Baseten vs vLLM vs oMLX
Side by side, from each listing’s own data. A row where every column says the same is shown once, dimmed.
 Basetenby BasetenRemove Baseten from this comparison |  vLLMby vLLM ProjectRemove vLLM from this comparison |  oMLXby Jun KimRemove oMLX from this comparison | |
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| Sets it apart |
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| What it is | Deploys models as production inference APIs with dedicated GPU deployments, Truss packaging, and OpenAI-compatible endpoints for agent workloads. | High-throughput LLM inference server with OpenAI-compatible API. Serves Llama, Mistral, Qwen, and 30+ model families with PagedAttention. pip install vllm. | Serves local LLM, vision, embedding and reranker models on Apple Silicon Macs through OpenAI- and Anthropic-compatible APIs, with continuous batching and an SSD-backed KV cache. |
| Provider | Baseten | vLLM Project | Jun Kim |
| Official vendor | Baseten | Not an official listing | Not an official listing |
| Verified | YesSame for all | ||
| Access | API, CLISame for all | ||
| Auth | API Key | None | API Key |
| CLI install | In its docsSame for all | ||
| API base URL | In its docsSame for all | ||
| Skills | Model Deployment, OpenAI-Compatible Inference, Chains | OpenAI-Compatible Serving, PagedAttention Engine, Multi-Model Support | Tiered KV Cache, OpenAI and Anthropic APIs, Multi-Model Serving, Agent Integrations |
| Streaming | YesSame for all | ||
| Push notifications | NoSame for all | ||
| Categories | Infrastructure & OpsSame for all | ||
| Tags | inference, model-serving, truss, gpu-deployment, openai-compatible, chains, autoscaling | llm-serving, inference-server, openai-compatible, paged-attention, high-throughput, self-hosted, gpu-inference | apple-silicon, mlx, local-inference, openai-compatible, anthropic-compatible, kv-cache, continuous-batching, macos |
| Listed since | Aug 15, 2026 | May 13, 2026 | Oct 4, 2026 |
| Use it now | |||
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