the AI development lifecycle through its cutting-edge model evaluation, data validation, and active learning capabilities.  Failure Correction: Fixes underperforming models by allowing the explainability reports to be exported which uncovers the issues of failures.  Analytics: Provides advanced analytics to detect and rectify model weak spots such as blind spots and data drift to ensure that the model stays accurate even when the data landscape changes.  Refining Model Performance: Reduces deployment timelines by integrating humans in the loop who help to evaluate and refine the model performance iteratively by building the active learning workflows.  Automated Error Correction: Allows automating surfacing and correction of label errors by using vector embeddings, AI assisted quality metrics and model predictions.  Collaboration: Team members can be easily added to your workflows and can be designated roles such as annotator, admin, reviewer, and so on.  Feedback for Improvement: Better model performance can be achieved by inspecting the model predictions and comparing them with the ground truth data, in case of any issues it can be sent back to the labeling team for further improvements. Advanced Search: Robust and balanced datasets which cater to the model’s needs can be easily built by using the advanced search and filter functionalities in the product.  Best For Teams and enterprises who want to automate, scale, and manage their AI pipelines with a focus on medical and geospatial data.  Pricing Offers starter, team and enterprise pricing plans. 2. Scale AI The scale data engine includes features that allow efficient collection, curation, and annotation of data, along with fast training and evaluation of models. It generates high-quality labels, provides scalability for projects of differing volumes, and delivers data variety to improve model performance.  Scale AI Dashboard Key Features  Overall Capabilities: The Generative AI Data Engine of Scale AI provides capabilities for RLHF, data generation, model evaluation, and safety and alignment.  Data Labeling: For data labeling, data types of 3D, image, mapping, text, and audio are supported. Its data labeling solution combines AI techniques with humans in the loop to generate quality data efficiently.  Dataset Management Tool: ‘Label what matters’ with the intelligent dataset management tool, which helps to select the highest value data by maintaining the labeling budget.  Effective Model Evaluation: Its set of model testing, evaluation, and model comparison tools also helps to curate data smartly, sometimes even without ground truth labels.  Access to Pretrained Models: Enterprises can create, structure, or modify their data using the leading foundational models of OpenAI, Anthropic, and Google PaLM.  Best For Individuals and teams focused on improving their data with advanced generative AI capabilities.  Pricing Does not explicitly mention pricing plans. Provides sales contact instead.  3. iMerit iMerit is an end-to-end data annotation platform for Enterprise AI. It provides ready-to-use workflows for specific industry use cases and follows a strategy of automate, annotate, and analyze by keeping experts in the loop to deliver high-quality solutions at scale.  Data Annotation by iMerit Key Features Gen AI-based Data Labeling: Ango Hub is iMerit’s data annotation platform which works on a Gen AI framework to provide data labeling services for industries such as Medical AI and Precision Agriculture.  Annotation Offerings: Provides annotation services for video, text, image, and LiDAR data with integrated features like APIs, webhooks, and custom dynamic workflows.  Annotation Types: Supports multiple annotation types such as bounding boxes, polygons, keypoints, polylines, and many more. ML Assisted Labeling: ML-assisted auto-labeling can be done using their integrated plugin library for pre-labeling.  No Code Integrations: Data pipelines can be easily taken to production level with their no-code integrations with services such as AWS, Google Cloud, Azure, and external APIs.  Best For Enterprises focused on solving industry use cases in agriculture or medical AI and want to deliver their solutions at scale with easy production.  Pricing Does not explicitly mention pricing plans.  4. Dataloop  Dataloop is a tool for all the blocks required to build a complete AI pipeline. The platform offers functionality to connect with any data source, state-of-the-art models, the ability to compose custom workflows, reusable elements, humans in the loop, and pre-built AI workflows.   4-points to polygon annotation by Dataloop Key Features Speedy Workflows: Allows easy collaboration amongst large data teams with easy-to-integrate human feedback capabilities which can ultimately speed up the time spent on building workflows.  Gen AI for LLMs: Provides features to build GenAI-based multimodal pipelines using off-the-shelf or fine-tuned Large Language Models (LLMs), incorporating Retrieval Augmented Generation (RAG) techniques, and leveraging foundational models.   Supported Data Types: Supports unstructured data such as image, video, text, JSON, Lidar, audio, and GIS.  Production Grade Features: Provides production-grade AI features such as removing the overhead of managing external tools such as AWS and Azure by allowing the compute to be scaled across the entire AI workflow.  Active Learning: Enables active learning in production by allowing automatic integration of human feedback back into the models. The integrated platform combines multiple elements of active learning such as data management, RLHF, and model training.  Cloud Compute: One of their interesting workflow capabilities is chaining multiple cloud compute nodes from different vendors all in a single pipeline.  Best For Individuals, teams, and enterprises building dynamic workflows and wanting to develop and deliver solutions quickly.  Pricing For information on pricing, contact sales or fill out the request form for more details.  5. SuperAnnotate SuperAnnotate empowers enterprises to build, fine-tune, iterate, and manage their custom AI models by providing a high-quality of training data with model-assisted labeling resulting in reduced error rate and time spent on creating annotations. Data Management Platform by SuperAnnotate Key Features Data Annotation: Supports data annotation for multiple data types such as image, video, audio, text, tiled, and LLM & Gen AI. It’s advanced tools to create accurate training data.  Data Management and Querying: Provides data management and curation features such as querying and debugging to find relevant data,  obtain data insights by visualizing data trends, and advanced data versioning capabilities.  MLOps: Allows creating CI/CD AI pipelines with their python SDKs, webhooks, and inbuilt neural networks. Provides advanced orchestration to automate complex ML processes with low-code tools.  