--- title: "BodhiApp vs fastDeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bodhisearch-bodhiapp-vs-notai-tech-fastdeploy" tools: ["bodhisearch-bodhiapp", "notai-tech-fastdeploy"] --- # BodhiApp vs fastDeploy *GraphCanon updated Sep 20, 2026* ## Verdict Pick BodhiApp if bodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods; pick fastDeploy if fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. [BodhiApp](https://getbodhi.app/) reports 139 GitHub stars, 11 forks, and 12 open issues, last pushed Sep 20, 2026. [fastDeploy](https://github.com/notAI-tech/fastDeploy) has 105 stars, 17 forks, and 0 open issues, last pushed Feb 10, 2026. Figures are from public GitHub metadata via [BodhiApp's repository](https://github.com/BodhiSearch/BodhiApp) and [fastDeploy's repository](https://github.com/notAI-tech/fastDeploy). | | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Tagline | Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs | Deploy DL/ML inference pipelines with minimal extra code. | | Stars | 139 | 105 | | Forks | 11 | 17 | | Open issues | 12 | 0 | | Language | TypeScript | Python | | Adopt for | BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. | fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. | | Persona | - | - | | Runtime | - | - | | License | The license information for BodhiApp has not been provided. | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 221d | | Open issues (now) | 12 | 0 | | Stars delta | +3 (30d) | 0 (30d) | | Open issues delta | +2 (30d) | 0 (30d) | | Full report | [trust report](/tools/bodhisearch-bodhiapp/trust.md) | [trust report](/tools/notai-tech-fastdeploy/trust.md) | ## Decision facts: BodhiApp - **Pricing:** unknown - Pricing details are not mentioned in the repository data. - **Requirements:** Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen. - **Adopt for:** BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. - **License detail:** The license information for BodhiApp has not been provided. ## Decision facts: fastDeploy - **Pricing:** freemium - - - **Requirements:** - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory. - **Adopt for:** fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. ## Choose when ### Choose BodhiApp if… - BodhiApp is primarily TypeScript; fastDeploy is Python. - Pricing: Pricing details are not mentioned in the repository data.. - Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen.. - Tags unique to BodhiApp: gemma, generative-ai, llama, llm. - Also covers LLM Frameworks. - You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ### Choose fastDeploy if… - fastDeploy is primarily Python; BodhiApp is TypeScript. - Pricing: -. - Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.. - Tags unique to fastDeploy: deep-learning, docker, falcon, gevent. - When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding. ## When NOT to use BodhiApp - Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models. - If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods. - You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems. ## When NOT to use fastDeploy - Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. - Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations. ## Common questions ### What is the difference between BodhiApp and fastDeploy? BodhiApp: Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs. fastDeploy: Deploy DL/ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories. ### When should I choose BodhiApp over fastDeploy? Choose BodhiApp over fastDeploy when BodhiApp is primarily TypeScript; fastDeploy is Python; Pricing: Pricing details are not mentioned in the repository data.; Requirements: Requires Docker; Requires Docker environment. Specific model requirements vary depending on the hardware variant chosen.; Tags unique to BodhiApp: gemma, generative-ai, llama, llm; Also covers LLM Frameworks; You need to deploy LLMs locally with flexible hardware support including AMD, NVIDIA GPUs, and CPUs. ### When should I choose fastDeploy over BodhiApp? Choose fastDeploy over BodhiApp when fastDeploy is primarily Python; BodhiApp is TypeScript; Pricing: -; Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.; Tags unique to fastDeploy: deep-learning, docker, falcon, gevent; When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding. ### When should I avoid BodhiApp? Your project strictly requires non-local deployment options, as BodhiApp focuses on local hosting of models. If your environment is limited to unsupported GPU hardware or lacks adequate drivers for CUDA, ROCm, or Vulkan acceleration methods. You need support beyond Mac platforms as BodhiApp does not yet provide installation instructions for other operating systems. ### When should I avoid fastDeploy? Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations. ### Is BodhiApp or fastDeploy more popular on GitHub? BodhiApp has more GitHub stars (139 vs 105). Stars measure visibility, not whether either tool fits your constraints. ### Are BodhiApp and fastDeploy open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to BodhiApp or fastDeploy? GraphCanon lists graph-backed alternatives at [BodhiApp alternatives](/tools/bodhisearch-bodhiapp/alternatives) and [fastDeploy alternatives](/tools/notai-tech-fastdeploy/alternatives) ([BodhiApp markdown twin](/tools/bodhisearch-bodhiapp/alternatives.md), [fastDeploy markdown twin](/tools/notai-tech-fastdeploy/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/bodhisearch-bodhiapp-vs-notai-tech-fastdeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, BodhiApp or fastDeploy? BodhiApp: Very active. fastDeploy: 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 BodhiApp and fastDeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [BodhiApp trust report](/tools/bodhisearch-bodhiapp/trust); [fastDeploy trust report](/tools/notai-tech-fastdeploy/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bodhisearch-bodhiapp`](/api/graphcanon/graph?tool=bodhisearch-bodhiapp) - 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/_