Scale AI Alternatives: Why AI Teams Choose Encord

On June 12 2025 Meta confirmed a $15 billion investment for a 49 % non-voting stake in Scale AI—and hired CEO Alexandr Wang to run its new “super-intelligence” group. Customers such as Google have already paused work with the platform and Encord is experiencing and influx of customers looking for alternative to Scale.

If you need vendor neutrality or faster throughput, now is the ideal moment to benchmark alternatives offering:

• lower or more predictable unit economics,
• modern quality-assurance workflows and audit trails,
• seamless SDK and API integrations, and
• true multimodal support, including emerging 3D sensors.

The best AI teams are moving to Encord because these features matter to them.

Encord is the scalable multimodal data engine alternative to Scale

Encord’s platform indexes, curates, and annotates petabyte-scale datasets spanning images, video, DICOM, audio, documents, and—as most recently—LiDAR and point-cloud data. The new Physical AI suite unifies 3D, camera, and radar streams inside one labeling workflow, giving robotics and autonomy teams a single pane of glass for perception data.

The influx of customers migrating from Scale to Encord are mentioning that these aspects matter the most to them:

• Encord's LiDAR launch: ingest MCAP or LAS files, visualize multi-sensor scenes, and auto-propagate 3D boxes across sequences.
• Encord scales from single-GPU prototypes to petabyte deployments without re-architecting.
• The platform works on-prem, in VPC, or SaaS; SDK covers import → train → export.

Encord offers a suite of tools designed to accelerate the creation of training data. Encord's annotation platform is powered by AI-assisted labeling, enabling users to develop high-quality training data and deploy models up to 10 times faster. Encord’s active learning toolkit allows you to evaluate your models, and curate the most valuable data for labeling. See also the detailed Scale vs Encord comparison for pricing and workflow nuances.

Encord ML Pipeline & Features

• State-of-the-art AI-assisted labeling and workflow tooling platform powered by micro-models
• Perfect for image, video, DICOM, and SAR annotation, labeling, QA workflows, and training computer vision models
• Native support for a wide range of annotation types, including bounding box, polygon, polyline, instance segmentation, keypoints, classification, and more
• Easy collaboration, annotator management, and QA workflows to track annotator performance and ensure high-quality labels
• Utilizes quality metrics to evaluate and improve ML pipeline performance across data collection, labeling, and model training stages
• Effortlessly search and curate data using natural language search across images, videos, DICOM files, labels, and metadata
• Auto-detect and resolve dataset biases, errors, and anomalies like outliers, duplication, and labeling mistakes
• Export, re-label, augment, review, or delete outliers from your dataset
• Robust security functionality with label audit trails, encryption, and compliance with FDA, CE, and HIPAA regulations
• Expert data labeling services on-demand for all industries
• Advanced Python SDK and API access for seamless integration and easy export into JSON and COCO formats

Integration and Compatibility

Encord offers robust integration capabilities, allowing users to import data from their preferred storage buckets and build pipelines for annotation, validation, model training, and auditing. The platform also supports programmatic automation, ensuring seamless workflows and efficient data operations.

Benefits and Customer Feedback

Its users have received Encord positively, with many highlighting the platform's efficiency in reducing false acceptance rates and its ability to train models on high-quality datasets. The platform's emphasis on AI-assisted labeling and active learning has been particularly appreciated, ensuring accurate and rapid training data creation.

Learn more about how computer vision teams use Encord instead of Scale

• Vida reduce their model false positive from 6% to 1%
• Floy reduce CT & MRI annotation times by ~50%
• Stanford Medicine reduce experiment time by 80%
• King's College London increase labeling efficiency by 6.4x
• Tractable go through hyper-growth supported by faster annotation operations 

Leverage your data to build intelligent applications and unlock the value of machine learning.

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As we delve into the best Scale AI alternatives, we'll explore platforms that offer an enhanced user experience, specializing in data labeling, cater to large-scale operations, and can handle many users. From platforms that leverage neural networks to those that focus on transcription, the future of AI is diverse and promising.

Find a summary of these tools in the table below:

An Overview of Scale AI

Scale AI was a provider of data annotation and ML operations solutions, designed to accelerate the development and deployment of machine learning models. It supported a wide range of industries, including autonomous vehicles, e-commerce, and robotics. It is unclear how the company will change post the acquisition.

