--- title: "ai-serving vs BodhiApp" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-bodhisearch-bodhiapp" tools: ["autodeployai-ai-serving", "bodhisearch-bodhiapp"] --- # ai-serving vs BodhiApp *GraphCanon updated Sep 20, 2026* ## Verdict Pick ai-serving if ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker; pick BodhiApp if bodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [BodhiApp](https://getbodhi.app/) has 139 stars, 11 forks, and 12 open issues, last pushed Sep 20, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [BodhiApp's repository](https://github.com/BodhiSearch/BodhiApp). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs | | Stars | 166 | 139 | | Forks | 31 | 11 | | Open issues | 3 | 12 | | Language | Scala | TypeScript | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | BodhiApp streamlines local deployment of open-source and open-weight LLMs via Docker images, compatible with multiple hardware acceleration methods. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The license information for BodhiApp has not been provided. | | Categories | Inference & Serving | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [BodhiApp](/tools/bodhisearch-bodhiapp.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 208d | 0d | | Open issues (now) | 3 | 12 | | Stars delta | 0 (30d) | +3 (30d) | | Open issues delta | 0 (30d) | +2 (30d) | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/bodhisearch-bodhiapp/trust.md) | ## Decision facts: ai-serving - **Adopt for:** Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. ## 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. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; BodhiApp is TypeScript. - Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx. - When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### Choose BodhiApp if… - BodhiApp is primarily TypeScript; ai-serving is Scala. - 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 NOT to use ai-serving - Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. - Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. - If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images. ## 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. ## Common questions ### What is the difference between ai-serving and BodhiApp? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. BodhiApp: Run Open Source/Open Weight LLMs locally with OpenAI compatible APIs. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over BodhiApp? Choose ai-serving over BodhiApp when ai-serving is primarily Scala; BodhiApp is TypeScript; Tags unique to ai-serving: ai-serving, grpc, inference-server, onnx; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### When should I choose BodhiApp over ai-serving? Choose BodhiApp over ai-serving when BodhiApp is primarily TypeScript; ai-serving is Scala; 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 avoid ai-serving? Avoid if your team lacks familiarity or willingness to use Scala for deployment through sbt build system for customization needs. Not suitable when only one model format, either PMML or ONNX but not both, is needed and a simpler solution would suffice. If your project strictly requires a non-Dockerized setup that does not align with using pre-built Docker images. ### 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. ### Is ai-serving or BodhiApp more popular on GitHub? ai-serving has more GitHub stars (166 vs 139). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and BodhiApp open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to ai-serving or BodhiApp? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [BodhiApp alternatives](/tools/bodhisearch-bodhiapp/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [BodhiApp markdown twin](/tools/bodhisearch-bodhiapp/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/autodeployai-ai-serving-vs-bodhisearch-bodhiapp.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or BodhiApp? ai-serving: Slowing. BodhiApp: 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 ai-serving and BodhiApp? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [BodhiApp trust report](/tools/bodhisearch-bodhiapp/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=autodeployai-ai-serving`](/api/graphcanon/graph?tool=autodeployai-ai-serving) - 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/_