--- title: "fastDeploy vs Server" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/notai-tech-fastdeploy-vs-rubixml-server" tools: ["notai-tech-fastdeploy", "rubixml-server"] --- # fastDeploy vs Server *GraphCanon updated Sep 20, 2026* ## Verdict Pick fastDeploy if fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines; pick Server if server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. [fastDeploy](https://github.com/notAI-tech/fastDeploy) reports 105 GitHub stars, 17 forks, and 0 open issues, last pushed Feb 10, 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 [fastDeploy's repository](https://github.com/notAI-tech/fastDeploy) and [Server's repository](https://github.com/RubixML/Server). | | [fastDeploy](/tools/notai-tech-fastdeploy.md) | [Server](/tools/rubixml-server.md) | | --- | --- | --- | | Tagline | Deploy DL/ML inference pipelines with minimal extra code. | Standalone inference server for Rubix ML estimators. | | Stars | 105 | 63 | | Forks | 17 | 13 | | Open issues | 0 | 1 | | Language | Python | PHP | | Adopt for | fastDeploy simplifies ML/DL model deployment focusing on minimal code for inference pipelines. | Server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [fastDeploy](/tools/notai-tech-fastdeploy.md) | [Server](/tools/rubixml-server.md) | | --- | --- | --- | | Days since push | 221d | 201d | | Open issues (now) | 0 | 1 | | Full report | [trust report](/tools/notai-tech-fastdeploy/trust.md) | [trust report](/tools/rubixml-server/trust.md) | ## 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. ## Decision facts: Server - **Adopt for:** Server is a standalone HTTP-based inference server for deploying Rubix ML estimators using PHP. ## Choose when ### Choose fastDeploy if… - fastDeploy is primarily Python; Server is PHP. - 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. ### Choose Server if… - Server is primarily PHP; fastDeploy is Python. - Tags unique to Server: api, inference-engine, infrastructure, json-api. - 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 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. ## 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 fastDeploy and Server? fastDeploy: Deploy DL/ML inference pipelines with minimal extra code.. Server: Standalone inference server for Rubix ML estimators.. See the comparison table for live GitHub stats and shared categories. ### When should I choose fastDeploy over Server? Choose fastDeploy over Server when fastDeploy is primarily Python; Server is PHP; 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 choose Server over fastDeploy? Choose Server over fastDeploy when Server is primarily PHP; fastDeploy is Python; Tags unique to Server: api, inference-engine, infrastructure, json-api; 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 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. ### 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 fastDeploy or Server more popular on GitHub? fastDeploy has more GitHub stars (105 vs 63). Stars measure visibility, not whether either tool fits your constraints. ### Are fastDeploy and Server open source? Yes - both are open-source projects on GitHub (fastDeploy: MIT, Server: MIT). ### Where can I find alternatives to fastDeploy or Server? GraphCanon lists graph-backed alternatives at [fastDeploy alternatives](/tools/notai-tech-fastdeploy/alternatives) and [Server alternatives](/tools/rubixml-server/alternatives) ([fastDeploy markdown twin](/tools/notai-tech-fastdeploy/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/notai-tech-fastdeploy-vs-rubixml-server.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, fastDeploy or Server? fastDeploy: 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 fastDeploy and Server? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [fastDeploy trust report](/tools/notai-tech-fastdeploy/trust); [Server trust report](/tools/rubixml-server/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=notai-tech-fastdeploy`](/api/graphcanon/graph?tool=notai-tech-fastdeploy) - 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/_