ML Pipeline & Features

• Supports annotations for images, videos, LiDAR, text, and more, enabling diverse use cases like object detection, sentiment analysis, and 3D mapping.
• Combines machine learning algorithms with human oversight to reduce annotation time and improve accuracy.
• Customizable workflows for quality assurance, collaboration, and project tracking.
• Handles projects of varying complexities, from small datasets to enterprise-level needs, ensuring scalability.
• Multi-layer quality checks and advanced metrics to ensure dataset reliability and consistency.
• Real-time insights into labeling performance, dataset health, and model readiness.

Integration and Compatibility

Scale AI offers robust integrations with major cloud platforms like AWS, Google Cloud, and Azure. Its APIs allow seamless connection to data pipelines and machine learning frameworks, enabling an efficient end-to-end ML workflow. The platform supports JSON, COCO, and other formats for easy data export.

Benefits and Customer Feedback

Scale AI is praised for its ability to streamline the ML data pipeline, reduce project turnaround times, and maintain high-quality output across large-scale projects. Its customers highlight the platform’s flexibility, scalability, and robust automation features as critical enablers of faster machine learning development. However, its cost and steep learning curve can be challenging for smaller teams. It also may lack flexibility for niche use cases and requires careful integration into existing workflows.

iMerit

iMerit specializes in providing data annotation solutions, including those for LiDAR, which is crucial for applications like autonomous vehicles and robotics. With a focus on complex data types, iMerit ensures high precision and quality in its annotations, making it a preferred choice for industries that require intricate data labeling.

ML Pipeline and Features

• Expertise in LiDAR data annotation, ensuring accurate and high-quality annotations
• While Scale AI is known for its broad range of data labeling services, iMerit's strength lies in its specialization in complex data types, most notably LiDAR
• Robust integration options, allowing seamless connection with various platforms and tools
• Various tools and platforms for efficient data annotation and management
• Emphasis on compliance and data protection, ensuring that businesses can trust them with their sensitive data

Benefits and Customer Feedback

iMerit has garnered positive feedback from its clientele, particularly for its expertise in LiDAR data annotation. Many users have highlighted the platform's precision, efficiency, and quality of annotations. The platform's ability to handle complex data types and provide tailored solutions has been particularly appreciated, making it a go-to solution for industries like autonomous driving and robotics.

Refer to the G2 Link for customer feedback on the iMerit platform.

Dataloop

Dataloop, an AI-driven data management platform, is tailored to streamline the process of generating data for AI. While Scale AI is recognized for its human-centric approach to data labeling, Dataloop differentiates itself with its cloud-based platform, providing flexibility and scalability for organizations of all sizes.

ML Pipeline & Features

• Streamlines administrative tasks efficiently, organizing management and numerical data.
• Dataloop's object tracking and detection feature stands out, providing users with exceptional data quality
• Requires a stable and fast internet connection, which might pose challenges in areas with connectivity issues.

Integration and Compatibility

Dataloop, being a cloud-based platform, offers the advantage of flexibility. However, it also requires a stable and fast internet connection, which might pose challenges in areas with connectivity issues. Despite this, its integration capabilities ensure users can seamlessly connect their data sources and ML models to the platform.

Benefits and Customer Feedback

Dataloop has received positive feedback from its users. Users have noted the platform's scalability and flexibility, making it suitable for both small projects and larger needs. However, some users have pointed out that the user interface can be challenging to navigate, suggesting the need for tutorials or a more intuitive design.

Here is the G2 link for customer reviews on the Dataloop platform.

SuperAnnotate

SuperAnnotate offers tools to streamline annotation. Their platform is equipped with tools and automation features that enable the creation of accurate training data across multiple data types. SuperAnnotate's offerings include the LLM Editor, Image Editor, Video Editor, Text Editor, LiDAR Editor, and Audio Editor.

ML Pipeline &  Features

• Features like data insights, versioning, and a query system to filter and find relevant data
• Marketplace of over 400 annotation teams that speak 18 languages. This ensures high-quality annotations tailored to specific regional and linguistic requirements 
• Dedicated annotation project managers, ensuring stellar project delivery
• Annotation tools for different data types, from images and videos to LiDAR and audio
• Certifications like SOC 2 Type 2, ISO 27001, and HIPAA
• Data integrations with major cloud platforms like AWS, Azure, and GCP

Benefits and Customer Feedback

SuperAnnotate has received positive user feedback, with companies like Hinge Health praising the platform's high and consistent quality. Refer to the G2 link for customers' thoughts about the SuperAnnotate platform.

