--- title: "deeplake vs infinity" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-deeplake-vs-infiniflow-infinity" tools: ["activeloopai-deeplake", "infiniflow-infinity"] --- # deeplake vs infinity *GraphCanon updated Aug 21, 2026* ## Verdict Pick deeplake if deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities; pick infinity if designed for high-speed hybrid searches in LLM applications, infinity supports dense vector, sparse vector, tensor, and full-text data types. [deeplake](https://deeplake.ai) reports 9.2k GitHub stars, 721 forks, and 63 open issues, last pushed May 21, 2026. [infinity](https://infiniflow.org) has 4.7k stars, 437 forks, and 64 open issues, last pushed Aug 17, 2026. Figures are from public GitHub metadata via [deeplake's repository](https://github.com/activeloopai/deeplake) and [infinity's repository](https://github.com/infiniflow/infinity). | | [deeplake](/tools/activeloopai-deeplake.md) | [infinity](/tools/infiniflow-infinity.md) | | --- | --- | --- | | Tagline | AI Data Runtime for Agents with scalable retrieval and training features | AI-native database for LLM applications offering fast hybrid search capabilities. | | Stars | 9,224 | 4,675 | | Forks | 721 | 437 | | Open issues | 63 | 64 | | Language | C++ | C++ | | Adopt for | Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities. | Designed for high-speed hybrid searches in LLM applications, infinity supports dense vector, sparse vector, tensor, and full-text data types. | | Persona | - | - | | Runtime | - | - | | License | Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution. | Apache-2.0 | | Categories | Data & Retrieval, Model Training, Vector Databases | Data & Retrieval, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deeplake](/tools/activeloopai-deeplake.md) | [infinity](/tools/infiniflow-infinity.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 87d | 3d | | Open issues (now) | 63 | 64 | | Stars delta | +16 (30d) | +51 (30d) | | Open issues delta | -6 (30d) | -2 (30d) | | Full report | [trust report](/tools/activeloopai-deeplake/trust.md) | [trust report](/tools/infiniflow-infinity/trust.md) | ## Shared compatibility - **Python**: [deeplake](/tools/activeloopai-deeplake.md) - Python runtime; [infinity](/tools/infiniflow-infinity.md) - Python runtime ## Decision facts: deeplake - **Pricing:** unknown - Pricing details are not specified for Deeplake's public repository. - **Requirements:** Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`. - **Adopt for:** Deeplake is an AI Data Runtime for Agents designed with serverless Postgres and multimodal data lake support, targeting scalable retrieval and training capabilities. - **License detail:** Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution. ## Decision facts: infinity - **Adopt for:** Designed for high-speed hybrid searches in LLM applications, infinity supports dense vector, sparse vector, tensor, and full-text data types. ## Choose when ### Choose deeplake if… - Pricing: Pricing details are not specified for Deeplake's public repository.. - Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`.. - Tags unique to deeplake: agent, agentic-rag, ai, computer-vision. - Also covers Model Training. - When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### Choose infinity if… - Tags unique to infinity: ai-native, approximate-nearest-neighbor-search, bm25, cpp20. - When your application requires rapid hybrid search capabilities across multiple data types including tensors and full texts. - More recently updated (last pushed Aug 17, 2026). ## When NOT to use deeplake - If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. - When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training. ## When NOT to use infinity - If your project does not benefit from fast hybrid search features or if you prefer not to use an AI-native database solution. - When support for only dense vectors is sufficient, and the added complexity of supporting tensors and full texts is unnecessary. ## Common questions ### What is the difference between deeplake and infinity? deeplake: AI Data Runtime for Agents with scalable retrieval and training features. infinity: AI-native database for LLM applications offering fast hybrid search capabilities.. See the comparison table for live GitHub stats and shared categories. ### When should I choose deeplake over infinity? Choose deeplake over infinity when Pricing: Pricing details are not specified for Deeplake's public repository.; Requirements: Deeplake can be installed using pip, making it accessible via the command `pip install deeplake`.; Tags unique to deeplake: agent, agentic-rag, ai, computer-vision; Also covers Model Training; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### When should I choose infinity over deeplake? Choose infinity over deeplake when Tags unique to infinity: ai-native, approximate-nearest-neighbor-search, bm25, cpp20; When your application requires rapid hybrid search capabilities across multiple data types including tensors and full texts; More recently updated (last pushed Aug 17, 2026). ### When should I avoid deeplake? If your project does not benefit from an agent-centric architecture and you primarily require traditional database operations without multimodal features. When cost control is critical and serverless PostgreSQL might introduce variable costs compared to on-premises solutions for data retrieval and training. ### When should I avoid infinity? If your project does not benefit from fast hybrid search features or if you prefer not to use an AI-native database solution. When support for only dense vectors is sufficient, and the added complexity of supporting tensors and full texts is unnecessary. ### Is deeplake or infinity more popular on GitHub? deeplake has more GitHub stars (9,224 vs 4,675). Stars measure visibility, not whether either tool fits your constraints. ### Are deeplake and infinity open source? Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, infinity: Apache-2.0). ### Where can I find alternatives to deeplake or infinity? GraphCanon lists graph-backed alternatives at [deeplake alternatives](/tools/activeloopai-deeplake/alternatives) and [infinity alternatives](/tools/infiniflow-infinity/alternatives) ([deeplake markdown twin](/tools/activeloopai-deeplake/alternatives.md), [infinity markdown twin](/tools/infiniflow-infinity/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/activeloopai-deeplake-vs-infiniflow-infinity.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deeplake or infinity? deeplake: Steady. infinity: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for deeplake and infinity? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deeplake trust report](/tools/activeloopai-deeplake/trust); [infinity trust report](/tools/infiniflow-infinity/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=activeloopai-deeplake`](/api/graphcanon/graph?tool=activeloopai-deeplake) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_