--- title: "ai-serving vs serve" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-jina-ai-serve" tools: ["autodeployai-ai-serving", "jina-ai-serve"] --- # ai-serving vs serve *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 serve if serve is a Python-based framework for building multimodal AI applications using a cloud-native stack, supporting deep learning, microservices, and orchestration. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [serve](https://jina.ai/serve) has 22k stars, 2.2k forks, and 26 open issues, last pushed Mar 24, 2025. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [serve's repository](https://github.com/jina-ai/serve). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [serve](/tools/jina-ai-serve.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | Build multimodal AI applications with cloud-native stack | | Stars | 166 | 21,861 | | Forks | 31 | 2,240 | | Open issues | 3 | 26 | | 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. | Serve is a Python-based framework for building multimodal AI applications using a cloud-native stack, supporting deep learning, microservices, and orchestration. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Serve is available under the Apache-2.0 license, allowing for free use, modification, and distribution. | | Categories | Inference & Serving | Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ai-serving](/tools/autodeployai-ai-serving.md) | [serve](/tools/jina-ai-serve.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 208d | 542d | | Open issues (now) | 3 | 26 | | Stars delta | 0 (30d) | -2 (30d) | | Open issues delta | 0 (30d) | -1 (30d) | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/jina-ai-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: serve - **Pricing:** freemium - Serve is free to use, but additional services or support might be available for a fee. - **Requirements:** Min 4 GB RAM; Requires Docker; Installation requires Python and pip.; Supports Apple Silicon and Windows installations. - **Adopt for:** Serve is a Python-based framework for building multimodal AI applications using a cloud-native stack, supporting deep learning, microservices, and orchestration. - **License detail:** Serve is available under the Apache-2.0 license, allowing for free use, modification, and distribution. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; serve is Python. - Tags unique to ai-serving: ai-serving, inference-server, onnx, pmml-deployment. - When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### Choose serve if… - serve is primarily Python; ai-serving is Scala. - Pricing: Serve is free to use, but additional services or support might be available for a fee.. - Requirements: Min 4 GB RAM; Requires Docker; Installation requires Python and pip.; Supports Apple Silicon and Windows installations.. - Tags unique to serve: cloud-native, cncf, deep-learning, docker. - Also covers Evaluation & Observability, Model Training. - Use Serve when you need to build applications that integrate multiple types of data, such as text, images, and audio, into a single AI application. ## 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 serve - Avoid Serve if your project does not require multimodal capabilities and you are looking for a simpler serving solution. - Do not use Serve if you are not comfortable with cloud-native technologies and prefer a more traditional deployment approach. ## Common questions ### What is the difference between ai-serving and serve? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. serve: Build multimodal AI applications with cloud-native stack. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over serve? Choose ai-serving over serve when ai-serving is primarily Scala; serve is Python; Tags unique to ai-serving: ai-serving, inference-server, onnx, pmml-deployment; When you need to serve models in both PMML and ONNX formats without manual configuration changes between formats. ### When should I choose serve over ai-serving? Choose serve over ai-serving when serve is primarily Python; ai-serving is Scala; Pricing: Serve is free to use, but additional services or support might be available for a fee.; Requirements: Min 4 GB RAM; Requires Docker; Installation requires Python and pip.; Supports Apple Silicon and Windows installations.; Tags unique to serve: cloud-native, cncf, deep-learning, docker; Also covers Evaluation & Observability, Model Training; Use Serve when you need to build applications that integrate multiple types of data, such as text, images, and audio, into a single AI application. ### 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 serve? Avoid Serve if your project does not require multimodal capabilities and you are looking for a simpler serving solution. Do not use Serve if you are not comfortable with cloud-native technologies and prefer a more traditional deployment approach. ### Is ai-serving or serve more popular on GitHub? serve has more GitHub stars (21,861 vs 166). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and serve open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, serve: Apache-2.0). ### Where can I find alternatives to ai-serving or serve? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [serve alternatives](/tools/jina-ai-serve/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [serve markdown twin](/tools/jina-ai-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-jina-ai-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 serve? ai-serving: Slowing. serve: Dormant. 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 serve? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [serve trust report](/tools/jina-ai-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/_