Labelbox

Labelbox, a leading data labeling platform, is designed to focus on collaboration and automation. It offers a centralized hub where teams can create, manage, and maintain high-quality training data. Labelbox provides tools for image, video, and text annotations.

ML Pipeline & Features

• Labelbox supports data collection to model training
• Features include MAL (Model Assisted Labeling), which uses pre-trained models to accelerate the labeling process
• Easy collaboration, allowing multiple team members to work on the same dataset and ensuring annotation consistencyReviewer Workflow feature enables quality assurance by allowing senior team members to review and approve annotations
• Ontology Manager provides a centralized location to manage labeling instructions, ensuring clarity and consistency
• API integrations, allowing users to connect their data sources and ML models to the platform
• Supports integrations with popular cloud storage solutions

Integration and Compatibility

Labelbox offers API integrations, allowing users to connect their data sources and ML models seamlessly to the platform. This ensures a workflow from data ingestion to model training. The platform also supports integrations with popular cloud storage solutions, ensuring flexibility in data management. Here is the G2 link for customer reviews about the LabelBox platform.

Prolific

Prolific, a human data platform, accelerates AI development by providing direct access to verified participants for training, alignment, and evaluation data. Prolific's API-first architecture integrates human feedback directly into existing ML tools and workflows. This enables AI researchers and developers to source authentic, high-quality human data for their specific use cases in hours, not weeks.

ML Pipeline & Features

• Support for varied AI alignment and evaluation needs including RLHF, model evaluation, factuality testing and real-world user experience testing
• 200K+ active participants across 40+ countries and 80+ languages
• 300+ screeners for precise participant targeting
• Pre-qualified AI taskers and verified domain experts across fields like STEM, languages, programming
• Built-in quality controls with ability to screen participants, monitor performance, and build persistent expert pools that improve with your products over time
• API-first infrastructure along with an intuitive UI
• Option to use either the self-serve platform or fully managed services
• Batch data collections and always-on data pipelines for continuous improvement

Integration and Compatibility

Prolific's API-first architecture integrates with any of your existing ML workflows and tools. The platform supports both batch data collections and always-on data pipelines. Data export is available in standard formats with webhook support for automated data ingestion. As an Encord partner, Prolific provides complementary human intelligence capabilities that enhance annotation workflows.

Benefits and Customer Feedback

Prolific is valued by AI teams at organizations such as Google, AI2 and Hugging Face for making AI development fast and scalable - dramatically reducing time-to-data from weeks to hours while delivering production-grade data quality. Users particularly appreciate the transparency of knowing exactly who is providing feedback, the ability to precisely target participant demographics and expertise, and the flexibility to scale from small pilots to production-level data collection. The platform excels at tasks requiring genuine human judgment - from model evaluation and bias detection to user experience testing for AI products.

Scale Alternatives: Key Takeaways after the acquisition:

Meta’s stake introduces potential conflicts of interest; several hyperscalers have paused new Scale AI projects.

• Encord now leads for scalable multimodal pipelines, adding LiDAR/3D support and active learning to compress annotation cycle times by up to 10 ×.
• Specialized tools still win in niche domains—iMerit for LiDAR labeling services, V7 for medical imaging—while Labelbox appeals to collaboration-heavy enterprises.
• Before migrating, pilot two short projects: one “easy” class and one edge-case-heavy class. Measure unit economics, cycle time, and F1 uplift side-by-side.

Scale’s interactive platform has been recognized for its excellent automation and streamlined workflows tailored for various use cases.

While many platforms in the market are open-source, Scale AI's proposition lies in its focus on machine learning and AI-powered algorithms. The platform offers a range of plugins and tools that provide metrics and insights in real-time. With its robust API integrations, it seamlessly connects with platforms like Amazon, ensuring that artificial intelligence is leveraged to its full potential.

In this rapidly evolving domain, optimizing workflows and harnessing the power of natural language processing is paramount. 

Here are our key takeaways:

• The AI domain is witnessing a transformative phase with new platforms and tools emerging. As industries seek efficient data labeling and management solutions, platforms like Encord are becoming indispensable. Encord's AI-assisted labeling accelerates the creation of high-quality training data, making it a prime choice in this evolving landscape.
• One of the standout features of modern AI platforms is the ability to harness AI for faster and more accurate data annotation. Encord excels in this with its AI-powered labeling, enabling users to annotate visual data swiftly and deploy models up to 10 times faster than traditional methods.