# Encord vs Models.dev

Encord records 244,204 monthly visits with +9.4% month-over-month growth and Contact for Pricing pricing. Models.dev records 200,451 monthly visits with -3.9% growth and Free pricing.

| Metric | [Encord](https://openaitools.app/tool/encord-com) | [Models.dev](https://openaitools.app/tool/models-dev) |
| :--- | :--- | :--- |
| Website URL | https://encord.com | https://models.dev |
| Categories | AI Developer Tools, AI Models, AI For Data Analytics | AI Models, AI API, Open Source AI Models, AI Tools Directory |
| Pricing Model | Contact for Pricing | Free |
| Monthly Visits | 244,204 | 200,451 |
| MoM Growth Rate | +9.4% | -3.9% |
| Primary Market | United States | China |
| Direct Visits | 104,126 | 136,316 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## Encord

Encord positions itself as the multimodal data layer for physical AI. Instead of being a single-purpose labeling tool, it covers the whole path from raw data to model improvement: collecting and ingesting sensor data, curating what is worth labeling, running annotation workflows, evaluating models, and feeding failures back into the next training round.

The platform is unusually broad in the modalities it handles, spanning images, video, 3D and LiDAR, documents and text, audio, and medical imaging formats such as DICOM and NIfTI. That breadth matters for robotics, autonomous vehicles, drones, industrial inspection, surgical AI, and other domains where a single model consumes several sensor types at once. Label lineage and quality controls are built in so teams can trace where each label came from.

Encord also offers Data-as-a-Service, supplying vetted domain experts and managed collection pipelines that plug into the same platform, which is useful for teams that need throughput without building an in-house annotation organization. The company highlights an API/SDK-first design and zero data migration, meaning customer data can remain in its own cloud. Its customer list includes enterprise and frontier AI teams, and pricing is quoted on request.

**Pricing & Plans:** Encord uses custom pricing quoted on request; the site directs interested teams to book a demo rather than publishing public tiers.

- **Multimodal annotation and labeling:** Native video, image, LiDAR, audio, text, and sensor-fusion labeling in one workflow, with label lineage and quality controls built for production scale.
- **Data curation and collection:** Use embedding-based search and model-in-the-loop curation to surface rare edge cases and close distribution gaps before they hurt model performance.
- **Model alignment and evaluation:** Orchestrate RLHF, rubric-based evaluation, and pairwise comparison for models in production, then route failures back into training.
- **Data-as-a-Service:** Access vetted domain experts for annotation and training-ready data collection, fully integrated with the Encord platform.
- **Zero data migration:** Keep your data in your own cloud while Encord supplies the tooling, with API/SDK-first integrations and enterprise security controls.

**Strengths:** Covers the full data pipeline from collection and annotation to evaluation and alignment; Strong multimodal support including LiDAR, DICOM, audio, video, and text; Optional Data-as-a-Service with vetted domain experts built into the platform; Zero data migration and API/SDK-first design keep engineering overhead low

**Limitations:** No self-serve free tier published; access starts with a sales conversation; Enterprise scope can be more than small teams need; Breadth of features across modalities implies a learning curve for new users

[Profile (Markdown)](https://openaitools.app/tool/encord-com.md)

## Models.dev

Models.dev exists to answer a very practical developer question: which model should I use, and what will it cost? The site organises every entry under a canonical identifier in the form provider/model, so the same model served by several providers can be compared directly rather than guessed at. Columns cover context window, maximum output, whether input and output are supported, reasoning, tool calling, structured output, temperature control, open versus closed weights, price per million tokens, and release and update dates.

Because model releases move fast, the catalogue is built to stay current, with sortable and searchable tables that let you scan by lab or provider and filter down to what matters. Entries include both closed flagship models and open-weight alternatives, making it useful for cost planning as well as capability research.

The project is open source and free to use, and it is intended to be consumed programmatically. Developers can pull the catalogue into their own tooling through a JSON API or a type-safe SDK, which makes it a practical backend for dashboards, routing logic, or internal cost calculators rather than just a reference page to browse.

