Key Takeaways
- Scale AI has shifted focus toward LLM RLHF and defense contracts, leaving autonomous driving teams looking for annotation partners with deeper AV capabilities.
- The five alternatives covered here — Kognic, Labelbox, V7, SuperAnnotate, and Dataloop — each have different strengths. The right choice depends on your specific use case, sensor stack, and model architecture.
- For teams building autonomous driving perception, planning, or end-to-end models, the critical differentiators are 3D/LiDAR depth, native sensor fusion, AV-specific quality assurance, and whether the platform's roadmap is aligned with next-generation VLM/VLA model training.
- This comparison uses only publicly available information as of March 2026.
Disclosure: Kognic is the publisher of this article. We have made every effort to present each platform fairly, using publicly available information. We encourage you to evaluate all platforms firsthand against your specific requirements.
Scale AI built its reputation as the dominant name in data labeling, which is why teams searching for Scale AI alternatives often start by understanding what changed. For years, it was the default choice for AI teams that needed large volumes of labeled data across text, image, and video.
But the Scale AI of 2026 looks different from the Scale AI that many autonomous driving teams originally evaluated.
The leadership changed. Founder and CEO Alexandr Wang departed in 2025 to join Meta as VP of AI. Meta now holds a 49% stake in the company. Several major technology customers, including Google, OpenAI, and Microsoft, ended their relationships with Scale AI, citing concerns about data conflicts with a Meta-controlled entity.
The focus shifted. Scale AI's primary business is now RLHF annotation for large language models and defense contracts, including a $100 million Department of Defense deal and a $99 million U.S. Army contract. The company's foundation model, AFM-1, is focused on 2D image understanding, not 3D point cloud annotation or multi-sensor autonomous driving data.
The workforce contracted. Scale AI announced a 14% layoff in 2025 as it restructured around its new strategic priorities.
None of this means Scale AI is a bad company. But for teams whose primary use case is autonomous driving annotation (3D LiDAR, sensor fusion, sequential frame labeling, safety-critical quality standards), Scale AI's current trajectory raises a legitimate question: Is this platform still the best fit for what we need?
If you are asking that question, here are five alternatives worth evaluating.
| Capability | Kognic | Labelbox | V7 | SuperAnnotate | Dataloop |
|---|---|---|---|---|---|
| Primary focus | Autonomous driving | Multi-industry AI | Multi-industry AI | Multi-industry AI | Multi-industry AI |
| 3D / LiDAR | Deep, production-grade (7+ years) | Available, not prominent | Available with AI Sense pre-labeling | Supported but shallow | Dedicated LiDAR studio |
| Sensor fusion | Native multi-sensor | Not documented | Not documented | Basic | Dedicated capability |
| VLA / Language Grounding | Chain of Causation, Write/Edit/Rank | LLM RLHF (not driving-specific) | Not available | LLM RLHF (not driving-specific) | Not available |
| Named AV customers | 10+ OEMs and Tier 1s | None public | Continental | None public | Foresight Automotive |
| Annotation services | AV domain experts | Alignerr (LLM/STEM) | Not a core offering | Marketplace model | Managed workforce |
| Quality assurance | 90+ AV-specific checker apps | Quality workflows | Collaborative review | Consensus scoring | QA workflows |
| Pricing | Enterprise | Freemium + enterprise | Freemium ($29/mo+) | Free tier + enterprise | Custom (free trial) |
| AV certifications | TISAX Level 3 (ISO 27001 in progress) | SOC 2, HIPAA | Not documented | Not documented | Not documented |
Best for: Teams whose primary use case is autonomous driving or ADAS annotation at production scale.
Founded: 2018, Gothenburg, Sweden
Focus: Autonomous driving annotation exclusively
Named AV customers: Qualcomm, BMW, Zenseact, Continental, Bosch, Kodiak, ZF, Einride, Gatik, Jaguar Land Rover
Kognic is the only platform on this list built exclusively for autonomous driving. Every product decision, from the point cloud rendering engine to the quality assurance framework, has been shaped by the requirements of OEMs and Tier 1 suppliers building production perception stacks. For a direct head-to-head, see our full Kognic vs. Scale AI comparison.