Annotation Services: Provides access to a huge and diverse annotation services marketplace with over 400 trained and professionally managed annotation teams. They offer expertise on projects across various industries and speak up to 18 languages.  Best For Enterprises focused on annotating and managing huge amounts of raw data rather than deploying their solutions to production.  requiring high quality training data  Pricing Offers Free, Pro, and Enterprise plans. 6. Labelbox  Labelbox is an all in one data-centric AI platform for creating AI ready datasets, evaluating model outputs, training and tuning LLMs, scaling RLHF and building intelligent applications in general. Labelbox Enterprise Dashboard Key Features Model Foundry: Contains a foundry of state-of-the-art foundation models to enrich the data, automate tasks, and build pipelines for applications across vision and language models. Supports building systems such as object detection, segmentation, chatbots, knowledge assistants, and generative AI applications.  Annotation Types: Supports annotation of image, video, text, document, RLHF, audio, medical, and geospatial data types. Allows for the creation of dynamic workflows by filtering on various attribute types to increase labeling efficiency with less time.  Advanced Data Searching: Leverages metadata, vector embeddings, and annotations to provide an advanced way of searching unstructured data for creating datasets.  Model Assisted Labeling: Supports model-assisted labeling by offering a variety of options for foundation models such as Google Gemini Pro, Llama 2, and Grounding DINO.  Analytics Platform: Provides an advanced analytics platform to review the performance of a project by visualizing it’s throughput, quality, and efficiency across a dashboard.  LLM Evaluation: Empowers human-centric LLM evaluation by providing capabilities for selection, benchmarking, ranking, NER, and classification.  Best For Developers, researchers, and teams building applications across vision and language use cases, and requiring flexible usage policy.  Pricing Offers Free, Starter, and Enterprise plans. Labelbox Units (LBUs) are available based on pay per use policy.  7. Aquarium  Aquarium is a ML data curation platform to surface the critical errors that hinder model performance and improve them using the right data with their embeddings. It supports only vision-related data types for tasks such as classification, segmentation, 2D and 3D object detection.     Autosegment by Aquarium Key Features Automated Workflows: Provides AI-powered workflows to speed up model improvement by surfacing the errors without requiring human effort.  Cloud for Production: Supports production workflows by providing fully managed cloud infrastructure that handles the tasks of generating, processing, and querying embeddings.  Collaboration: Provides an easy-to-collaborate platform for data labeling and analysis, which makes sharing tasks and assigning issues to other teammates hassle free.  Automation and Integration: Provides integrations with Python API, data storage services, and frameworks such as PyTorch and TensorFlow.  Best For Individuals and small teams looking to enhance their vision data and remove critical errors.  Pricing Provides Starter, Team, Business, and Enterprise plans.  8. Voxel51  FiftyOne (developed by Voxel51) is an open source toolkit for building computer vision workflows with enhanced data quality. It provides features for curating high quality datasets, and  evaluating models. Dataset Management Feature by Voxel51 Key Features Public Dataset Integrations: Provides integrations with largely popular public datasets such as COCO and Open Images. Also allows creating custom datasets from scratch.  Automatic Error Identification: Helps to automatically identify, visualize, and correct the label mistakes which cause poor model performance.  VoxelGPT: Provides an AI assistant for computer vision as a plugin called ‘VoxelGPT’ which organizes and explores data based on natural language prompts. Easy Image Search: Allows easy searching through billions of images with their advanced vector search integrations, which have capabilities such as reverse image search and semantic document search.  Collaboration: Enables secure collaboration with FiftyOne Teams either on-premises or in the cloud, which allows access to all the workflows built with FiftyOne. Provides cloud-based media storage easily accessible from the browser or Python SDK. Best For Individuals and teams looking to build custom computer vision workflows, starting from creating datasets to evaluating models for production-readiness.  Pricing Does not explicitly mention pricing plans; contact sales.  9. Aporia  Aporia is an AI control platform which provides real-time control over AI apps with its two major technology stacks - Aporia Guardrails and Aporia Observe. The former focuses on GenAI integrity and AI security while the latter is about model monitoring, observation, and evaluation.  Model Management with Aporia Key Features Responsible LLM Interactions: Provides real-time hallucination mitigation, blocks prompt injection attempts, prevents off-topic conversations, and builds custom AI policies to ensure responsible interactions.  Integration: Offers easy integration of GenAI guardrails with a single API call. Insights: Generates user-engagement insights based on LLM interactions and offers budget tracking by monitoring costs of queries and tokens. ML Observability: Offers ML observability features such as dynamic dashboards, model monitoring, root cause analysis, and explainability.  Best For Individuals, small teams, and enterprises focused on AI security, gaining insights, and monitoring the outcomes of ML models.  Pricing Provides Guardrails Trial (free), Guardrails Pro, and Guardrails Enterprise (custom) plans.  10. Picsellia  Picsellia is a unified platform to build, deploy, and improve all Computer Vision (CV) related products. It offers a complete MLOps stack to build and manage data, manage models, perform continuous CI/CD and implement serverless model deployment.  Model Training with Picsellia Key Features Supported Data Types: The annotation tool supports various data types including multispectral images. It enhances the data annotation efficiency with model-assisted labeling and allows seamless collaboration within the team. Experiment Tracking: Provides experiment tracking to help compare different experiments which assists to choose the best performing model based on the metrics.  Deployment: Provides serverless deployment service based on pay-as-you-go model.  Model Analysis: Allows real-time model performance analysis and identifies edge-cases to further improve the model.  Best For Individuals and small teams focused on building and deploying computer vision solutions.  Pricing Offers Standard, Business, and Enterprise along with custom plans. Lightly AI Alternatives: Key Takeaways Lightly AI Limitations: Lightly AI offers very limited features. Some of the main concerns are its limited support to data types allowed, access to few integrations to build workflows, and unavailability of pre-trained models. Additionally, it does not offer the latest Gen AI and LLM based integrations and support for applications.  Alternatives to Lightly AI:  Offer a broad set of features and capabilities for building AI lifecycles.  Extensive repositories of pre-trained and ready-to-use application oriented models.  Scalable model building, monitoring, testing, and production services.  Automations with Gen AI and integrated support for LLMs.

