--- title: "ai-serving vs runanywhere-sdks" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-runanywhereai-runanywhere-sdks" tools: ["autodeployai-ai-serving", "runanywhereai-runanywhere-sdks"] --- # ai-serving vs runanywhere-sdks *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 runanywhere-sdks if runAnywhere SDKs enable efficient cross-platform deployment of various AI models on local devices with support for specific hardware optimizations. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [runanywhere-sdks](https://www.runanywhere.ai) has 10k stars, 380 forks, and 136 open issues, last pushed Sep 19, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [runanywhere-sdks's repository](https://github.com/RunanywhereAI/runanywhere-sdks). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [runanywhere-sdks](/tools/runanywhereai-runanywhere-sdks.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | Production ready toolkit to run AI locally | | Stars | 166 | 10,297 | | Forks | 31 | 380 | | Open issues | 3 | 136 | | Language | Scala | C++ | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | RunAnywhere SDKs enable efficient cross-platform deployment of various AI models on local devices with support for specific hardware optimizations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache 2.0 with additional terms for commercial use | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [runanywhere-sdks](/tools/runanywhereai-runanywhere-sdks.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 208d | 0d | | Open issues (now) | 3 | 136 | | Stars delta | 0 (30d) | -3 (30d) | | Open issues delta | 0 (30d) | +120 (30d) | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/runanywhereai-runanywhere-sdks/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: runanywhere-sdks - **Requirements:** Min 2 GB RAM; Hexagon NPU for Snapdragon 8 Elite class or newer - **Adopt for:** RunAnywhere SDKs enable efficient cross-platform deployment of various AI models on local devices with support for specific hardware optimizations. - **License detail:** Apache 2.0 with additional terms for commercial use ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; runanywhere-sdks is C++. - License: ai-serving is Apache-2.0, runanywhere-sdks 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 runanywhere-sdks if… - runanywhere-sdks is primarily C++; ai-serving is Scala. - License: runanywhere-sdks is Other, ai-serving is Apache-2.0. - Requirements: Min 2 GB RAM; Hexagon NPU for Snapdragon 8 Elite class or newer. - Tags unique to runanywhere-sdks: android, cpp, diffusion-models, edge. - When deploying AI models that need to run locally across multiple platforms including Web, iOS, macOS, Android, React Native, and Flutter ## 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 runanywhere-sdks - If your deployment does not require support for older versions of Android below API level 24 or macOS earlier than version 14.0+ - In environments that do not meet the minimum hardware requirements, such as devices with less than 2 GB of RAM ## Common questions ### What is the difference between ai-serving and runanywhere-sdks? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. runanywhere-sdks: Production ready toolkit to run AI locally. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over runanywhere-sdks? Choose ai-serving over runanywhere-sdks when ai-serving is primarily Scala; runanywhere-sdks is C++; License: ai-serving is Apache-2.0, runanywhere-sdks 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 runanywhere-sdks over ai-serving? Choose runanywhere-sdks over ai-serving when runanywhere-sdks is primarily C++; ai-serving is Scala; License: runanywhere-sdks is Other, ai-serving is Apache-2.0; Requirements: Min 2 GB RAM; Hexagon NPU for Snapdragon 8 Elite class or newer; Tags unique to runanywhere-sdks: android, cpp, diffusion-models, edge; When deploying AI models that need to run locally across multiple platforms including Web, iOS, macOS, Android, React Native, and Flutter. ### 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 runanywhere-sdks? If your deployment does not require support for older versions of Android below API level 24 or macOS earlier than version 14.0+ In environments that do not meet the minimum hardware requirements, such as devices with less than 2 GB of RAM ### Is ai-serving or runanywhere-sdks more popular on GitHub? runanywhere-sdks has more GitHub stars (10,297 vs 166). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and runanywhere-sdks open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, runanywhere-sdks: Other). ### Where can I find alternatives to ai-serving or runanywhere-sdks? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [runanywhere-sdks alternatives](/tools/runanywhereai-runanywhere-sdks/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [runanywhere-sdks markdown twin](/tools/runanywhereai-runanywhere-sdks/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-runanywhereai-runanywhere-sdks.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or runanywhere-sdks? ai-serving: Slowing. runanywhere-sdks: 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 runanywhere-sdks? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [runanywhere-sdks trust report](/tools/runanywhereai-runanywhere-sdks/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/_