Production-grade 3D and LiDAR. Seven-plus years of 3D point cloud annotation tooling means efficient cuboid placement, multi-frame sequential labeling, support for dense and sparse point clouds, and multi-LiDAR workflows for vehicles running multiple scanners. This is not a checkbox feature added to a generalist platform.
Native sensor fusion. Annotators work across synchronized camera, LiDAR, and radar views in a unified workflow. A pedestrian labeled in the LiDAR point cloud corresponds exactly to the same object in every camera view, with calibrated coordinate systems maintaining consistency across all sensor modalities.
Language Grounding for VLM/VLA models. As the industry shifts from traditional perception to vision-language models, Kognic has built annotation capabilities specifically for this new class of models. Write, Edit, and Rank annotation modes, combined with the Chain of Causation methodology that prevents hindsight bias, produce the structured reasoning data that VLMs and VLAs need for training. Read more about how foundation models are changing autonomous vehicle annotation.
AV-specific quality assurance. Over 90 quality checker applications designed for autonomous driving catch the kinds of errors that matter in safety-critical data: incorrect cuboid orientations, inconsistent track IDs, labels that violate physical constraints, and cross-sensor inconsistencies.
Kognic is not a generalist platform. If your organization needs annotation across many different AI use cases (text, medical imaging, document processing), you will need a second tool for those workloads. Kognic is enterprise-only with no self-serve tier.
Best for: Organizations with diverse AI labeling needs across multiple business units, especially those doing LLM fine-tuning alongside computer vision.
Founded: 2018, San Francisco
Focus: Multi-industry annotation and LLM alignment
Funding: ~$189M at ~$1B valuation
CEO: Manu Sharma (co-founder, still at helm)
Labelbox is the strongest multi-industry annotation platform on this list. Its data type support is the broadest on this list: image, video, text, audio, PDF, geospatial, medical imaging, and multimodal chat, all in one platform.
For LLM alignment specifically, Labelbox has the strongest tooling of any platform on this list. The model comparison interface evaluates up to ten models side-by-side. RLHF workflows, LLM-as-judge, and preference data pipelines are mature and production-ready. The Alignerr services arm provides PhD-level expert feedback for language model fine-tuning.
The freemium tier lets teams start labeling without a sales conversation, and stable leadership under co-founder Manu Sharma provides continuity.
For a deeper breakdown, see our full Kognic vs. Labelbox comparison.
Labelbox does not publicly name any automotive customers. 3D/LiDAR annotation is available but not prominently featured. Sensor fusion is not documented as a core capability. The platform's investment and roadmap are oriented toward LLM alignment and generative AI, not autonomous driving.
Best for: Teams working primarily with camera-based annotation who want strong AI-assisted labeling to accelerate throughput.
Founded: 2018, London
Focus: Multi-industry annotation with strong auto-annotation
Funding: ~$36M
Named AV customer: Continental
V7 has invested heavily in AI-assisted annotation, and it shows. The SAM integration provides one-click segmentation that genuinely accelerates 2D labeling. SAM 3 adds text-based auto-detection: describe what you want to label and the model finds it. Auto-Track handles object tracking across video frames.
V7 has a legitimate automotive customer in Continental, which scaled from 35,000 images per week to approximately 200,000 using the platform. The $29/month starter plan makes V7 accessible for smaller teams or proof-of-concept projects.
For more detail, read the full Kognic vs. V7 comparison.
V7's 3D/LiDAR capabilities are newer and less mature than purpose-built AV tooling. AI Sense pre-labeling covers a limited set of 3D object classes (cars, pedestrians, bicycles). Sensor fusion is not documented as a native workflow, and there are no Language Grounding or reasoning annotation capabilities for VLM/VLA models. To understand why sensor fusion matters, see our guide on accelerating sensor fusion annotations.