With almost 60% of organizations planning to integrate CV into their daily operations, technology is becoming indispensable for surviving in the modern industrial space. However, reports predict users will generate around 175 zettabytes of data by 2025, calling for robust CV software solutions to streamline the development and deployment processes. Supervisely is a popular choice for managing large-scale CV operations among the many platforms available in the market. However, due to its high price tag, the tool may not be suitable for small—to medium-sized businesses. This article discusses the top alternatives to Supervisely to help you choose a platform that suits your needs. The list below highlights the major solutions mentioned in the following sections. Encord Amazon SageMaker Ground Truth VGG Image Annotator Keymakr Hive Dataloop AI Kili Computer Vision Annotation Tool (CVAT) Super.ai SuperAnnotate Segments.ai Labelbox Supervisely Supervisely is a data labeling and model-building platform that lets you annotate images, videos, point-cloud, and Digital Imaging and Communications in Medicine (DICOM) data. It also features smart labeling tools that use artificial intelligence (AI) to automate annotation workflows. The solution also helps with data management by offering data import and export functionalities with additional features to process complex datasets. Its collaboration tools allow you to share and manage assets with team members more efficiently through workspaces and projects. Further, its ecosystem includes pre-built neural networks that allow you to develop high-quality machine learning models (ML models) for multiple use cases through an intuitive user interface (UI). Users can also visualize performance metrics to analyze model failures and debug issues. Challenges While Supervisely is a cloud-based solution for data annotation and management, it has a few limitations that make it unsuitable for specific applications. Challenging to use: The tool becomes difficult to learn for new users, who may become overwhelmed by the numerous options for labeling, managing, and visualizing data. Limited annotation methods: While Supervisely supports annotation for multiple modalities, it lacks sufficient methods to label modern data objects. High up-front costs: Although Supervisely offers a free community version, it falls short of the features required for large-scale projects. The Pro and Enterprise versions have an extremely high price tag, making them only accessible to larger enterprises with hefty budgets. Due to these challenges, Supervisely is a poor option for startups in the CV industry who want a simple tool for building novel applications. So, let’s see what alternatives are available to help new businesses get started with CV systems quickly. Struggling with computer vision data management? Read our article to find out about the best data management tools. Supervisely Alternatives With Supervisely being an expensive solution, the list below mentions multiple alternatives ranked according to affordability, ease of use, annotation methods, and other data and model management features. 1. Encord Encord is an end-to-end data-centric platform that enables you to annotate, curate, and manage computer vision datasets through AI-assisted annotation features. It also provides intuitive dashboards to view insights on key metrics, such as label quality and annotator performance, to optimize workforce efficiency and ensure you build production-ready models faster. The platform consists of three tools that help you with high-quality data preparation for building AI applications: Encord Annotate: Includes basic and advanced features for labeling image data for multiple CV use cases. Encord Active: Supports active learning pipelines for debugging datasets and evaluating CV models. Index: Helps curate and manage multi-modal data. Encord Key Features Ease-of-use: Encord Annotate offers an intuitive user interface (UI) and an SDK to label and manage annotation projects. Affordability: Encord offers multiple plans to suit different needs. It has a Starter plan for small teams, a Team plan for scaling AI models, and an Enterprise plan offering features for managing large-scale projects. Supported data types: The platform lets you annotate images, videos (and image sequences), DICOM, and mammography data. Supported annotation methods: Encord supports multiple annotation methods, including classification, bounding box, keypoint, polylines, and polygons. Other Features Scalability: The platform allows you to upload up to 500,000 images (recommended), 100 GB in size, and 5 million labels per project. You can also upload up to 200,000 frames per video (2 hours at 30 frames per second) for each project. See more guidelines for scalability in the documentation. Collaboration: To manage tasks at different stages, you can create workflows and assign roles to relevant team members. User roles include admin, team member, reviewer, and annotator. Automated labeling: The platform speeds up the annotation with automation features, including: - Segment Anything Model (SAM) to automatically create labels around distinct features in all supported file formats. - Interpolation to auto-create instance labels by estimating where labels should be created in videos and image sequences. - Object tracking to follow entities within images based on pixel information enclosed within the label boundary. Integration: Integrate popular cloud storage platforms, such as AWS, Google Cloud, Azure, and Open Telekom Cloud OSS, to import datasets. Data security: Encord complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards. It uses advanced encryption protocols to ensure data security and privacy. Best For Teams of all sizes looking for a complete CV solution to build innovative applications. Pricing Encord has a pay-per-user pricing model with Starter, Team, and Enterprise options. Having trouble curating data for computer vision? Learn about data curation for computer vision tasks 2. Amazon SageMaker Ground Truth Amazon SageMaker Ground Truth (GT) is a labeling platform that uses human feedback to speed up annotation. It also allows you to customize, monitor, and evaluate ML models for multiple use cases. Amazon SageMaker Ground Truth Key Features Ease-of-use: The platform features an intuitive UI with clear instructions to help new users with quick learning. Annotation Methods: SageMaker GT offers all the standard labeling methods for image classification, object detection and tracking, semantic segmentation, and text classification. Other Features Reinforcement Learning with Human Feedback (RLHF): SageMaker GT allows users to enhance model training using RLHF functionality, which leverages human feedback to optimize model performance. Model Evaluation: The platform offers multiple metrics to evaluate large language models (LLMs), including response relevance, toxicity, bias, brand voice, and style. Custom Workflows: You can create customized labeling workflows through pre-defined templates for a faster annotation process. Best For Enterprises looking for a solution to build multi-modal models using text and vision. Pricing Pricing depends on the labeling workload. 