--- title: "deeplake vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/activeloopai-deeplake-vs-tensorchord-awesome-llmops" tools: ["activeloopai-deeplake", "tensorchord-awesome-llmops"] --- # deeplake vs Awesome-LLMOps *GraphCanon updated Aug 20, 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 Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [deeplake](https://deeplake.ai) reports 9.2k GitHub stars, 721 forks, and 63 open issues, last pushed May 21, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [deeplake's repository](https://github.com/activeloopai/deeplake) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [deeplake](/tools/activeloopai-deeplake.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | AI Data Runtime for Agents with scalable retrieval and training features | An awesome & curated list of best LLMOps tools for developers | | Stars | 9,224 | 5,915 | | Forks | 721 | 993 | | Open issues | 63 | 247 | | Language | C++ | Shell | | 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. | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | Deeplake uses the Apache-2.0 license, allowing free use in both open source and commercial projects with attribution. | CC0-1.0 | | Categories | Data & Retrieval, Model Training, Vector Databases | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deeplake](/tools/activeloopai-deeplake.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 87d | 91d | | Open issues (now) | 63 | 247 | | Stars delta | +16 (30d) | +28 (30d) | | Open issues delta | -6 (30d) | +66 (30d) | | Full report | [trust report](/tools/activeloopai-deeplake/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## 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: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose deeplake if… - deeplake is primarily C++; Awesome-LLMOps is Shell. - License: deeplake is Apache-2.0, Awesome-LLMOps is CC0-1.0. - 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 Vector Databases. - When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; deeplake is C++. - License: Awesome-LLMOps is CC0-1.0, deeplake is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## 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 Awesome-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between deeplake and Awesome-LLMOps? deeplake: AI Data Runtime for Agents with scalable retrieval and training features. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose deeplake over Awesome-LLMOps? Choose deeplake over Awesome-LLMOps when deeplake is primarily C++; Awesome-LLMOps is Shell; License: deeplake is Apache-2.0, Awesome-LLMOps is CC0-1.0; 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 Vector Databases; When you are developing applications that require seamless integration with AI agents, as Deeplake supports agent-centric design. ### When should I choose Awesome-LLMOps over deeplake? Choose Awesome-LLMOps over deeplake when Awesome-LLMOps is primarily Shell; deeplake is C++; License: Awesome-LLMOps is CC0-1.0, deeplake is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### 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 Awesome-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is deeplake or Awesome-LLMOps more popular on GitHub? deeplake has more GitHub stars (9,224 vs 5,915). Stars measure visibility, not whether either tool fits your constraints. ### Are deeplake and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (deeplake: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to deeplake or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [deeplake alternatives](/tools/activeloopai-deeplake/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([deeplake markdown twin](/tools/activeloopai-deeplake/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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-tensorchord-awesome-llmops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deeplake or Awesome-LLMOps? deeplake: Steady. Awesome-LLMOps: Slowing. 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 Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deeplake trust report](/tools/activeloopai-deeplake/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/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/_