Scale AI alternatives

3 tools to consider instead of Scale AI, shown against it.

Scale AI Labelbox Surge AI Snorkel AI
Vendor Scale AI, Inc. Labelbox, Inc. Surge AI, Inc. Snorkel AI, Inc.
Pricing model Usage-based Quote only Quote only Quote only
Free tier Yes Yes — —
Deployment Cloud Cloud Cloud Cloud, Self-hosted
Open source No No No No
Best for Foundation-model builders and enterprises needing outsourced labeling capacity, not just an annotation UI. Enterprise ML teams needing multimodal annotation plus a path into RLHF/agent-training data collection. Teams needing a high-quality managed workforce for RLHF, preference data or specialized/expert labeling tasks. Teams that want to generate training labels programmatically at scale rather than annotate manually example by example.
Pricing

Self-serve Data Engine is pay-as-you-go with a small free allowance; larger programs are custom enterprise contracts.

Self-Serve Data Engine pay as you go
Enterprise custom

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

A free starter tier is offered; paid plans and managed workforce/RLHF services are quoted per team, contact sales.

Pricing has not been verified yet — see the vendor's site.

No public pricing found; projects are scoped and quoted individually, contact sales.

Pricing has not been verified yet — see the vendor's site.

No public pricing found; quoted per deployment, contact sales.

Pricing has not been verified yet — see the vendor's site.

Features
  • Managed human labeling workforce alongside self-serve tooling
  • Image, video, text, LiDAR and multi-sensor annotation
  • RLHF and human-feedback data collection for LLMs
  • Model evaluation and red-teaming services
  • Data curation and quality-control workflows
  • API and SDK for programmatic task submission
  • Enterprise SLAs and dedicated support on larger contracts
  • Multimodal annotation editors (vision, NLP, audio, geospatial, LLM chat)
  • Catalog for data curation and example prioritization
  • Model-assisted pre-labeling to speed up annotation
  • RL environments and preference-signal collection for LLM post-training
  • Managed expert workforce access for specialized tasks
  • Role-based review and QA workflows
  • API/SDK for pipeline integration
  • Vetted, domain-matched human labeling workforce
  • RLHF and preference-ranking data collection for LLMs
  • Off-the-shelf datasets and RL environments for direct purchase
  • Multilingual and expert/technical annotator pools
  • Red-teaming and safety-evaluation task support
  • Custom project scoping and quality-control processes
  • Benchmark datasets for model evaluation
  • Programmatic labeling via user-defined labeling functions
  • Weak-supervision algorithms combining multiple noisy label sources
  • Active learning to prioritize human review effort
  • VPC/on-prem deployment for enterprise and government customers
  • Data-quality and drift monitoring on labeled datasets
  • Expert data-development services for frontier LLM/agent evaluation
  • Integration with existing model outputs as label sources

In the index now