--- title: "infinity vs ome" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/michaelfeil-infinity-vs-ome-projects-ome" tools: ["michaelfeil-infinity", "ome-projects-ome"] --- # infinity vs ome *GraphCanon updated Aug 25, 2026* ## Verdict Pick infinity if infinity is a high-throughput, low-latency serving engine that supports text-embeddings, reranking models, CLIP, CLAP, and ColPaLi, with GPU acceleration including ROCm and TensorRT; pick ome if oME is a Kubernetes operator tailored for LLM serving and management, focusing on tasks such as GPU scheduling and model lifecycle control, compatible with SGLang, vLLM, TensorRT-LLM, and Triton. [infinity](https://michaelfeil.github.io/infinity/) reports 2.9k GitHub stars, 196 forks, and 130 open issues, last pushed Mar 24, 2026. [ome](http://ome-projects.github.io/ome/) has 495 stars, 92 forks, and 127 open issues, last pushed Aug 25, 2026. Figures are from public GitHub metadata via [infinity's repository](https://github.com/michaelfeil/infinity) and [ome's repository](https://github.com/ome-projects/ome). | | [infinity](/tools/michaelfeil-infinity.md) | [ome](/tools/ome-projects-ome.md) | | --- | --- | --- | | Tagline | High-throughput, low-latency serving engine for text-embeddings and various models | Kubernetes operator for LLM serving and management | | Stars | 2,907 | 495 | | Forks | 196 | 92 | | Open issues | 130 | 127 | | Language | Python | Go | | Adopt for | Infinity is a high-throughput, low-latency serving engine that supports text-embeddings, reranking models, CLIP, CLAP, and ColPaLi, with GPU acceleration including ROCm and TensorRT. | OME is a Kubernetes operator tailored for LLM serving and management, focusing on tasks such as GPU scheduling and model lifecycle control, compatible with SGLang, vLLM, TensorRT-LLM, and Triton. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [infinity](/tools/michaelfeil-infinity.md) | [ome](/tools/ome-projects-ome.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 136d | 0d | | Open issues (now) | 130 | 127 | | Stars delta | Unknown | +13 (30d) | | Open issues delta | Unknown | +6 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/michaelfeil-infinity/trust.md) | [trust report](/tools/ome-projects-ome/trust.md) | ## Decision facts: infinity - **Adopt for:** Infinity is a high-throughput, low-latency serving engine that supports text-embeddings, reranking models, CLIP, CLAP, and ColPaLi, with GPU acceleration including ROCm and TensorRT. ## Decision facts: ome - **Adopt for:** OME is a Kubernetes operator tailored for LLM serving and management, focusing on tasks such as GPU scheduling and model lifecycle control, compatible with SGLang, vLLM, TensorRT-LLM, and Triton. ## Choose when ### Choose infinity if… - infinity is primarily Python; ome is Go. - License: infinity is MIT, ome is Apache-2.0. - Tags unique to infinity: clap, clip, colpali, docker-container. - When you need to serve embeddings and various models with high throughput and low latency. ### Choose ome if… - ome is primarily Go; infinity is Python. - License: ome is Apache-2.0, infinity is MIT. - Tags unique to ome: gpu-scheduling, kubernetes-operator, llm-inference, model-serving. - If you need robust GPU scheduling alongside LLM serving ## When NOT to use infinity - Avoid using Infinity if your setup does not require GPU acceleration since its specialized Docker images may introduce unnecessary complexity. - Do not use Infinity if you are working with models that are not supported by it (such as specific NLP models outside of embeddings and reranking). ## When NOT to use ome - In environments where a language other than Go for the operator's implementation is preferred - When your infrastructure does not support or utilize Kubernetes for orchestration purposes ## Common questions ### What is the difference between infinity and ome? infinity: High-throughput, low-latency serving engine for text-embeddings and various models. ome: Kubernetes operator for LLM serving and management. See the comparison table for live GitHub stats and shared categories. ### When should I choose infinity over ome? Choose infinity over ome when infinity is primarily Python; ome is Go; License: infinity is MIT, ome is Apache-2.0; Tags unique to infinity: clap, clip, colpali, docker-container; When you need to serve embeddings and various models with high throughput and low latency. ### When should I choose ome over infinity? Choose ome over infinity when ome is primarily Go; infinity is Python; License: ome is Apache-2.0, infinity is MIT; Tags unique to ome: gpu-scheduling, kubernetes-operator, llm-inference, model-serving; If you need robust GPU scheduling alongside LLM serving. ### When should I avoid infinity? Avoid using Infinity if your setup does not require GPU acceleration since its specialized Docker images may introduce unnecessary complexity. Do not use Infinity if you are working with models that are not supported by it (such as specific NLP models outside of embeddings and reranking). ### When should I avoid ome? In environments where a language other than Go for the operator's implementation is preferred When your infrastructure does not support or utilize Kubernetes for orchestration purposes ### Is infinity or ome more popular on GitHub? infinity has more GitHub stars (2,907 vs 495). Stars measure visibility, not whether either tool fits your constraints. ### Are infinity and ome open source? Yes - both are open-source projects on GitHub (infinity: MIT, ome: Apache-2.0). ### Where can I find alternatives to infinity or ome? GraphCanon lists graph-backed alternatives at [infinity alternatives](/tools/michaelfeil-infinity/alternatives) and [ome alternatives](/tools/ome-projects-ome/alternatives) ([infinity markdown twin](/tools/michaelfeil-infinity/alternatives.md), [ome markdown twin](/tools/ome-projects-ome/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/michaelfeil-infinity-vs-ome-projects-ome.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, infinity or ome? infinity: Slowing. ome: 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 infinity and ome? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [infinity trust report](/tools/michaelfeil-infinity/trust); [ome trust report](/tools/ome-projects-ome/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=michaelfeil-infinity`](/api/graphcanon/graph?tool=michaelfeil-infinity) - 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/_