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Short answer: Choose Labelbox for a unified labeling-to-evaluation lifecycle, Scale AI for a managed expert operation at enterprise or frontier-model scale, and SuperAnnotate for agent trajectories, tool-use traces, rubric scoring, lineage, quality gates, and orchestration. All three support reinforcement-learning-from-human-feedback (RLHF), but their centers of gravity differ. “AI-agent training” is not just image or text annotation: it usually requires multi-step trajectories, tool calls, preferences, outcome labels, safety tests, and repeatable evaluation.
Public official pages do not present comparable prices or contract minimums, so the practical decision is based on workflow fit, workforce model, controls, integrations, and the volume and risk of your data.
At a glance: the best fit for each buying situation
| Decision axis | Labelbox | Scale AI | SuperAnnotate |
|---|---|---|---|
| Primary strength | One lifecycle hub for annotation, expert services, RLHF, evaluation, and cataloging | Managed expert data generation, RLHF, red-teaming, and enterprise evaluation | Agent trajectories, tool use, rubric scoring, lineage, quality gates, and orchestration |
| Managed expert workforce | On-demand expert labeling; teams can combine internal annotators, vendors, and managed workers | Vetted subject-matter experts and human evaluation are central to the offering | Human and expert collaboration is available within orchestrated workflows |
| RLHF and preference data | Explicitly supported through RLHF and preference-ranking workflows | Explicitly supported through RLHF and alignment data generation | Supported through RLHF and preference-data workflows |
| Agent trajectories and tool use | Coding and AI-agent tasks are documented | Agent and end-to-end evaluation are supported through its generative-AI evaluation workflows | Multi-step trajectories, tool use, long-horizon tasks, rubric evaluation, model judges, and pass-rate scoring are explicit features |
| Modalities | Text, image, video, audio, multimodal, coding, and text-to-image workflows | Text, image, video, 3D sensor, and generative-AI data | Text, image, video, audio, LiDAR, and custom modalities |
| Evaluation and safety | Multimodal LLM evaluation, custom-model testing, and red teaming | Automated tests, expert human evaluation, custom metrics and datasets, human-in-the-loop verification, iteration, monitoring, and red-teaming | Rubrics, model judges, QA controls, dashboards, and pass-rate workflows |
| Data management | Cataloging, search, curation, team monitoring, and lineage-oriented lifecycle management | Data Engine workflow for collecting, curating, annotating, training, and evaluating | Dataset versioning, lineage, quality gates, dashboards, and automated routing |
| Best initial fit | Organizations consolidating internal and managed labeling in one environment | Enterprise or frontier-model teams buying a managed operation and broad evaluation | Teams whose core asset is structured agentic data and repeatable orchestration |
The matrix is a starting point, not a substitute for a scoped pilot. A team training a customer-support agent may need fewer modalities but much tighter trajectory and outcome controls than a computer-vision program. Conversely, a robotics program may prioritize 3D or LiDAR support and a managed expert workforce.
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What “AI-agent training” actually requires
Traditional supervised labeling attaches a target to an isolated item: a class to an image, a transcript to an audio clip, or a span label to text. An agent produces a sequence of actions in an environment. A useful training record can therefore include the initial instruction, observations, intermediate reasoning or state summaries, tool calls and arguments, tool responses, retries, elapsed steps, final answer, and whether the task achieved its goal.
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Trajectory and tool-use data
Trajectory data preserves the order and context of decisions. Tool-use labels can identify whether an API was appropriate, whether arguments were valid, whether permissions were respected, and whether the returned result was used correctly. Long-horizon tasks add failure points: an agent can make an apparently reasonable early choice that prevents completion many steps later.
Preference and outcome data
RLHF and preference ranking let reviewers compare two or more responses or trajectories. Outcome labels answer a different question: did the agent complete the task, satisfy a policy, or produce an acceptable side effect? Strong programs collect both. A preferred answer can still fail the business objective, while a successful outcome can hide unsafe or unnecessarily costly behavior.
Evaluation and safety data
Agent evaluation commonly combines deterministic checks, model-based judges, and human review. Red-team examples probe prompt injection, data leakage, unsafe tool calls, policy evasion, and brittle behavior. The platform must preserve the evaluation configuration and dataset version so a score can be reproduced after a model or rubric changes.
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Labelbox: the unified lifecycle choice
Labelbox documentation describes a unified platform spanning data annotation, model-assisted labeling, RLHF workflows, dataset cataloging, team monitoring, custom-model testing, and post-training tasks. Its documented scope includes supervised fine-tuning, multimodal LLM evaluation, preference ranking, red teaming, text-to-image, video, audio, coding, and AI-agent tasks. Expert labeling is documented in more than 30 languages.
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Where Labelbox fits best
Labelbox is a strong first choice when the same team wants to move from raw data to labeling, post-training, evaluation, and cataloging without stitching together separate systems. It also suits organizations that need to mix internal annotators, external vendors, and an on-demand managed workforce. A single catalog can help teams find and curate data across projects instead of treating every annotation job as an isolated export.
