# Labelbox vs Postman

Labelbox records 762,803 monthly visits with -22.8% month-over-month growth and Contact for Pricing pricing. Postman records 1,764,877 monthly visits with -7.6% growth and Freemium pricing.

| Metric | [Labelbox](https://openaitools.app/tool/labelbox) | [Postman](https://openaitools.app/tool/postman) |
| :--- | :--- | :--- |
| Website URL | https://labelbox.com | https://www.postman.com |
| Categories | AI Developer Tools, AI API, AI Workflow, AI Productivity Tools | AI Developer Tools, AI API, AI Agent, AI Workflow |
| Pricing Model | Contact for Pricing | Freemium |
| Monthly Visits | 762,803 | 1,764,877 |
| MoM Growth Rate | -22.8% | -7.6% |
| Primary Market | United States | India |
| Direct Visits | 370,024 | 580,232 |
| Review Score | 4.4 | 4.7 |
| Saved Count | 0 | 0 |
| 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)

## Postman

Postman began as a simple HTTP client and has grown into a platform that teams use across the entire API lifecycle. Requests live in collections, collections are organized into workspaces, and environments hold the variables that differ between local, staging, and production. From there, teams generate specs from requests or requests from specs, publish mock servers before an API exists, and use native Git support plus a CLI to keep API definitions in version control.

Testing and monitoring are first-class: the Collection Runner executes suites with data files and exports results, performance testing simulates load locally or from the cloud, and monitors poll live endpoints for uptime and drift. Documentation can be generated and custom-branded, and a shared package library and vault help teams reuse logic and manage secrets.

The newest direction is agentic. Agent Mode, the Postman CLI with installable agent skills, and a Model Context Protocol server let AI coding assistants operate Postman directly. A Context Graph gives those agents a structured map of APIs, collections, and relationships, while Passport lets callers — including agents — invoke approved APIs through a proxy without ever holding the secret. Around the core product, Postman also offers Fabric for AI traffic governance, Fern for docs and SDK generation, and Astropods for running agents in production. Postman reports being used by more than 50 million developers and 500,000 companies.

**Pricing & Plans:** Freemium. Free for individuals with 50 AI credits per month and core client tools. Solo around $9 per month billed annually (400 AI credits per month, data-driven testing, custom branding, expanded monitoring). Team around $19 per user per month billed annually (collaboration, viewer seats, basic RBAC, 400 AI credits per user). Enterprise is contact-sales and adds API Catalog, governance, advanced RBAC, audit logs, and pooled credits. AI credit overages and cloud performance testing are billed as usage add-ons.

- **API client and collections:** A multi-protocol client with collections, environments, variables, and reusable requests that works for local debugging and for driving automated test suites.
- **Automated testing and Collection Runner:** Generates assertions automatically, supports data-driven runs with exported datasets, and executes tests locally, in the cloud, or from CI pipelines via the Postman CLI.
- **AI agents and MCP server:** Agent Mode, the Postman CLI agent skills, and a Model Context Protocol server let coding assistants build collections, write tests, and trigger API workflows from inside the editor.
- **Context Graph:** A structured, queryable map of an organization's APIs, collections, and dependencies that lets agents find the right service and understand what a schema change would break.
- **Governance and enterprise controls:** API Catalog, Insights, RBAC with SSO/SAML and SCIM, secret scanning, and audit logs help large organizations standardize and secure API usage across teams.

**Strengths:** Covers the full API lifecycle in one tool instead of stitching together several products; Strong AI story with Agent Mode, installable agent skills, an MCP server, and a Context Graph; Free plan is genuinely usable and paid tiers are comparatively affordable; Very large community, extensive docs, and integrations across CI, Git, and identity providers

**Limitations:** The desktop app is resource-heavy and can feel slow on older machines; Many advanced capabilities (API Catalog, advanced RBAC, governance, SSO/SCIM) sit behind Enterprise pricing; AI credits and Context Graph API queries are metered, so heavy agent use adds pay-as-you-go cost; The breadth of features, workspaces, and roles means teams need onboarding time before they are productive

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

## Frequently Asked Questions

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

Labelbox has 762,803 monthly visits, while Postman has 1,764,877. 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%; Postman has -7.6%.

### 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. Postman: Freemium. Freemium. Free for individuals with 50 AI credits per month and core client tools. Solo around $9 per month billed annually (400 AI credits per month, data-driven testing, custom branding, expanded monitoring). Team around $19 per user per month billed annually (collaboration, viewer seats, basic RBAC, 400 AI credits per user). Enterprise is contact-sales and adds API Catalog, governance, advanced RBAC, audit logs, and pooled credits. AI credit overages and cloud performance testing are billed as usage add-ons.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [VS Code](https://openaitools.app/tool/vs-code) | 7,397,370 | -3.8% | Freemium | Integrates API testing and development directly inside your code editor, but lacks Postman's dedicated workspace collection management and documentation publishing. | [.md](https://openaitools.app/tool/vs-code.md) |
| 2 | [Qoder](https://openaitools.app/tool/qoder) | 5,145,040 | +81.8% | Freemium, Free Trial, Free | Excels at autonomous multi-file code generation and repository-wide understanding, but is not purpose-built for manual API endpoint testing. | [.md](https://openaitools.app/tool/qoder.md) |
| 3 | [n8n](https://openaitools.app/tool/n8n) | 4,522,449 | -7.4% | Freemium | Powerful visual workflow automation and API integration builder, but lacks Postman's granular API request inspection and documentation features. | [.md](https://openaitools.app/tool/n8n.md) |
| 4 | [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) |
| 5 | [T3 Code](https://openaitools.app/tool/t3-code) | 1,071,866 | +52.5% | Free | Open-source control plane for orchestrating coding agents | [.md](https://openaitools.app/tool/t3-code.md) |
| 6 | [KaneAI](https://openaitools.app/tool/kaneai) | 901,340 | +13.2% | Freemium, Paid, Contact for Pricing | Automates end-to-end testing using plain language agents, but lacks Postman's manual debugging and environment variable management tools. | [.md](https://openaitools.app/tool/kaneai.md) |
| 7 | [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) |
| 8 | [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) |
| 9 | [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) |
| 10 | [Qase](https://openaitools.app/tool/qase) | 319,213 | -25.5% | Freemium, Free Trial, Contact for Pricing, Free | Offers superior test case management and CI integration for QA workflows, but lacks native API request crafting and mocking tools. | [.md](https://openaitools.app/tool/qase.md) |

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

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