3. VGG Image Annotator VGG Image Annotator (VIA) is an open-source web-based labeling tool that allows users to label large datasets through a two-stage process involving automated labeling and manual review. VIA Key Features Affordability: VIA is free to use, making it suitable for small-scale researchers who want to experiment with multiple labeling tasks. Ease-of-use: The platform is straightforward, requiring no prior expertise or complex configurations. Annotation methods: VIA supports rectangles, circles, ellipses, polygons, polylines, and key points. Other Features Supported formats: Users can import and export data in CSV and JSON formats. Filtering, Selection, and Update: VIA lets annotators filter, select, and update annotations to ensure high labeling quality. Best For Individual researchers working on small-scale projects. Pricing The tool is open-source. . 4. KeyMakr KeyMakr is a labeling service provider that helps users with image and video annotation. It also offers data curation services. Keymakr Key Features Affordability: KeyMakr is suitable for companies that find it expensive to hire and manage an in-house labeling team. Ease-of-use: Since KeyMakr handles all the labeling and technical requirements, users only need to submit their data and specify their use case. Annotation Methods: The service supports multiple methods, including bounding boxes, cuboids, polygons, semantic and instance segmentation, and key points. Other Features Data Validation: The platform offers services to validate training data to ensure its accuracy, consistency, and relevance. Data Collection and Creation: KeyMakr helps you collect and create suitable data according to your requirements. Best For Teams looking for a company to outsource their labeling projects. Pricing Pricing is not publicly available 5. Hive Hive is a content moderation platform that uses deep learning models to flag harmful or explicit content in images, videos, text, and audio. Hive Key Features Ease of use: Hive has an intuitive interface and a moderation dashboard that lets you visualize content and control model outputs through custom moderation policies. Other Features AutoML: The AutoML feature lets you build models from scratch without using any code. Embeddings: The platform lets you quickly create text embeddings to augment LLMs with retrieval augmented generation (RAG). Search: Hive offers a flexible web search functionality where users can provide an image and get relevant links to similar images. Generative AI (Gen AI): Hive features APIs to generate text, images, and videos based on textual prompts. Best For Teams looking for content moderation solutions to enhance application safety. Pricing Pricing is not publicly available. 6. Dataloop AI Dataloop is an all-in-one platform for managing data and building models that helps you through each stage of the mode development lifecycle. Dataloop Key Features Ease-of-use: The platform provides a no-code UI to build data and modeling pipelines using simple drag-and-drop functionality. Other Features Supported Data Types: Dataloop supports image, video, text, audio, and point cloud data. Active Learning Pipelines: The platform lets you create active learning pipelines to speed up annotation. Model Marketplace: Dataloop lets users pick the latest state-of-the-art (SOTA) models from its marketplace and use them directly in the platform for specific use cases. Elements: The platform enables you to add pre-built functions called elements to enhance your applications. Best For Large teams consisting of data scientists, engineers, and software developers requiring strong collaboration. Pricing Pricing is not publicly available. 7. Kili Kili is a data platform for building high-qualitytraining data to create LLMs and generative AI models. It offers relevant annotation tools and quality metrics to boost annotation speed and quality. Kili Key Features Affordability: Kili offers a free version suitable for small-scale projects with up to 100 annotations. The Grow and Enterprise versions feature custom prices based on your needs. Ease of use: The platform offers an intuitive UI for managing projects, exploring data, and analyzing annotation progress and quality. Annotation Methods: Kili supports bounding boxes, polygons, key points, cuboids, and landmark annotation. Other Features Automation: The tool lets you pre-label textual data using ChatGPT and Segment Anything Model (SAM) to label images for semantic segmentation tasks. Plugins: The platform enables you to build custom plugins through its Python SDK for more efficient labeling. Best For Teams looking for a labeling solution that supports CV and natural language processing (NLP) tasks. Pricing Kili offers custom pricing based on specific user needs. 8. CVAT The Computer Vision Annotation Tool (CVAT) is a solution for labeling images and videos in multiple formats. It uses modern deep-learning frameworks to boost annotation speed and quality. CVAT Key Features Affordability: CVAT offers a free version for personal and charges only USD 33 per month for professional and organizational use. Ease-of-use: The platform provides an easy-to-use interface with straightforward navigation and a clear workspace. Annotation Methods: The tool supports bounding boxes, polygons, polylines, points, ellipses, and cuboids. Other Features Automation: You can use built-in CV models, such as YOLO v7 and Faster RCNN, to automatically label your data. Performance Monitoring: CVAT lets you monitor annotation quality through a performance dashboard featuring metrics like objects labeled per hour, object count, timelines, etc. Best For Beginners looking for a lightweight annotation tool to create basic CV applications. Pricing CVAT offers a free, Solo, and Team version. 