Short definition
The Ollama REST API is an HTTP interface exposed locally at 127.0.0.1:11434 by the Ollama runtime. It provides JSON endpoints for running model inference, managing downloaded models, and controlling multi-turn chat sessions. Engineers use it to integrate locally hosted language models into applications without sending data to external providers.
Extended definition
Ollama is a runtime that runs open-weight large language models on local hardware, including consumer-grade GPUs and Apple Silicon. The Ollama REST API it exposes is the primary way application code interacts with those models. Every inference request, model download, and session management action goes through this HTTP interface using standard JSON payloads.
The API covers four core operations. Single-turn completions go through POST /api/generate, where you send a prompt and receive a response. Multi-turn conversations use POST /api/chat, which accepts a messages array in the same format as the OpenAI Chat Completions API. Model inventory is available via GET /api/tags, and POST /api/pull downloads a named model from the Ollama registry.
The OpenAI-compatible message format on /api/chat is practically significant. Many client libraries, LangChain integrations, and internal tooling written for OpenAI can be pointed at localhost:11434 with minimal changes. This makes Ollama a drop-in local backend for development and for production systems where data must stay on-premises. The binding address is configurable via the OLLAMA_HOST environment variable, so the API can be exposed to other hosts on a private network when needed.
For engineering teams building AI-enabled products, Ollama REST API removes the dependency on third-party inference providers during development, reduces cost at scale, and gives teams full control over which models run and how they are configured. It is commonly used in internal tooling, document processing pipelines, code analysis agents, and data-sensitive enterprise applications.
Deep technical explanation
The Ollama server process starts a lightweight HTTP server on startup. By default it binds to 127.0.0.1:11434, restricting access to the local machine. Setting OLLAMA_HOST to 0.0.0.0:11434 or a specific network interface makes the API reachable from other machines, which is common in containerized or multi-host deployments.
Key endpoints
POST /api/generate accepts a JSON body with at minimum a model name and a prompt string. Optional fields include system (a system prompt), template (a custom prompt template), options (model parameters like temperature and top_p), and stream (a boolean controlling whether the response is streamed token-by-token or returned in a single JSON object). Streaming is enabled by default and returns newline-delimited JSON objects.
POST /api/chat accepts a messages array where each object has a role field (system, user, or assistant) and a content field. This mirrors the OpenAI Chat Completions format. The endpoint maintains no server-side session state; the caller is responsible for accumulating and re-sending message history on each turn. Responses include a message object with the assistant reply plus metadata such as token counts and inference duration.
GET /api/tags returns a JSON array of locally available models with their names, sizes, and modification timestamps. POST /api/pull streams download progress as newline-delimited JSON, reporting layer-by-layer download status. POST /api/delete removes a model from local storage. POST /api/show returns detailed metadata about a specific model including its Modelfile, parameters, and template.
Streaming and response handling
When stream is true (the default), the client receives a series of JSON objects over a persistent HTTP connection. Each object contains a partial response token and a done flag. The final object in the stream sets done to true and includes full inference statistics. Clients that do not handle streaming must set stream to false, which buffers the entire response before returning it as a single JSON object. This matters for long outputs where streaming provides better perceived latency.
Common failure modes
The most frequent issue is requesting a model that has not been pulled. The API returns a 404 with an error message; callers should check GET /api/tags before inference or handle the error and trigger a pull. Memory exhaustion is another failure mode: if the requested model exceeds available VRAM or RAM, the server will fail to load it. Context length overflow occurs when the messages array grows beyond the model’s context window, causing the model to truncate or error. Applications must track token counts and prune history when approaching the limit.
When OLLAMA_HOST exposes the API on a network interface, there is no built-in authentication layer. Teams running Ollama in shared environments should place it behind a reverse proxy with access controls, or use network-level restrictions to limit which clients can reach port 11434.
Practical examples
Internal document summarization tool: A legal tech team needed to summarize contracts without sending text to external APIs. They deployed Ollama on a workstation with a GPU, pointed their Python backend at the /api/chat endpoint using an OpenAI-compatible client library, and processed thousands of documents locally. Data never left the office network.
Development environment for an AI product: Engineers building a production system backed by a commercial LLM provider used Ollama during local development. By setting the base URL to localhost:11434, they ran the same application code against a local model, eliminating API costs and rate limits during iteration.
Code review agent: A DevOps team built a CI step that posted diffs to /api/generate with a code review prompt. The agent returned structured feedback as JSON. Because the API was local to the CI runner, latency was low and there were no per-token charges.
Multi-model routing: An AI orchestration layer used GET /api/tags to discover available models at startup and routed inference requests to the most appropriate model based on task type. Lightweight classification tasks went to a smaller model; longer generation tasks went to a larger one.
Why it matters
- Local inference eliminates data transmission to third-party providers, which is required in regulated industries and privacy-sensitive applications.
- OpenAI-compatible message format on
/api/chatmeans existing integrations and client libraries can switch to a local backend with minimal code changes. - No per-token pricing makes the Ollama REST API practical for high-volume internal tooling where commercial API costs would be prohibitive.
- The OLLAMA_HOST environment variable makes the binding address configurable, supporting single-machine setups, Docker containers, and multi-host private networks without code changes.
- Streaming responses over HTTP reduce time-to-first-token perceived latency for interactive applications, matching the behavior users expect from production AI products.
- Model management endpoints allow fully automated workflows: pull, query, use, and delete models programmatically without manual intervention.