--- title: "ai-serving vs sarathi-serve" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-microsoft-sarathi-serve" tools: ["autodeployai-ai-serving", "microsoft-sarathi-serve"] --- # ai-serving vs sarathi-serve *GraphCanon updated Aug 25, 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 sarathi-serve if sarathi Serve targets efficient low-latency and high-throughput inference for LLMs using Python. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [sarathi-serve](https://github.com/microsoft/sarathi-serve) has 520 stars, 65 forks, and 16 open issues, last pushed Jan 8, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [sarathi-serve's repository](https://github.com/microsoft/sarathi-serve). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [sarathi-serve](/tools/microsoft-sarathi-serve.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | A low-latency and high-throughput serving engine for LLMs | | Stars | 166 | 520 | | Forks | 31 | 65 | | Open issues | 3 | 16 | | Language | Scala | Python | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | Sarathi Serve targets efficient low-latency and high-throughput inference for LLMs using Python. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [sarathi-serve](/tools/microsoft-sarathi-serve.md) | | --- | --- | --- | | Days since push | 171d | 229d | | Open issues (now) | 3 | 16 | | Stars delta | 0 (30d) | +8 (30d) | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/microsoft-sarathi-serve/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: sarathi-serve - **Adopt for:** Sarathi Serve targets efficient low-latency and high-throughput inference for LLMs using Python. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; sarathi-serve is Python. - 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 sarathi-serve if… - sarathi-serve is primarily Python; ai-serving is Scala. - Tags unique to sarathi-serve: llama, llm-inference, pytorch, transformer. - Optimize Python-based projects needing quick responses from large language models. ## 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 sarathi-serve - Necessitate a non-Python environment for deployment and operation. - Prefer a tool that incorporates more than just low-latency, high-throughput focus such as multi-language support or specialized optimizations. ## Common questions ### What is the difference between ai-serving and sarathi-serve? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. sarathi-serve: A low-latency and high-throughput serving engine for LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over sarathi-serve? Choose ai-serving over sarathi-serve when ai-serving is primarily Scala; sarathi-serve is Python; 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 sarathi-serve over ai-serving? Choose sarathi-serve over ai-serving when sarathi-serve is primarily Python; ai-serving is Scala; Tags unique to sarathi-serve: llama, llm-inference, pytorch, transformer; Optimize Python-based projects needing quick responses from large language models. ### 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 sarathi-serve? Necessitate a non-Python environment for deployment and operation. Prefer a tool that incorporates more than just low-latency, high-throughput focus such as multi-language support or specialized optimizations. ### Is ai-serving or sarathi-serve more popular on GitHub? sarathi-serve has more GitHub stars (520 vs 166). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and sarathi-serve open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, sarathi-serve: Apache-2.0). ### Where can I find alternatives to ai-serving or sarathi-serve? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [sarathi-serve alternatives](/tools/microsoft-sarathi-serve/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [sarathi-serve markdown twin](/tools/microsoft-sarathi-serve/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-microsoft-sarathi-serve.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or sarathi-serve? ai-serving: Slowing. sarathi-serve: 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 ai-serving and sarathi-serve? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [sarathi-serve trust report](/tools/microsoft-sarathi-serve/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/_