9. Superb AI Superb is an AI platform that offers multiple tools to curate and label data. It also helps with model training and deployment, providing apps to automate AI workflows. SuperbAI Key Features Ease-of-use: The tool features a straightforward UI with clearly organized sections highlighted with self-explanatory options. Annotation Methods: Superb Label supports bounding boxes, polygons, polylines, key points, and cuboids. Other Features Automation: The Auto-Label feature lets you annotate everyday objects using a pre-trained model that is available out of the box. Data Curation: Superb Curate generates data clusters based on embeddings to help users organize their data assets more efficiently. Superb Model: Superb Model provides a complete no-code solution to train and deploy baseline models using data curated through Superb Curate. Best For Teams looking for a solution to manage complex data. Pricing Pricing is not publicly available. 10. SuperAnnotate SuperAnnotate is an AI platform for building LLMs and Gen AI models through a user-friendly interface. It also includes annotation tools to label images, videos, text, and audio. SuperAnnotate Key Features Affordability: SuperAnnotate’s free version offers sufficient features for startups and independent researchers to work on standalone projects. Ease of use: The platform’s UI offers guided steps for building efficient LLMs and Gen AI frameworks. Annotation Methods: The tool supports bounding boxes, key points, segmentation masks, OCR annotation, speech recognition, and sound event detection. Other Features Data Curation: SuperAnnotate lets you create data subsets and query relevant items through advanced filtration and visualizations. Automation: The tool enables you to build automated pipelines to manage annotation projects at different stages of the development lifecycle. Best For Teams looking for a platform to orchestrate LLMs and Gen AI model development. Pricing Pricing is not publicly available. 11. Segments.ai Segment.ai is a data labeling tool that annotates multi-sensor data through a single interface by combining 3D point clouds and 2D image data. Segments.ai Key Features Affordability: The tool is suitable for large organizations that collect data from multiple sensors and edge devices. Ease-of-use: The platform features intuitive interfaces to label 2D and 3D image data. Annotation Methods: Segments.ai supports bounding boxes, cuboids, segmentation masks, polylines, polygons, and key points. Other Features 3D to 2D Projection: The platform allows you to project 3D cuboids on 2D bounding boxes for greater labeling accuracy. Sensor Fusion: The tool lets you combine data from multiple sensors and label them through a single interface. Moving Object Annotation: Segments.ai supports real-time interpolation and ML-based object tracking to annotate moving objects. Best For Teams looking for an advanced solution for labeling sensor data. Pricing Segments.ai offers a Team, Scale, and Enterprise version. 12. LabelBox LabelBox is a data management solution that lets you curate and label unstructured data using pre-trained foundation models to automate the annotation process. LabelBox Key Features Affordability: LabelBox offers a free version for individuals and a Starter version that charges USD 0.1 per LabelBox unit. Ease of use: The tool features user-friendly interfaces for exploring and visualizing datasets, annotating data items, and evaluating model outputs through intuitive dashboards. Annotation Methods: LabelBox supports bounding boxes, segmentation masks, polygons, polylines, and cuboids. Other Features Workflows: The tool lets you create custom pipelines to review and monitor annotation progress. Foundry: LabelBox uses Foundry, which consists of foundation models to predict labels and allows you to compare the performance of different models. Best For Medium-sized enterprises looking for a cost-effective labeling solution for CV and NLP tasks. Pricing The tool offers a Free, Starter, and Enterprise version. Supervisely Alternatives: Key Takeaways AsCV and NLP models become integrated into every aspect of business operations, investing in a scalable AI platform is becoming a strategic necessity to stay ahead of the competition. Below are a few points to remember regarding such AI platforms. Supervisely’s Limitations: While Supervisely is a comprehensive solution to manage data and build models, it is expensive and only suits organizations with extensive cloud-based infrastructure. Essential Factors: Businesses and researchers should consider a tool’s total cost of ownership, which includes its upfront costs and maintenance expenditures. Further, they must determine its ease of use and ensure it covers all the necessary features to handle the ever-evolving data volume and variety. Supervisely Alternatives: Encord, SuperAnnotate, and Kili are a few cost-effective, easy-to-use, and feature-rich alternatives to Supervisely.

Machine learning (ML) observability tools are indispensable for data scientists and developers. They provide deep insights into understanding, monitoring, and improving the performance of ML models.  These tools are crucial in identifying potential issues, such as prediction drift or data anomalies, and facilitating performance improvements. Beyond monitoring real-time performance and ensuring model accuracy, observability tools are pivotal in promoting accountability, especially in navigating the complex ethical issues in AI. Arize AI is an ML observability platform that provides real-time model performance monitoring, anomaly detection, and root cause analysis. It integrates with existing ML pipelines with metrics and visualizations to track model reliability over time. This is critical for managing and improving the functionality of ML models, even large language models (LLMs), which present unique challenges due to their complexity and scale. But what if Arize AI is not the right fit for you or what your project needs? Finding suitable alternatives is a good next step. This article guides you through 12 alternative tools for Arize AI. Each tool addresses specific challenges and may be worth exploring based on our requirements. Here’s the list:  Encord Active Aporia DeepChecks Fiddler AI Superwise SageMaker Model Monitor WhyLabs  IBM Watson Studio Voxel51 Azure Machine Learning Databricks and  Vertex AI.  