What to validate in an agent pilot
- How a trajectory is represented: individual turns, tool calls, observations, and final outcomes.
- Whether your coding or agent ontology can express retries, invalid calls, escalation, and policy violations.
- How preference pairs and rubric scores are exported for your training stack.
- How custom-model tests and red-team cases are versioned alongside the data they evaluate.
- Whether the managed workforce has the language and domain expertise your agent requires.
Labelbox’s breadth can reduce tool sprawl, but a broad lifecycle does not automatically guarantee the exact trajectory schema or orchestration behavior you need. Confirm those details with a representative task before committing.
Scale AI: the managed expert and evaluation operation
Scale AI positions its Data Engine around a loop of collecting, curating, annotating, training, and evaluating data. Its generative-AI data offering combines automation with vetted subject-matter experts for data generation, RLHF, model evaluation, safety, and alignment. The enterprise evaluation offering combines automated tests with expert human evaluation, custom metrics and datasets, human-in-the-loop verification, iteration, and production monitoring.
Where Scale AI fits best
Scale is the clearest fit when the buyer wants an operational partner to source and manage expert contributors, generate large volumes of alignment data, conduct red-team exercises, and monitor models in production. This is particularly relevant to frontier-model or enterprise programs that cannot recruit and supervise every specialist themselves.
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Company-reported scale figures
Scale AI reported, in 2026, that it had supported 15 billion human decisions used to train AI models, paid $1 billion to contributors globally, reached a $29 billion valuation, employed more than 1,000 people, and was founded in 2016. These are company-reported figures, not an independent capacity benchmark; treat them as dated corporate disclosures when comparing suppliers.
Questions to ask about managed delivery
- Which expert qualifications, screening steps, and calibration tests apply to your project?
- How are disagreements adjudicated, and can you inspect agreement and error metrics by task type?
- What turnaround and replacement commitments apply when a specialist pool is unavailable?
- How are sensitive prompts, tool credentials, and production traces isolated from the workforce?
- Can automated tests, human judgments, custom metrics, and monitoring results be exported in a reproducible format?
Scale’s operational depth can be valuable when volume and risk dominate the decision. It may be more infrastructure than a small team needs for a narrowly scoped prototype, so define the minimum viable workload and service level in the request for proposal.
SuperAnnotate: the trajectory, QA, and orchestration specialist
SuperAnnotate describes a data engine for multimodal AI, agentic AI, data management, and data orchestration. Its agentic workflows explicitly cover multi-step trajectories, tool use, long-horizon tasks, rubric evaluation, model judges, pass-rate scoring, dataset versioning, lineage, quality gates, dashboards, and automated routing between models and human experts.
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SuperAnnotate is the natural short-list leader when the primary artifact is a structured agent trajectory and the team needs repeatable controls around it. A workflow can route straightforward examples to models, send uncertain cases to people, apply rubric checks, and retain the lineage needed to understand how a training-ready record was produced.
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Controls that matter for agent teams
- Rubrics: Convert vague quality goals into explicit criteria for tool selection, argument correctness, policy compliance, and task completion.
- Model judges: Use automated review for scale, while reserving human adjudication for uncertain or high-impact cases.
- Pass-rate scoring: Track how often an agent meets a defined bar instead of relying only on average label agreement.
- Versioning and lineage: Connect each exported example to its source, transformations, rubric version, and review history.
- Quality gates and routing: Prevent incomplete or low-confidence records from entering a training set and direct exceptions to the appropriate reviewer.
The trade-off is that an orchestration-heavy platform requires your team to define schemas, rubrics, routing rules, and acceptance gates carefully. Without those decisions, advanced controls become configuration overhead rather than a quality advantage.
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RLHF and preference ranking
All three platforms explicitly support RLHF or preference-data workflows. Labelbox presents RLHF and preference ranking as part of a broader post-training lifecycle. Scale AI emphasizes expert-generated alignment data and human evaluation at managed scale. SuperAnnotate places RLHF alongside its trajectory, rubric, and orchestration controls. The deciding question is not whether a vendor uses the RLHF label; it is who supplies reviewers, how preferences are sampled and adjudicated, and how the resulting pairs or rankings map into your training pipeline.
Agent trajectories and tool use
Labelbox documents coding and AI-agent tasks, making it a viable lifecycle hub for teams that already use its annotation and catalog tools. Scale supports agent and end-to-end evaluation within its generative-AI evaluation operation. SuperAnnotate is the most explicit about multi-step trajectories, tool calls, long-horizon tasks, and trajectory-level scoring. Ask each supplier to ingest one real trace containing a successful run, a retry, an invalid tool argument, and a policy violation; then inspect the exported representation.
