# Labelbox vs PromptLayer

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. PromptLayer records 85,899 monthly visits with -4.6% growth and Free Trial, Contact for Pricing pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [PromptLayer](https://openaitools.app/tool/promptlayer) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://promptlayer.com |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | Prompt Engineering, Large Language Models (LLMs), AI Monitor, AI Developer Tools |
| Pricing Model | Contact for Pricing | Free Trial, Contact for Pricing |
| Monthly Visits | 762,803 | 85,899 |
| MoM Growth Rate | -22.8% | -4.6% |
| Primary Market | United States | United States |
| Direct Visits | 370,024 | 14,092 |
| Review Score | 4.4 | 4.5 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## Labelbox

Labelbox occupies an unusual position in the AI stack: it does not primarily sell a chatbot or a generator, it builds the environments and data that models learn from plus the agents that take over recurring engineering and research work. The homepage claim is direct — frontier AI labs train on Labelbox environments and expert data, while engineering and research teams hand their recurring backlog to agents running on Recursion. A recent Series E of more than $100 million signals a heavily funded bet on that thesis.,For enterprises, the newer half of the product is Recursion. Agents are triggered by real signals — a failing build, a PagerDuty alert, a new dependency release, a fresh model checkpoint, or a Monday morning schedule — and then work in the background until the job is done. The examples the company promotes are concrete deliverables rather than summaries: a tested pull request with a preview link, a postmortem draft in Confluence with every event sourced, an ablation table naming the changes that mattered, or a patch for each exposed service after a critical CVE. A grader agent scores each run, and the graded session leaves behind memories, notes and skills that the next session reads before it acts.,The original foundation remains the data business: labeling, annotation, curation and model evaluation supported by expert human reviewers, used for computer vision, NLP, supervised fine-tuning, RLHF and red teaming. In practice the two halves reinforce each other, since the environments that produce high-quality training data are the same environments where agents learn to do real work. Pricing is enterprise-style, routed through sales conversations and shaped by data volume and agent usage, with no public self-serve tier.

**Pricing & Plans:** Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases.

- **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.
- **Native tool and data integrations:** Agents operate against the tools teams already use, including GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery, Databricks, Vercel and cloud providers.

**Strengths:** 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; Integrates with the toolchain teams already run: GitHub, Jira, Linear, Slack, Datadog, PagerDuty, BigQuery and Databricks

**Limitations:** No self-serve pricing or open signup: everything routes through a sales conversation, which slows down evaluation; The platform assumes real engineering and research infrastructure (GitHub, cloud providers, data warehouses) to deliver value; Positioning has shifted quickly toward autonomous agent sessions, so teams wanting only labeling workflows may find it heavier than needed

[Profile (Markdown)](https://openaitools.app/tool/labelbox.md)

## PromptLayer

PromptLayer targets the gap between prototyping with an LLM API and running that application reliably in production. It packages the tooling teams usually end up building themselves: a prompt registry, a visual CMS for editing and versioning prompts, an evaluation harness, and an observability stack that ties production traffic back to the prompt versions that generated it.

The core insight is that prompt quality is a product concern, not only an engineering one. Because prompts live in a dashboard rather than scattered through a codebase, domain experts — product managers, marketers, curriculum designers, support leads — can edit and test them directly, while engineers keep control over what actually ships. Version history, diffs, comments and rollbacks keep that collaboration auditable.

Evidence comes from evaluation and production. Teams define datasets, run automated and human grading, set up regression tests that fire whenever a prompt changes, and backtest new versions against historical data. In production, traces connect each request to its prompt version, so quality, cost and latency can be compared side by side. Agent workflows get the same treatment: prompts, tool calls, evaluations and logs stay in one place for multi-step runs.

**Pricing & Plans:** PromptLayer has a free tier and three paid tiers. Free is $0/month with 5 users, 2.5k requests per month, 1 workspace, 250 eval cell executions and 10MB datasets. Pro is $49/month and adds unlimited playgrounds, unlimited workspaces and 150MB datasets. Team is $500/month with 25 users, 100k+ requests, 7.5k+ eval cell executions and 1GB datasets. Enterprise offers custom limits plus RBAC, deployment approvals, HIPAA with BAA, self-hosted or EU hosting and dedicated support. Overage is billed per transaction (about $0.003 on Pro, $0.002 on Team).