**Pricing & Plans:** Models.dev is free and open source. It is a reference database, so the prices it lists are the per-million-token rates charged by the model providers themselves, not a fee for using the site.

- **Canonical model identifiers:** Every model uses a consistent provider/model format so the same model across providers can be compared directly.
- **Specification comparison:** Side-by-side context window, output limit, reasoning, tool calling, structured output, and temperature support.
- **Pricing transparency:** Input and output prices per million tokens listed alongside release and update dates.
- **Open and closed models:** Covers closed flagship APIs and open-weight models from many labs in one sortable catalogue.
- **Developer access:** Consume the catalogue through a JSON API or a type-safe SDK for your own tools and dashboards.

**Strengths:** Free and open source with no account required; Consistent canonical identifiers make cross-provider comparison reliable; Covers pricing, limits, and capabilities in one sortable table; Available as a JSON API and type-safe SDK for programmatic use

**Limitations:** It is a reference catalogue, not an inference or hosting service; Data quality depends on community and maintainer updates keeping pace with releases; Interface is aimed at developers and offers little guidance for non-technical users

[Profile (Markdown)](https://openaitools.app/tool/models-dev.md)

## Frequently Asked Questions

### How do Encord and Models.dev compare in monthly traffic?

Encord has 244,204 monthly visits, while Models.dev has 200,451. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Encord has a month-over-month growth rate of +9.4%; Models.dev has -3.9%.

### How does pricing compare?

Encord: Contact for Pricing. Encord uses custom pricing quoted on request; the site directs interested teams to book a demo rather than publishing public tiers. Models.dev: Free. Models.dev is free and open source. It is a reference database, so the prices it lists are the per-million-token rates charged by the model providers themselves, not a fee for using the site.

## 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 | Faster setup and model deployment for computer vision, but less advanced enterprise data infrastructure features. | [.md](https://openaitools.app/tool/roboflow.md) |
| 2 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Stronger enterprise workflow automation and agentic labeling, but less specialized in complex physical AI video pipelines. | [.md](https://openaitools.app/tool/labelbox.md) |
| 3 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Similar multimodal annotation capabilities, but features more integrated project management and dataset curation tools. | [.md](https://openaitools.app/tool/super-annotate.md) |
| 4 | [Stork](https://openaitools.app/tool/stork) | 201,544 | +2.3% | Free | Includes model pricing trackers and developer news alongside tools directories, but less focused on raw API specs. | [.md](https://openaitools.app/tool/stork.md) |
| 5 | [Memories.ai](https://openaitools.app/tool/memories-ai) | 188,127 | -14.6% | Freemium, Contact for Pricing | Video data infrastructure for physical AI and robot training | [.md](https://openaitools.app/tool/memories-ai.md) |
| 6 | [Label Studio](https://openaitools.app/tool/label-studio) | 137,199 | -16.1% | Contact for Pricing, Free | Completely open-source and highly customizable for self-hosted labeling, but lacks built-in enterprise multimodal infrastructure. | [.md](https://openaitools.app/tool/label-studio.md) |
| 7 | [Voxel51](https://openaitools.app/tool/voxel51-com) | 117,715 | -8.2% | Contact for Pricing | Superior visual dataset search and quality evaluation capabilities, but relies more on programmatic workflows. | [.md](https://openaitools.app/tool/voxel51-com.md) |
| 8 | [Rerun](https://openaitools.app/tool/rerun) | 54,018 | -23.7% | Free | The open-source data layer for robotics and physical AI | [.md](https://openaitools.app/tool/rerun.md) |
| 9 | [InfraBase.ai](https://openaitools.app/tool/infrabase-ai) | 39,072 | -13.6% | Free | Catalogues and compares AI infrastructure tools and services across categories, but lacks deep per-token pricing tables. | [.md](https://openaitools.app/tool/infrabase-ai.md) |
| 10 | [Datature](https://openaitools.app/tool/datature) | 24,530 | -23.2% | Freemium, Paid, Contact for Pricing | Provides end-to-end model training alongside annotation, but with a narrower focus than Encord's physical AI platform. | [.md](https://openaitools.app/tool/datature.md) |

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Source: https://openaitools.app/compare/encord-com-vs-models-dev

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