Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Sieve records 124 monthly visits with +1.6% growth and Contact for Pricing pricing.
The environments frontier AI trains on, and the agents that do the recurring work
Data labeling and expert annotation
Structured annotation, curation, model evaluation and human-in-the-loop review across vision, language and generative tasks, used to build the training and eval data behind production models.
Autonomous agent sessions
Recursion agents start on their own from builds, alerts, commits, checkpoints and schedules, then work in the background until the task is finished and reviewable.
Domain specialists by function
Specialist agents cover software engineering, reliability, security, ML research, evals, R&D, product and finance workflows, from flaky test fixes to postmortem drafts and vulnerability patches.
Graded outcomes that compound
Every agent run is scored by a grader, and graded sessions leave memories, notes and skills that the next session reads before acting, so quality compounds over time.
Strengths & Pros
+Covers both sides of the problem: training and eval data production plus agents that act on recurring work
+Backed by a recent $100M+ Series E and adoption by frontier AI labs, indicating real funding and demand
+Agent runs produce concrete deliverables with proof — tests, reruns, ablations — rather than unverified suggestions
Frequently Asked Questions: Labelbox vs Sieve
Labelbox has 762,803 monthly visits, while Sieve has 124. Traffic estimates describe audience size, not product quality.
SpecialistRelies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox.
AI Models4.4Editorial score: 4.4Contact for Pricing36% direct
Free / Open-sourceProvides a flexible open-source alternative for custom data labeling, but requires self-hosting and manual infrastructure maintenance.
AI Developer Tools4.7Editorial score: 4.7Contact for Pricing, Free34% direct
SpecialistRelies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox.
Free / Open-sourceProvides a flexible open-source alternative for custom data labeling, but requires self-hosting and manual infrastructure maintenance.
