vLLM
The serving engine
PagedAttention and continuous batching set the open-source throughput bar for serving one generative model per server, behind OpenAI-compatible, Anthropic and gRPC fronts. Apache 2.0, Linux-first, at home on GPU fleets.
LM-Kit One versus vLLM
Tokens per second, or time to production.
vLLM squeezes maximum throughput out of one big model on GPUs. LM-Kit One turns the hardware you already have into the private backend applications call.
Start from what each one is for.
One optimizes a model. The other operates a backend. Most teams comparing them are really choosing which problem they have.
LM-Kit One
The application server
Document intelligence, extraction, cited search, and governed agents as ready endpoints, five API dialects on one engine, and an operations layer with identities, audit, and policies. Runs from a Windows workstation to a cluster.
Side by side, where it matters.
The rows that decide real deployments, not a feature checklist.
| Dimension | LM-Kit One | vLLM |
|---|---|---|
| Primary job | Application backend: documents, search, and agents as endpoints, plus model serving | Maximum-throughput serving of generative models |
| Serving shape | One server, many models and tasks, sized to the hardware | One base model per server, batched hard; LoRA adapters multiply variants |
| API surface | OpenAI, Anthropic, Ollama, MCP, native REST | OpenAI-compatible, Anthropic Messages, gRPC |
| Document intelligence | Full pipelines: OCR, Markdown, splitting, extraction with confidence, redaction, signatures, PDF/A | Serves vision models; document pipelines are yours to build |
| Search and grounded answers | Built-in service: ingestion, hybrid retrieval, reranking, answers citing document and page | Serves embedding and retrieval models; no retrieval service |
| Agents | Server-side agents with skills, governed tools, memory, MCP | Serves the model; the loop, tools, and policy live in your app |
| Governance and operations | Admin console, identities, SSO, per-key grants, audit, capability policies | A static API key flag; operations through external tooling |
| Platforms and hardware | Windows, Linux, macOS installers; CPU-first, CUDA, Vulkan, Metal | Linux-first; NVIDIA and AMD GPUs, CPU builds, TPUs and further accelerators |
| Scaling out | Horizontal scaling, any node serves any request, KEDA-ready | Battle-tested at fleet scale with Ray and Kubernetes deployments |
| Licensing | Free to build and evaluate; Professional for larger production use | Apache 2.0 open source |
vLLM moves fast and this table reflects our reading of its public documentation at publication; check their docs for the current state. Corrections are welcome through contact.
A fair way to decide.
One question settles most cases: is your bottleneck tokens per second on one model, or everything around the model?
Choose vLLM
One model, maximum load
You serve one large generative model to heavy concurrent traffic on Linux GPU infrastructure, and your team builds and operates the application stack around it.
Choose LM-Kit One
Many tasks, one backend
Applications need documents, cited answers, agents, and several model tasks behind one governed server, on hardware from a Windows workstation to a GPU cluster.
They also pair: nothing stops a vLLM fleet from serving your heaviest chat model while LM-Kit One runs the document, search, and agent workloads beside it.
LM-Kit One