Ollama
The local model runner
Pull a model, chat, build against a clean local API. A curated library, an app, official SDKs, and the largest integration ecosystem in local AI, with metered cloud tiers when a model outgrows the machine.
LM-Kit One versus Ollama
The developer's runner, and the team's server.
Ollama made local models effortless on a developer's machine. LM-Kit One is what applications point at when the prototype has to become a governed backend.
Start from what each one is for.
Both run open models on your hardware. They were built for different moments in a project's life.
LM-Kit One
The private AI application server
Documents, extraction, cited search, and governed agents as ready endpoints, five API dialects including Ollama's own, and an operations layer with identities, audit, and policies for the whole team.
Side by side, where it matters.
The rows that decide real deployments, not a feature checklist.
| Dimension | LM-Kit One | Ollama |
|---|---|---|
| Primary job | Backend for applications and teams | Personal model runner: pull a model, chat, build against it |
| API surface | OpenAI, Anthropic, MCP, native REST, and Ollama's dialect, so Ollama clients connect unchanged | Its own REST API plus OpenAI-compatible endpoints |
| Document intelligence | Full pipelines: OCR, Markdown, splitting, extraction with confidence, redaction, signatures, PDF/A | Vision models and app-level file chat; no document pipeline |
| Search and grounded answers | Built-in service: ingestion, hybrid retrieval, reranking, answers citing document and page | Serves embedding models; retrieval is yours to build |
| Agents | Server-side agents with skills, governed tools, memory, MCP | Tool calling on the API; loops and policy live in your code |
| Governance and operations | Admin console, identities, SSO, per-key grants, audit, capability policies | No built-in authentication on the local server; cloud accounts are separate |
| Platforms and hardware | Windows, Linux, macOS; installers, desktop mode, Windows service; CPU-first, CUDA, Vulkan, Metal | macOS, Windows, Linux, Docker; efficient on CPUs and common GPUs |
| Beyond one machine | Horizontal scaling, any node serves any request, KEDA-ready | Single-node by design; the cloud tiers offload models to Ollama's servers |
| Cost model | Free to build and evaluate; Professional for larger production use; no per-token fees | Free unlimited local use; metered cloud tiers for hosted models |
Ollama evolves quickly and this table reflects our reading at publication; check their site for the current lineup. Corrections are welcome through contact.
A fair way to decide.
One question settles most cases: is the customer a developer at a machine, or an application serving a team?
Choose Ollama
Developers exploring models
You want the fastest path to running and building against local models on your own machine, with the broadest ecosystem of clients and integrations.
Choose LM-Kit One
Applications in production
Software needs a private AI backend: document workloads, cited answers, governed agents, audit, identities, and a path to horizontal scale without per-token fees.
They also pair naturally: keep Ollama on the laptop and point production at LM-Kit One. Since One speaks the Ollama dialect, the switch is a host name; the migration guide shows it step by step.
LM-Kit One