V7 is also diversifying its business. The V7 Go product line focuses on document AI workflows for finance, legal, and insurance, signaling that annotation may not be the company's sole strategic focus going forward. Engineering investment split across two distinct product lines may slow AV-specific feature development.
Best for: Teams that want a well-supported general annotation platform with strong GenAI/LLM tooling and workforce marketplace.
Founded: 2019, San Francisco (R&D in Armenia)
Focus: Multi-industry annotation with GenAI emphasis
Funding: ~$75M (investors include NVIDIA, Databricks, Dell Technologies)
CEO: Vahan Petrosyan (co-founder)
SuperAnnotate is growing fast: the company reported 5x software revenue growth in 2024 and tripled its customer base. The investor list is notable: NVIDIA, Databricks Ventures, and Dell Technologies Capital all back the platform.
The annotation services marketplace provides access to pre-qualified labeling teams, which helps organizations scale without building internal workforce. GenAI and LLM fine-tuning tools are well-developed, including RLHF workflows, model comparison, and supervised fine-tuning templates.
SuperAnnotate has a dedicated autonomous driving page on its website and supports LiDAR/3D point cloud data types.
Despite having an AV landing page, SuperAnnotate does not publicly name any automotive customers. The 3D/LiDAR tooling lacks the depth of purpose-built AV platforms: limited export formats (SuperAnnotate and COCO only), no annotation versioning on exports, and sensor fusion capabilities that reviewers describe as basic compared to specialized tools.
The NVIDIA partnership centers on NeMo (language models), not NVIDIA DRIVE (autonomous vehicles). The company's content marketing overwhelmingly focuses on RLHF and LLM fine-tuning, not autonomous driving perception.
Users report a roughly three-week learning curve for advanced features and some performance issues with very large, high-resolution datasets.
Best for: Teams that need sensor fusion capabilities in a generalist platform, especially those in the Israeli AV ecosystem.
Founded: 2017, Herzliya, Israel
Focus: Multi-industry data pipeline and annotation
Funding: ~$49M (last round: Nov 2022)
CEO: Eran Shlomo (co-founder)
Named AV customer: Foresight Automotive
Among the generalist platforms on this list, Dataloop has the strongest autonomous driving story. The platform offers a dedicated sensor fusion capability that combines multi-angle video, synchronized cameras, LiDAR, and IR sensors in a unified annotation view. There is a dedicated LiDAR annotation studio with 3D cuboids, polylines, and semantic segmentation tools.
Dataloop has published a case study with Foresight Automotive, an ADAS developer that used the platform to scale from a 15-person internal annotation team to an automated pipeline with human-in-the-loop validation. The platform also offers a drag-and-drop pipeline builder with a pre-built autonomous driving perception pipeline template.
Dataloop is a small company: approximately 79 employees with $3.5 million in revenue in 2024. The last funding round was November 2022, more than three years ago. For enterprise automotive procurement teams, company durability is a legitimate concern.
The platform requires WebM-VP8 format for frame-accurate video annotation, which creates friction for automotive teams that typically work with H.264 or H.265 codecs. Multiple reviewers report UI complexity and performance issues with large datasets.
Foresight Automotive is a small ADAS company, not a Tier 1 OEM, so the AV reference customer, while genuine, does not carry the same weight as references from major automotive programs. Like others on this list, Dataloop is actively repositioning toward GenAI pipelines and RAG workflows.
| Use Case | Recommended Platform | Runner-Up |
|---|---|---|
| Full AV perception (3D + fusion) | Kognic | Dataloop |
| VLM / VLA model training | Kognic | No close alternative |
| LLM fine-tuning / RLHF | Labelbox | SuperAnnotate |
| Camera-only ADAS | V7 | Kognic |
| Multi-industry annotation (single vendor) | Labelbox | SuperAnnotate |
| Managed annotation services | SuperAnnotate | Dataloop |
| Budget-conscious / early-stage team | V7 ($29/mo) | Labelbox (free tier) |
| Sensor fusion annotation | Kognic | Dataloop |
| Pipeline automation / workflow orchestration | Dataloop | Labelbox |
If you are evaluating Scale AI alternatives specifically for autonomous driving, here are the questions that matter most:
1. How mature is the 3D/LiDAR tooling? Ask for a demo with your actual point cloud data, not a curated sample. Test with dense and sparse scans, multiple LiDAR sensors, and long sequences. The difference between early-stage and production-hardened 3D tooling becomes obvious quickly.