Let us take a closer look at these alternatives. Encord Active Encord is an end-to-end computer vision (CV) platform that enables you to annotate and curate datasets through AI-assisted features. The platform’s data-centric approach helps you curate high-quality data, evaluate and monitor models, and use active learning techniques to improve them. Optimize model performance with Encord Annotate and Active. Encord Active is part  of the suite of Encord tools that help you monitor models based on the quality of data and predictions: Encord Annotate: Includes basic and advanced features for labeling image data for multiple CV use cases. Encord Active: Monitor models based on data quality and use the AI-assisted features to perform root-cause analysis. Index: Helps curate and manage multi-modal data. Encord Active. Key Features Scalability: The platform allows you to upload up to 500,000 images (recommended), 100 GB in size, and 5 million labels per project. You can also upload up to 200,000 frames per video (2 hours at 30 frames per second) for each project. See more guidelines for scalability in the documentation. Data Quality Monitoring: Encord Active has the advantage of native integration with a smart annotation tool in Annotate. For a project, it monitors the quality of input data, checking for missing values, outliers, and ensuring data types and schema are consistent with what the model expects using data quality metrics. Customization: It provides the option of monitoring custom metrics you use to evaluate your model’s performance and data quality outside the extensive pre-built metrics. You can upload your predictions manually through the UI or programmatically from your production environment through the SDK. Auto-Discovery: Encord Active provides a quick method to filter unwanted or problematic images by auto-discovering the issues (e.g., anomalies, label quality, etc.) in your model predictions with ML. Feature Importance Tracking: Track the impact of label and data quality on the features your model predicts to get granular insights into your model’s performance scores for better debugging.  Model Version Tracking: Active allows you to track multiple prediction sets from different model versions and compare the performance of models based on quality metrics on your validation or production data. Compliance and Governance: Encord complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards. It uses advanced encryption protocols to ensure data security and privacy. Collaboration: It allows users a way to communicate with annotators, reviewers, team managers, and administrators within annotation projects and active projects through comments. Model Optimization Workflow: Because Active also provides insights into the data quality and is tightly integrated with an annotation tool, you can build a loop of continually improving your model by curating high quality data, evaluating model performance, adding newly annotated images, re-training, and performing comparisons against production models. Best for Computer Vision Engineers who prioritize data quality, comprehensive model evaluation tools, and a data-centric approach to improve production model performance. Pricing Encord has a pay-per-user pricing model with Starter, Team, and Enterprise options. Aporia Aporia is an ML observability platform for data science teams across various industries. It supports all use cases and accommodates various model types, including LLMs, tabular models, NLP, and computer vision.  Aporia Dashboard Key Features  Scalability: Aporia focuses on real-time monitoring of billions of predictions without compromising data integrity, suggesting it is good at scaling to large datasets.  Customization: Aporia offers customizable dashboards, thresholds, and language flexibility. Better Insights: Aporia provides comprehensive observation capabilities, addressing ethical concerns and enhancing interpretability.  Centralized Model Management: Aporia's centralized system provides comprehensive observability across models, focusing on drift, bias, and feature importance. Production Incident Response: Aporia is a collaborative notebook-like environment for root cause analysis and actionable insights, making it a solid choice for production incident response. Best for CV engineers, ML engineers, data scientists, and researchers.  Pricing Free tier for its Guardrails (protecting from LLM risks) and Observe (monitoring ML models) products. Paid plans offer more features, users, tokens (for Guardrails), and functionalities like custom dashboards (Observe) with custom pricing for Enterprise plans. Recommended Read: A Guide to Machine Learning Model Observability.   DeepChecks DeepChecks is a holistic open-source solution for all your AI and ML validation needs. It enables comprehensive testing of data and models from research to production. It ensures model quality, compliance, and robustness, making it an essential toolkit for NLP practitioners, researchers, and organizations. Deepchecks Key Features Scalability: It's built to handle a large number of models in parallel and works well even as the data scales. Customization: It emphasizes flexibility, customizable dashboards, and proactive alerts to track data science metrics and business KPIs. Beta Program for GPT-4: This is not just a program; it’s a ticket to explore next-gen LLMs like GPT-4. Best for  NLP practitioners, researchers, and organizations who want comprehensive oversight of their language models.   Pricing Different pricing tiers, including Startup, Scale, and Dedicated plans, cater to various needs and ensure user flexibility. Fiddler AI Fiddler AI is a pioneering platform for AI Observability that empowers enterprises to monitor, explain, analyze, and improve their models and generative AI applications. Fiddler ensures high performance, model quality, and responsible AI practices by providing actionable insights and transparency. It offers interactive, explainable AI, model monitoring, and support for the entire ML lifecycle. Fiddler AI Key Features  Scalability: It unifies model monitoring, explainable AI, performance analytics, and fairness capabilities within a single system. That is why, in terms of scalability, Fiddler AI stands out for its purpose-built platform, unified features, enterprise-scale capabilities, and actionable insights.  Customization: Fiddler AI is a customizable monitoring tool with dashboards, thresholds, and metrics for enhanced responsiveness and agility. It allows users to track key metrics and meet business requirements. Better Insights: Fiddler AI covers explainability techniques like feature attribution, SHAP values, and counterfactuals and allows users to customize explanation details. Diverse Features: Fiddler AI stands out from Arize AI due to its customizable analytics and what-if analysis capabilities. It also prioritizes regulation-friendly transparency and compliance, ensuring responsible AI practices while adhering to privacy standards. Best for  Data scientists, machine learning engineers, and AI practitioners who need comprehensive model monitoring, explainability, and compliance features. Pricing Pay-per-use pricing model for users to pay for the specific features and resources they utilize. This flexible approach ensures cost-effectiveness and scalability based on individual needs and usage patterns. Superwise Superwise AI is an observability platform designed to enhance the management and monitoring of models in production. It provides visibility and context into model behavior, allowing teams to identify anomalies, assess risks, and collaborate effectively to resolve issues. Superwise AI Key Features  Scalability: Superwise AI suits businesses with numerous models, handling large-scale ML operations. Customization: Superwise provides customizable