Multimodal and specialized data
Labelbox covers text, image, video, audio, multimodal editors, coding, and text-to-image workflows. Scale lists text, image, video, 3D sensor, and generative-AI data. SuperAnnotate lists text, image, video, audio, LiDAR, and custom modalities. For an agent that acts on screens, documents, voice, or physical sensors, verify not just whether a modality can be uploaded, but whether it can be synchronized with actions and outcomes in one example.
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Evaluation, red-teaming, and monitoring
Labelbox documents multimodal LLM evaluation, custom-model testing, and red teaming. Scale has the broadest stated evaluation stack: automated tests, expert human evaluation, custom metrics and datasets, human-in-the-loop verification, iteration, red-teaming, and production monitoring. SuperAnnotate emphasizes rubric and model-judge evaluation, QA, dashboards, and pass-rate workflows. Match the evaluation method to the risk: deterministic checks for hard constraints, human review for nuanced judgments, and model judges only with calibration and escalation paths.
Data management and reproducibility
Labelbox’s catalog, search, curation, and team-monitoring functions support a lifecycle view. Scale’s Data Engine organizes collection through evaluation as a managed operation. SuperAnnotate highlights dataset versioning, lineage, quality gates, dashboards, and routing. For any of them, require immutable dataset identifiers, export manifests, schema versions, and a way to reproduce a score after changing a model, rubric, or reviewer pool.
Pricing, contracts, and requirements
The official material reviewed does not publish comparable public prices or contract minimums for these offerings. Costs will depend on modality, volume, reviewer expertise, turnaround, evaluation complexity, security requirements, and whether you buy software, managed labor, or both. Do not compare a self-service annotation license with a managed expert program as though they were equivalent line items.
What to put in a quote request
- Describe the workload: Give expected monthly examples, average trajectory length, number of tool calls, modalities, languages, and peak volume.
- Define quality: Specify rubric dimensions, acceptable disagreement, adjudication rules, pass-rate targets, and the tests that must block an export.
- Separate labor from platform: Request distinct prices for software access, managed annotation, expert review, red teaming, evaluation, storage, and overages.
- Specify service levels: Ask for committed turnaround, staffing, escalation, rework, and replacement terms.
- Require an export plan: Name the formats, metadata, ontology, rubric, lineage, and evaluation artifacts your training and observability systems need.
Technical and governance requirements
- Security controls and data-residency options for prompts, traces, and tool outputs.
- API, storage, identity, and model-platform integrations, including authentication and rate limits.
- Ontology and rubric flexibility for new tools, policies, languages, and modalities.
- Workforce sourcing, confidentiality, access segregation, and reviewer auditability.
- Quality measurement by task type, reviewer, model, and dataset version.
- Evaluation reproducibility, retention, deletion, and incident-response procedures.
A practical selection process
- Map the agent’s data objects. List instructions, observations, tool calls, arguments, responses, retries, final outputs, outcomes, preferences, and safety labels. Mark which fields are sensitive.
- Rank your constraint. Choose the dominant requirement: lifecycle consolidation, managed expert capacity, or trajectory and orchestration control. This prevents a long feature checklist from hiding the real decision.
- Run the same pilot. Give each platform an identical, mixed batch containing routine, ambiguous, long-horizon, multimodal, and adversarial examples.
- Measure operational quality. Record setup time, reviewer throughput, agreement, adjudication rate, trajectory completeness, export effort, and time to correct a bad label.
- Test failure recovery. Deliberately change a rubric, reject a batch, revoke a reviewer, and rerun an evaluation. A platform’s recovery path often matters more than its happy-path demo.
- Price the complete workflow. Include software, labor, expert premiums, storage, evaluation runs, rework, integration work, and expected growth rather than comparing headline seats or per-item rates.
- Choose with a written acceptance plan. Document schema, quality gates, service levels, security terms, export format, and the conditions for expanding beyond the pilot.
Which platform should you choose?
Choose Labelbox when
- You want annotation, expert labeling, RLHF, evaluation, cataloging, and model testing in one lifecycle environment.
- Your workforce will combine internal staff, vendors, and on-demand experts.
- You need broad text, image, video, audio, coding, and multimodal coverage rather than a trajectory-only system.
Choose Scale AI when
- You need a managed expert operation for large-scale data generation, alignment, red-teaming, or evaluation.
- Human-in-the-loop verification, custom metrics, production monitoring, and enterprise service delivery are central requirements.
- Your organization prefers buying an operational capability instead of building and supervising the entire contributor network.
Choose SuperAnnotate when
- Agent trajectories, tool use, long-horizon tasks, rubric scoring, and pass-rate metrics are the heart of the program.
- You need explicit dataset versioning, lineage, quality gates, dashboards, and automated routing.
- Your team is prepared to define detailed schemas, rubrics, and escalation rules and wants those controls embedded in the workflow.
In practice, a hybrid architecture may still make sense: one system can manage a broad catalog or expert operation while another handles a specialized evaluation or orchestration stage. If you take that route, make ownership of the canonical dataset, identifiers, lineage, and quality decisions explicit before production data starts flowing.
Quick Recap
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