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Alternatives
Encord is the full-platform alternative to Kognic for ADAS and physical AI teams. Go beyond 3D annotation — curate your data at scale, close the retraining loop with active learning, and keep complete visibility into your annotation pipeline.
Trusted by leading AI teams
Kognic is a strong ADAS annotation tool. But as your data volumes grow and your models mature, you need more than an annotation UI — you need curation, active learning, and full pipeline visibility. Encord is the only platform that delivers all of this.
Full-platform: annotation + curation + active learning
Encord Index and Encord Active sit alongside annotation, giving ADAS teams similarity search, edge case surfacing, and model evaluation without switching tools or building custom pipelines.
Complete annotator visibility and QA control
Kognic manages annotation as a black box. Encord gives you per-annotator analytics, configurable QA workflows, confidence-based routing, and HITL — so your team stays in control of quality.
Multimodal and beyond ADAS
Kognic is ADAS-only. As your roadmap expands to VLA models, embodied AI, or non-sensor modalities, Encord already supports images, video, audio, text, and 3D in one platform.
KEY FEATURES
Annotation
3D LiDAR + sensor fusion annotation (camera, LiDAR, radar)
3D cuboid creation from 2D camera view
Sequences up to 1000+ frames (Kognic capped at ~600)
Multimodal annotation (images, video, audio, text, 3D)
Auto-labeling on cuboids
Workflow & Quality
Per-annotator analytics and QA visibility
Configurable workflows (HITL, confidence routing, QA review)
Self-serve + managed annotation on same project
Full Platform
Data curation and similarity search (Encord Index)
Active learning and model evaluation (Encord Active)
Open API/SDK for custom model integration
The best ADAS and physical AI programs need to move fast from raw sensor data to deployed models. Encord closes the entire loop.
Curate the right data before you label
Use embedding-based similarity search and edge case surfacing to identify the frames worth annotating — rather than labeling everything blindly. Teams with hundreds of billions of sensor frames can use this to prioritize annotation budgets.
API-first and cloud-native — your data stays in your cloud
Encord integrates directly with your cloud storage (S3, GCS, Azure) and ML stack. Your data never leaves your infrastructure. Kognic requires data ingestion into their managed environment.
Sensor calibration and temporal synchronization
Encord supports sensor calibration workflows and temporal synchronization across multi-camera rigs — a key capability for automotive teams.
Case studies
We now have an integrated, one-stop solution where we can manage our data and also understand our model performance to create feedback mechanisms to improve data and models.
Encord made it very easy to centrally keep track of annotations, including who had made them and who had reviewed them. It also has this great interpolation tool which was especially useful.
It’s always about balancing speed and quality. A lot of platforms prioritize speed over quality or quality over speed. Encord speeds up annotation while still allowing for strong quality control.
Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted.
We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have.
Getting started with Encord and integrating it into our workflow was really fast. The thing that I find the most valuable is the flexibility of how we can integrate the Encord pipeline into our own pipeline.
Encord Index is a high-performance system for our AI data, enabling us to sort and search at any level of complexity.
Before using Encord, it was challenging to see all the data, projects, and annotations in one place. Now, with Encord I feel like we have a much clearer understanding of everything that's happening.
We now have an integrated, one-stop solution where we can manage our data and also understand our model performance to create feedback mechanisms to improve data and models.
Encord made it very easy to centrally keep track of annotations, including who had made them and who had reviewed them. It also has this great interpolation tool which was especially useful.
It’s always about balancing speed and quality. A lot of platforms prioritize speed over quality or quality over speed. Encord speeds up annotation while still allowing for strong quality control.
Encord’s robust support system has been remarkable. Whenever questions or issues come up, they are always supportive and helpful. This ensures that our workflows remain uninterrupted.
We went through a bunch of vendors and one of the things that stood out about Encord was the video first support, which other vendors do not have.
Getting started with Encord and integrating it into our workflow was really fast. The thing that I find the most valuable is the flexibility of how we can integrate the Encord pipeline into our own pipeline.
Encord Index is a high-performance system for our AI data, enabling us to sort and search at any level of complexity.
Before using Encord, it was challenging to see all the data, projects, and annotations in one place. Now, with Encord I feel like we have a much clearer understanding of everything that's happening.
Both platforms support 3D LiDAR and sensor fusion annotation. Encord adds a full data curation layer (Encord Index), active learning (Encord Active), and per-annotator analytics that Kognic does not offer. Encord also supports sequences of 1000+ frames vs Kognic's ~600 frame cap, and allows creating 3D cuboid instances directly from the 2D camera view.
Unlike Kognic, which is exclusively ADAS-focused, Encord supports images, video, audio, text, and 3D point clouds in a single platform. This matters for teams building VLA models or expanding beyond sensor data.
Kognic charges per hours of tool usage with a minimum, which can be cheaper at low annotation volumes. Encord's per-seat model becomes comparable or more cost-effective at scale, and the total platform value (curation, active learning, analytics) replaces separate tools.
Yes. Encord offers in-house managed annotation delivery with ADAS expertise, alongside a fully self-serve option. Unlike Kognic's services-first model, Encord gives you complete visibility into who is annotating your data and per-labeler quality metrics.
No. Encord connects directly to your cloud storage (AWS S3, GCS, Azure Blob). Your data stays in your infrastructure — there is no requirement to upload or migrate data to Encord's environment.
Join the ADAS and physical AI teams already using Encord to build better models faster.