monitoring features to meet users' business needs. Better Insights: SuperWise AI ensures the trustworthiness of AI models with customizable monitoring features. Diverse Features: SuperWise AI provides observability, customization, and insights for ML models. It includes AI performance management, bias detection, explainability, and analytics capabilities. Best for  Data scientists and ML engineers seeking trustworthy, reliable, and compliant AI models. Pricing Flexible pay-as-you-go pricing model without feature lockouts, where costs are based on model usage. The first three models are free with up to 1 million monthly predictions, and volume discounts are available as usage grows. SageMaker Model Monitor Amazon SageMaker is a fully managed ML service provided by AWS. It simplifies the process of building, training, and deploying ML models at scale. With SageMaker, data scientists and developers can quickly and confidently create, train, and deploy ML models in a production-ready hosted environment. Amazon Sagemaker Key Features  Scalability: Amazon SageMaker offers robust scalability, a comprehensive integrated development environment (IDE), and seamless integration within the AWS ecosystem. Customization: Amazon SageMaker offers extensive customization options, allowing users to tailor ML workflows, choose from various instance types, and leverage AWS services. SageMaker supports a wide range of frameworks and algorithms, providing high customization. Better Insights: Amazon SageMaker provides tools for data scientists and developers to gain better insights from their data. Features like SageMaker Canvas chat for data prep and Foundation model evaluations (FMEval) help users create data preparation flows and quantify the risk of providing inaccurate or biased content with their language models. Diverse Features: Amazon SageMaker offers features catering to various ML aspects. This includes the Code Editor for writing and testing code, Deep learning containers for large model inference, and SageMaker HyperPod for developing large models. Best for  Data and ML teams that are already building and deploying their tech stack on AWS. Pricing Pay-as-you-go pricing model where you are billed by the second for building, training, and deploying models, with no minimum fees or upfront commitments.  WhyLabs WhyLabs is an AI observability platform that prevents data quality issues and model performance degradation. By monitoring data pipelines and machine learning models in production, WhyLabs helps detect data drift, concept drift, and data quality issues. The platform is based on the Whylogs open-source library, which generates statistical summaries for privacy-preserving observability and monitoring at scale. WhyLabs AI Key Features  Scalability: WhyLabs offers a platform that scales seamlessly with your data. It can handle massive amounts of data and provide real-time actionable insights, making it suitable for large-scale AI operations. Customization: WhyLabs seamlessly integrates with existing data pipelines, allowing tailored customization for specific workflows.  Better Insights: With WhyLabs, teams can detect data and model drift, identify training-serving skew, and continuously monitor key performance metrics. Diverse Features: The platform supports structured or unstructured data, batch or streaming data, and integrates with various data platforms. It offers functionalities like outlier detection, histograms, data drift monitoring, and missing feature values, catering to a wide range of AI monitoring needs. Best for  Organizations (including financial services, logistics, manufacturing, retail, e-commerce, and healthcare), data engineers, and machine learning practitioners that need to monitor and maintain the quality of their data pipelines and ensure that models perform optimally in production. Pricing Structured to accommodate a range of users, offering a complimentary Starter plan for individuals, an Expert plan for smaller teams, and customized solutions for larger enterprises with specific scale, security, and support needs. IBM Watson Studio IBM Watson Studio is an integrated development environment (IDE) that empowers data scientists, developers, and analysts to build, run, and manage AI models. It is available as Software as a Service (SaaS) or for self-hosting as part of IBM Cloud Pak for Data. With Watson Studio, you can work with open-source frameworks, visual modeling, MLOps, decision optimization, NLP, and generative AI. It provides a collaborative workspace for solving business problems through data science. IBM Watson Studio Key Features  Scalability: Watson Studio supports an open multi-cloud architecture, which allows for scaling AI models across different cloud environments. This ensures that as your data grows or your computational needs change, providing the flexibility to build, run, and manage AI models at scale.  Customization: The platform offers a range of tools and languages such as Python, R, and Scala, and integrates with open-source frameworks like PyTorch, TensorFlow, and scikit-learn.. Better Insights: Watson Studio provides capabilities for visual modeling, decision optimization, and natural language processing, which can help in gaining better insights from your data.  Diverse Features: Watson Studio combines features such as MLOps, decision optimization, visual modeling, and AI governance. It also offers tools for data preparation, model building, and deployment, making it a comprehensive solution for various data science and ML tasks. Best for  Data scientists, developers, and analysts who need an integrated environment for building, running, and managing AI models. Pricing Flexible pricing model that includes both a free Lite plan and a professional plan. The Professional plan operates pay-as-you-go, charging for compute usage measured in capacity unit hours. Voxel51 Voxel51 is an AI software company that provides open-source computer vision tools for ML. Their flagship product, FiftyOne, enables data scientists to curate and experiment with datasets, leading to the development of higher-performing machine learning models. With applications in various sectors such as agriculture, aviation, healthcare, retail, and manufacturing, Voxel51 aims to enhance the quality and efficiency of computer vision workflows. Voxel51 Key Features  Scalability: FiftyOne can manage billions of samples and supports secure collaboration on datasets in the cloud, making it highly scalable for large-scale projects. Customization: While Voxel51 provides intuitive visualizations and allows users to add metadata to samples, its customization options are limited. Better Insights: Voxel51’s FiftyOne Teams enable better insights by identifying data problems early, validating hypotheses, and improving solution engineering for products and services. Diverse Features: It offers a range of features, including the ability to clean and relabel datasets efficiently, convert