- **No-code visual prompt CMS:** Edit, version, diff and roll back prompts from a visual dashboard with no coding, so product, marketing and content teams can iterate on prompts while engineers keep control of the deploy.
- **Evaluations and regression testing:** Run dataset-backed tests with human and AI graders, trigger regression evals whenever a prompt changes, backtest new versions against historical data and compare prompts across models.
- **Observability and tracing:** Connect production traces to the exact prompt version that produced them and inspect failures, cost, latency and token usage by feature, model or user.
- **Agent workflow support:** Build, trace and improve agent workflows with prompts, tool calls, evaluations and logs in one place, so multi-step runs can be tested before they ship.
- **A/B testing and usage analytics:** Release new prompt versions gradually, compare live metrics, and see exactly how the LLM application is being used, by whom and how often, without jumping to a separate analytics tool.

**Limitations:** Team plan jumps to $500/month, which is steep for small teams that outgrow Pro; Usage beyond plan limits is billed per transaction, so heavy LLM traffic needs forecasting; Role-based access controls, deployment approvals and SSO are Enterprise-only; Live in the developer ecosystem, so setup expects an LLM API and engineering involvement

[Profile (Markdown)](https://openaitools.app/tool/promptlayer.md)

## Frequently Asked Questions

### How do Labelbox and PromptLayer compare in monthly traffic?

Labelbox has 762,803 monthly visits, while PromptLayer has 85,899. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Labelbox has a month-over-month growth rate of -22.8%; PromptLayer has -4.6%.

### How does pricing compare?

Labelbox: Contact for Pricing. Contact for pricing. Labelbox does not publish self-serve plans or public rate cards; its pricing route instead directs interested teams to talk to sales, and cost is shaped by data production scope, agent deployment and enterprise requirements. There is no published free tier. Prospective customers should request a quote based on their volumes and use cases. PromptLayer: Free Trial, Contact for Pricing. PromptLayer has a free tier and three paid tiers. Free is $0/month with 5 users, 2.5k requests per month, 1 workspace, 250 eval cell executions and 10MB datasets. Pro is $49/month and adds unlimited playgrounds, unlimited workspaces and 150MB datasets. Team is $500/month with 25 users, 100k+ requests, 7.5k+ eval cell executions and 1GB datasets. Enterprise offers custom limits plus RBAC, deployment approvals, HIPAA with BAA, self-hosted or EU hosting and dedicated support. Overage is billed per transaction (about $0.003 on Pro, $0.002 on Team).

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Streamlines end-to-end computer vision workflows from annotation to model deployment, but lacks Labelbox's general enterprise agent orchestration. | [.md](https://openaitools.app/tool/roboflow.md) |
| 2 | [Dify](https://openaitools.app/tool/dify-ai) | 819,691 | -15% | Freemium | Build, deploy, and govern production-ready agentic AI workflows | [.md](https://openaitools.app/tool/dify-ai.md) |
| 3 | [Appen](https://openaitools.app/tool/appen-com) | 665,840 | +0.2% | Contact for Pricing | Relies heavily on managed human-in-the-loop workforce services for data validation, offering less self-serve technical tooling than Labelbox. | [.md](https://openaitools.app/tool/appen-com.md) |
| 4 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Delivers enterprise-grade labeling and model evaluation at massive scale, but often requires higher enterprise budgets than Labelbox. | [.md](https://openaitools.app/tool/scale.md) |
| 5 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Offers robust multimodal annotation and workflow automation, featuring slightly more intuitive data curation pipelines for computer vision teams. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 6 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) |
| 7 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) |
| 8 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | Specializes in advanced computer vision and physical AI data pipelines, though with a narrower focus than general text-and-image labeling. | [.md](https://openaitools.app/tool/encord-com.md) |
| 9 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Provides a flexible open-source alternative for custom data labeling, but requires self-hosting and manual infrastructure maintenance. | [.md](https://openaitools.app/tool/label-studio.md) |
| 10 | [Future AGI](https://openaitools.app/tool/future-agi) | 121,741 | -7.8% | Freemium, Usage-based | Build, simulate, evaluate and monitor self-improving AI agents | [.md](https://openaitools.app/tool/future-agi.md) |

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Source: https://openaitools.app/compare/labelbox-vs-promptlayer

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