--- title: "ai-serving vs fastDeploy" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/autodeployai-ai-serving-vs-notai-tech-fastdeploy" tools: ["autodeployai-ai-serving", "notai-tech-fastdeploy"] --- # ai-serving vs fastDeploy *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 fastDeploy if fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. [ai-serving](https://github.com/autodeployai/ai-serving) reports 166 GitHub stars, 31 forks, and 3 open issues, last pushed Feb 24, 2026. [fastDeploy](https://github.com/notAI-tech/fastDeploy) has 105 stars, 17 forks, and 0 open issues, last pushed Feb 10, 2026. Figures are from public GitHub metadata via [ai-serving's repository](https://github.com/autodeployai/ai-serving) and [fastDeploy's repository](https://github.com/notAI-tech/fastDeploy). | | [ai-serving](/tools/autodeployai-ai-serving.md) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Tagline | Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints | Deploy DL/ML inference pipelines with minimal extra code. | | Stars | 166 | 105 | | Forks | 31 | 17 | | Open issues | 3 | 0 | | 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. | fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. | | 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) | [fastDeploy](/tools/notai-tech-fastdeploy.md) | | --- | --- | --- | | Days since push | 208d | 221d | | Open issues (now) | 3 | 0 | | Full report | [trust report](/tools/autodeployai-ai-serving/trust.md) | [trust report](/tools/notai-tech-fastdeploy/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: fastDeploy - **Pricing:** freemium - - - **Requirements:** - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory. - **Adopt for:** fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. ## Choose when ### Choose ai-serving if… - ai-serving is primarily Scala; fastDeploy is Python. - License: ai-serving is Apache-2.0, fastDeploy 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 fastDeploy if… - fastDeploy is primarily Python; ai-serving is Scala. - License: fastDeploy is MIT, ai-serving is Apache-2.0. - Pricing: -. - Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.. - Tags unique to fastDeploy: deep-learning, docker, falcon, gevent. - When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding. ## 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 fastDeploy - Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. - Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations. ## Common questions ### What is the difference between ai-serving and fastDeploy? ai-serving: Serving AI/ML models in open standard formats PMML and ONNX with HTTP and gRPC endpoints. fastDeploy: Deploy DL/ML inference pipelines with minimal extra code.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ai-serving over fastDeploy? Choose ai-serving over fastDeploy when ai-serving is primarily Scala; fastDeploy is Python; License: ai-serving is Apache-2.0, fastDeploy 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 fastDeploy over ai-serving? Choose fastDeploy over ai-serving when fastDeploy is primarily Python; ai-serving is Scala; License: fastDeploy is MIT, ai-serving is Apache-2.0; Pricing: -; Requirements: - Python is required for running fastDeploy.; - Docker installation is suggested but not mandatory.; Tags unique to fastDeploy: deep-learning, docker, falcon, gevent; When you aim to streamline the deployment of TensorFlow Serving, TorchServe, and Triton Inference Server models without extensive coding. ### 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 fastDeploy? Avoid if you are looking for a solution that supports real-time interactive deployments requiring advanced websocket handling beyond fastDeploy's basic capability. Not recommended when the project requires heavy customization of deployment scripts, as it emphasizes minimal coding and may restrict flexibility in pipeline configurations. ### Is ai-serving or fastDeploy more popular on GitHub? ai-serving has more GitHub stars (166 vs 105). Stars measure visibility, not whether either tool fits your constraints. ### Are ai-serving and fastDeploy open source? Yes - both are open-source projects on GitHub (ai-serving: Apache-2.0, fastDeploy: MIT). ### Where can I find alternatives to ai-serving or fastDeploy? GraphCanon lists graph-backed alternatives at [ai-serving alternatives](/tools/autodeployai-ai-serving/alternatives) and [fastDeploy alternatives](/tools/notai-tech-fastdeploy/alternatives) ([ai-serving markdown twin](/tools/autodeployai-ai-serving/alternatives.md), [fastDeploy markdown twin](/tools/notai-tech-fastdeploy/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-notai-tech-fastdeploy.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ai-serving or fastDeploy? ai-serving: Slowing. fastDeploy: 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 fastDeploy? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ai-serving trust report](/tools/autodeployai-ai-serving/trust); [fastDeploy trust report](/tools/notai-tech-fastdeploy/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/_