--- title: "ai-serving vs langserve" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-langchain-ai-langserve" tools: ["autodeployai-ai-serving", "langchain-ai-langserve"] --- # ai-serving vs langserve *GraphCanon updated Aug 14, 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 langserve if langServe offers tools to deploy and serve models using LangChain with FastAPI. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [langserve](https://github.com/langchain-ai/langserve) has 2.3k stars, 272 forks, and 139 open issues, last pushed May 5, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [langserve's repository](https://github.com/langchain-ai/langserve). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [langserve](/tools/langchain-ai-langserve.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | LangServe 🦜️🏓 | | Stars | 166 | 2,332 | | Forks | 31 | 272 | | Open issues | 3 | 139 | | Language | Scala | JavaScript | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | LangServe offers tools to deploy and serve models using LangChain with FastAPI. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [langserve](/tools/langchain-ai-langserve.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Archived (8%) | | Days since push | 171d | 94d | | Archived on GitHub | No | Yes | | Open issues (now) | 3 | 139 | | Stars delta | 0 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/langchain-ai-langserve/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: langserve - **Adopt for:** LangServe offers tools to deploy and serve models using LangChain with FastAPI. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; langserve is JavaScript. - License: ai-serving is Apache-2.0, langserve is Other. - 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 langserve if… - langserve is primarily JavaScript; ai-serving is Scala. - License: langserve is Other, ai-serving is Apache-2.0. - Tags unique to langserve: deployment, fastapi, langchain, llm. - When you are working in an environment where models need to be served efficiently and require the capabilities of both LangChain and FastAPI. ## 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 langserve - When you prefer using frameworks or tools that are not built around Python's ecosystem and require languages like JavaScript or Java. - If your project specifically requires a non-FastAPI backend for serving models because of specific performance criteria, constraints, or compatibility issues with FastAPI. ## Common questions ### What is the difference between ai-serving and langserve? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. langserve: LangServe 🦜️🏓. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over langserve? Choose ai-serving over langserve when ai-serving is primarily Scala; langserve is JavaScript; License: ai-serving is Apache-2.0, langserve is Other; 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 langserve over ai-serving? Choose langserve over ai-serving when langserve is primarily JavaScript; ai-serving is Scala; License: langserve is Other, ai-serving is Apache-2.0; Tags unique to langserve: deployment, fastapi, langchain, llm; When you are working in an environment where models need to be served efficiently and require the capabilities of both LangChain and FastAPI. ### 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 langserve? When you prefer using frameworks or tools that are not built around Python's ecosystem and require languages like JavaScript or Java. If your project specifically requires a non-FastAPI backend for serving models because of specific performance criteria, constraints, or compatibility issues with FastAPI. ### Is ai-serving or langserve more popular on GitHub? langserve has more GitHub stars (2,332 vs 166). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and langserve open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, langserve: Other). ### Where can I find alternatives to ai-serving or langserve? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [langserve alternatives](/tools/langchain-ai-langserve/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [langserve markdown twin](/tools/langchain-ai-langserve/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-langchain-ai-langserve.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or langserve? ai-serving: Slowing. langserve: Archived. 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 langserve? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [langserve trust report](/tools/langchain-ai-langserve/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/_