2. Is sensor fusion native or bolted on? True sensor fusion means annotating across calibrated, synchronized sensor views in a single workflow, not labeling camera images and point clouds separately and hoping the labels align.
3. What does the AV customer base look like? Named reference customers from major OEMs and Tier 1 suppliers confirm that the platform has handled real automotive annotation requirements at scale. Ask for references you can speak with.
4. What quality assurance is AV-specific? Generic quality tools catch generic errors. Autonomous driving annotation requires domain-specific checks: cuboid orientation validation, temporal tracking consistency, cross-sensor label alignment, and physical constraint verification.
5. Where is the product roadmap heading? If the platform is investing primarily in LLM alignment, GenAI pipelines, or document processing, the autonomous driving features you need in eighteen months may not be a development priority.
6. What certifications does the platform hold? Automotive programs typically require TISAX and ISO 27001 certifications. If the platform does not hold these, factor in the timeline and cost of security review as part of your evaluation.
What is the best Scale AI alternative for autonomous driving?
Kognic is the strongest alternative for teams focused on autonomous driving annotation. It is the only platform on this list built exclusively for AV, with production-grade 3D/LiDAR tooling, native multi-sensor fusion, and Language Grounding capabilities for VLM/VLA model training. For teams with broader AI labeling needs beyond driving, Labelbox offers the widest data type support.
Does Scale AI still support autonomous driving annotation?
Scale AI still offers data labeling services that can be applied to driving data. However, its strategic focus has shifted toward LLM RLHF and defense contracts. Its foundation model AFM-1 is 2D-only, and its AV customer base has contracted following the suspension of Cruise and the departure of several major technology customers. Teams with active AV annotation programs should evaluate whether Scale AI's current roadmap aligns with their needs.
Which annotation platform has the best sensor fusion capabilities?
For autonomous driving, Kognic offers the deepest sensor fusion: annotators work across calibrated, synchronized camera, LiDAR, and radar views in a unified workflow. Among the generalist platforms, Dataloop has the strongest sensor fusion story, combining multi-angle video, cameras, LiDAR, and IR sensors. Most other platforms handle sensor types separately rather than in a true fused view.
What happened to Scale AI's leadership?
Founder and CEO Alexandr Wang left Scale AI in 2025 to join Meta as VP of AI. Meta now holds a 49% stake in Scale AI. This ownership change prompted several large customers, including Google, OpenAI, and Microsoft, to exit their relationships with Scale AI over data independence concerns.
Is Kognic only for autonomous driving?
Yes. At the moment, Kognic focuses exclusively on autonomous driving and ADAS annotation. This means it is not the right choice if you need a single platform for text, medical imaging, or document labeling. But for teams where driving data is the primary workload, this specialization translates into deeper tooling, domain-specific quality assurance, and a product roadmap fully aligned with AV industry needs.
Scale AI remains a significant player in the broader data labeling market, but its strategic pivot toward LLM RLHF and defense contracts has created a gap for autonomous driving teams that need deep, domain-specific annotation capabilities.
The five alternatives compared here serve different needs:
No single platform is the right choice for every team. The best decision is to evaluate against your specific sensor stack, model architecture, quality requirements, and scale, and choose the platform whose depth matches where your program is heading.
This comparison was published by Kognic and uses publicly available information as of March 2026. We encourage readers to contact all platforms directly for current product details and to request demos with their own data.