model predictions into training datasets, and organize large research datasets. Users can view distributions over image attributes and filter datasets by those attributes. Best for Data scientists, machine learning engineers, and computer vision researchers who work with large-scale datasets and need powerful tools for data exploration, visualization, and curation. Pricing Voxel51 offers its core computer vision toolkit, FiftyOne, as an open-source platform, which means it is available free of charge. They also provide a free trial for their Teams version, which includes additional features for collaboration and dataset management in the cloud. Azure Machine Learning Azure Machine Learning (Azure ML) is a cloud-based platform that empowers data scientists, developers, and businesses to create, train, and deploy machine learning models. With Azure ML, you can build pipelines, experiment with algorithms, and integrate predictive models into your applications. Azure Machine Learning Key Features  Scalability: Azure ML provides a highly scalable platform that allows for the dynamic adjustment of resources to meet demand. It supports horizontal and vertical scaling, ensuring your applications can handle varying loads efficiently.  Customization: The platform balances out-of-the-box solutions and custom-built options, allowing for precise tailoring of services to meet specific needs. This means you can select services that are both forward-thinking and customized, considering the trade-offs between convenience and customization. Better Insights: Azure ML provides advanced analytics and automation capabilities. This empowers businesses to leverage better insights from their data, enabling informed decision-making and strategic planning. Diverse Features: Azure’s diverse features include secure storage options, reliability, and diverse data handling capabilities. The platform’s extensive suite of services covers various aspects of cloud computing, from databases and AI to DevOps and the Internet of Things (IoT), ensuring a comprehensive set of tools. Best for  Data scientists, developers, and enterprises building on Microsoft Azure and seeking a comprehensive platform for building, training, and deploying models. Pricing Flexible pricing options, including pay-as-you-go, savings plans, and reserved instances. Additionally, Azure offers options for reserved instances and spot instances for more cost-effective solutions.  Databricks Databricks is a platform that uses generative AI within the context of a data lakehouse. It understands the unique semantics of your data, optimizes performance, and manages infrastructure to match your business needs. Databricks AI Key Features  Scalability: Databricks provides a scalable and reliable platform capable of handling large datasets and complex workflows. This allows for dynamic scaling of compute resources based on workload demands. Customization: Users can build and manage complex data pipelines using various programming languages, including Python, Scala, and R. Databricks provides comprehensive customization for data processing, analytics, and machine learning workflows. Better Insights: Databricks allows users to share notebooks, data, and insights with team members, facilitating real-time collaboration on data engineering and machine learning tasks, and deriving better insights. Diverse Features: Databricks integrates with various Azure services, providing a comprehensive environment for data engineering, data science, and analytics. It also automates cluster provisioning, auto-scaling, and job scheduling, contributing to a diverse set of features that enhance the overall functionality of the platform. Best for  Data and ML teams that want to work very closely. Pricing Consumption-based pricing model, where users are billed for the resources they utilize, such as virtual machines, runtime hours, and data storage. The platform offers flexible pricing tiers and committed-use discounts for different usage levels, allowing for cost savings compared to pay-as-you-go rates. Vertex AI Vertex AI is a Google Cloud ML platform that provides a unified environment for training, deploying models, and customizing large language models (LLMs) for AI applications. The platform offers options for AutoML, custom training, and access to generative AI models for various modalities like text, code, images, and speech. Vertex AI Dashboard Key Features  Scalability: Vertex AI provides scalability through distributed training across GPUs and TPUs, allowing for the training of models at scale. Vertex AI focuses on scalability through distributed training and serverless model serving.  Customization: The platform includes multiple customization options, supports various ML frameworks like TensorFlow, GPTConsole, Slingshot, Cameralyze, and Kedro, allowing for a tailored ML experience. Vertex AI provides customized ML models through AutoML and custom training. Better Insights: Vertex AI provides better insights by making the ML journey more approachable for data scientists and integrating with popular ML frameworks. Diverse Features: It includes over 100 foundation models. Users work with Vertex AI to automate or streamline their ML projects with functionalities like data preparation, model training, model evaluation, and more. Best for  Computer vision, ML engineers, and data scientists who build, deploy, and manage models on Google Cloud. Pricing Flexible pricing model based on usage. For custom models, you pay for training based on data size and complexity, and for deployment, you’re billed per model endpoint. Generative AI charges depend on input and output characters.  Arize AI Alternatives: Key Takeaways ML Observability Tools are essential for data scientists and developers to understand, monitor, and improve the performance of machine learning models. Arize AI is a platform that features AI observability, automated model monitoring, embedding and cluster evaluation, and prompt iteration/troubleshooting. Benefits of Arize AI: Arize AI is a machine learning observability platform that offers real-time monitoring, anomaly detection, and root cause analysis. It integrates with existing ML pipelines, providing metrics and visualizations to track model accuracy and dependability over time, including AI and ML observability. Limitations of Arize AI: Like any other tool, Arize AI faces challenges like model drift, performance degradation, data drift, concept drift, black box models, data quality issues, and model readiness. Alternative tools may offer better monitoring, interpretability, data quality, and readiness checks. Alternatives to Arize AI (2024): Notable alternatives include Encord Active, Aporia, DeepChecks, Fiddler AI, Superwise, SageMaker Model Monitor, WhyLabs, IBM Watson Studio, Voxel51, Azure Machine Learning, Databricks, and Vertex AI, that offer larger scalability, more customization freedom, better insights, and other diverse features.