--- title: "ai-serving vs Server" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-rubixml-server" tools: ["autodeployai-ai-serving", "rubixml-server"] --- # ai-serving vs Server *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 Server if server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [Server](https://rubixml.github.io/ML) has 63 stars, 13 forks, and 1 open issues, last pushed Mar 3, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [Server's repository](https://github.com/RubixML/Server). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [Server](/tools/rubixml-server.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | Standalone inference server for Rubix ML estimators. | | Stars | 166 | 63 | | Forks | 31 | 13 | | Open issues | 3 | 1 | | Language | Scala | PHP | | Adopt for | Ai-Serving is an inference server supporting PMML and ONNX formats via HTTP or gRPC endpoints, easily deployable with Docker. | Server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [Server](/tools/rubixml-server.md) | | --- | --- | --- | | Days since push | 208d | 201d | | Open issues (now) | 3 | 1 | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/rubixml-server/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: Server - **Adopt for:** Server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; Server is PHP. - License: ai-serving is Apache-2.0, Server is MIT. - 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 Server if… - Server is primarily PHP; ai-serving is Scala. - License: Server is MIT, ai-serving is Apache-2.0. - Tags unique to Server: api, http-server, inference-engine, infrastructure. - When you are working with machine learning models trained in the Rubix ML framework and need to deploy them via a PHP-based infrastructure. ## 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 Server - Avoid using if your primary technology stack is not based on PHP, as it would necessitate integration with a non-native language environment, increasing complexity. - Do not use this tool for large-scale deployments requiring high throughput and low latency typical of more robust languages like Python or Rust. ## Common questions ### What is the difference between ai-serving and Server? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. Server: Standalone inference server for Rubix ML estimators.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over Server? Choose ai-serving over Server when ai-serving is primarily Scala; Server is PHP; License: ai-serving is Apache-2.0, Server is MIT; 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 Server over ai-serving? Choose Server over ai-serving when Server is primarily PHP; ai-serving is Scala; License: Server is MIT, ai-serving is Apache-2.0; Tags unique to Server: api, http-server, inference-engine, infrastructure; When you are working with machine learning models trained in the Rubix ML framework and need to deploy them via a PHP-based infrastructure. ### 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 Server? Avoid using if your primary technology stack is not based on PHP, as it would necessitate integration with a non-native language environment, increasing complexity. Do not use this tool for large-scale deployments requiring high throughput and low latency typical of more robust languages like Python or Rust. ### Is ai-serving or Server more popular on GitHub? ai-serving has more GitHub stars (166 vs 63). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and Server open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, Server: MIT). ### Where can I find alternatives to ai-serving or Server? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [Server alternatives](/tools/rubixml-server/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [Server markdown twin](/tools/rubixml-server/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-rubixml-server.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or Server? ai-serving: Slowing. Server: 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 Server? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [Server trust report](/tools/rubixml-server/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/_