{"slug": "mastering-ollama-ai-endpoints-how-to-use-each-one-correctly", "title": "Mastering Ollama AI endpoints: How to use each one correctly", "summary": "Ollama provides a REST API with 14 endpoints for running large language models locally. The API includes endpoints for text generation, chat, embeddings, model management, and OpenAI compatibility. Developers can use these endpoints to integrate AI into applications with benefits like privacy, lower latency, and reduced costs.", "body_md": "**Learn how to use all 14 Ollama API endpoints with real-world examples, best practices, and production-ready insights.**\n\nArtificial Intelligence is rapidly moving from cloud-only environments to local deployments. Developers increasingly want privacy, lower latency, reduced costs, and complete control over their AI infrastructure.\n\nThis is where **Ollama** shines.\n\nOllama allows you to run powerful Large Language Models (LLMs) such as Llama, Gemma, Mistral, Qwen, DeepSeek, and many others directly on your local machine or server. Beyond running models, Ollama provides a robust REST API that enables developers to integrate AI capabilities into applications, automation workflows, chatbots, coding assistants, search engines, and enterprise systems.\n\nIn this guide, you'll learn all **14 Ollama API endpoints**, understand when to use each one, and see practical examples that go beyond the official documentation.\n\nOllama is a platform designed to simplify the deployment and execution of large language models locally.\n\nSome advantages include:\n\nBy default, Ollama runs on:\n\n```\nhttp://localhost:11434\nPOST /api/generate\n```\n\nGenerates text from a single prompt.\n\n```\ncurl http://localhost:11434/api/generate \\\n-d '{\n  \"model\":\"llama3\",\n  \"prompt\":\"Explain quantum computing in simple terms.\"\n}'\n```\n\nUse `/api/generate`\n\nfor one-shot tasks where conversation history is unnecessary. It consumes fewer resources than chat endpoints.\n\n```\nPOST /api/chat\n```\n\nMaintains conversational context.\n\n```\ncurl http://localhost:11434/api/chat \\\n-d '{\n  \"model\":\"llama3\",\n  \"messages\":[\n    {\n      \"role\":\"user\",\n      \"content\":\"Create a Node.js REST API.\"\n    }\n  ]\n}'\n```\n\nFor production chat applications, always store conversation history externally rather than relying solely on the model context window.\n\n```\nPOST /api/embeddings\n```\n\nConverts text into numerical vectors.\n\n```\ncurl http://localhost:11434/api/embeddings \\\n-d '{\n  \"model\":\"nomic-embed-text\",\n  \"prompt\":\"How does machine learning work?\"\n}'\n```\n\nEmbeddings are the foundation of modern Retrieval-Augmented Generation (RAG) systems.\n\n```\nGET /api/tags\n```\n\nDisplays all downloaded models.\n\n```\ncurl http://localhost:11434/api/tags\n```\n\nUseful for:\n\n```\nPOST /api/show\n```\n\nReturns detailed model information.\n\n```\ncurl http://localhost:11434/api/show \\\n-d '{\n  \"name\":\"llama3\"\n}'\n```\n\nUse this endpoint to automatically validate model compatibility before deployment.\n\n```\nPOST /api/pull\n```\n\nDownloads a model from the Ollama registry.\n\n```\ncurl http://localhost:11434/api/pull \\\n-d '{\n  \"name\":\"deepseek-r1\"\n}'\n```\n\nWhen deploying a new server:\n\n```\nstartup.sh\n```\n\ncan automatically pull required models before application startup.\n\n```\nPOST /api/push\n```\n\nPublishes a model to a registry.\n\n```\ncurl http://localhost:11434/api/push \\\n-d '{\n  \"name\":\"mycompany-assistant\"\n}'\nPOST /api/create\n```\n\nCreates custom models from a Modelfile.\n\n```\ncurl http://localhost:11434/api/create \\\n-d '{\n  \"name\":\"seo-expert\",\n  \"modelfile\":\"FROM llama3\"\n}'\n```\n\nYou can:\n\n```\nPOST /api/copy\n```\n\nDuplicates an existing model.\n\n```\ncurl http://localhost:11434/api/copy \\\n-d '{\n  \"source\":\"llama3\",\n  \"destination\":\"llama3-backup\"\n}'\nDELETE /api/delete\n```\n\nRemoves a model from local storage.\n\n```\ncurl -X DELETE http://localhost:11434/api/delete \\\n-d '{\n  \"name\":\"old-model\"\n}'\n```\n\nAlways verify model usage before deleting in shared environments.\n\n```\nGET /api/ps\n```\n\nShows models currently loaded in memory.\n\n```\ncurl http://localhost:11434/api/ps\n```\n\nHelpful for:\n\nLarge models may occupy several gigabytes of RAM even when idle.\n\n```\nGET /api/version\n```\n\nReturns the installed Ollama version.\n\n```\ncurl http://localhost:11434/api/version\n```\n\nUseful for:\n\n```\nPOST /v1/chat/completions\n```\n\nProvides OpenAI API compatibility.\n\n```\ncurl http://localhost:11434/v1/chat/completions \\\n-d '{\n  \"model\":\"llama3\",\n  \"messages\":[\n    {\n      \"role\":\"user\",\n      \"content\":\"Write a Python function for sorting.\"\n    }\n  ]\n}'\n```\n\nApplications built for OpenAI can often switch to Ollama with minimal code changes.\n\n```\nGET /v1/models\n```\n\nLists available models using the OpenAI format.\n\n```\ncurl http://localhost:11434/v1/models\n```\n\nMany developers stop at generating text, but modern AI applications usually combine several endpoints:\n\n```\n/api/chat\n/api/show\n/api/ps\n/api/embeddings\n/api/chat\n/api/pull\n/api/show\n/api/chat\n/api/delete\n/v1/chat/completions\n/v1/models\n```\n\nCombining endpoints intelligently is what separates a proof of concept from a production-ready AI solution.\n\nBefore exposing Ollama publicly:\n\nNever expose an unrestricted Ollama instance directly to the internet.\n\nTo achieve better performance:\n\nThese practices can significantly reduce latency and improve throughput.\n\nOllama is much more than a tool for running local language models, it is a complete AI platform with endpoints covering text generation, conversational AI, embeddings, model lifecycle management, monitoring, and OpenAI compatibility.\n\nUnderstanding all 14 endpoints allows developers to build sophisticated AI solutions without relying entirely on external providers. Whether you're creating a chatbot, a RAG-powered knowledge base, a coding assistant, or an enterprise AI platform, Ollama provides the building blocks needed to deploy AI locally, securely, and efficiently.\n\nAs organizations increasingly prioritize privacy, cost control, and infrastructure ownership, mastering the Ollama API is becoming a valuable skill for modern software engineers, DevOps professionals, and AI developers.", "url": "https://wpnews.pro/news/mastering-ollama-ai-endpoints-how-to-use-each-one-correctly", "canonical_source": "https://dev.to/nube_colectiva_nc/mastering-on-device-ai-orchestration-a-deep-dive-into-ollamas-local-api-3abk", "published_at": "2026-06-22 06:54:02+00:00", "updated_at": "2026-06-22 07:10:27.228579+00:00", "lang": "en", "topics": ["large-language-models", "developer-tools", "ai-infrastructure"], "entities": ["Ollama", "Llama", "Gemma", "Mistral", "Qwen", "DeepSeek"], "alternates": {"html": "https://wpnews.pro/news/mastering-ollama-ai-endpoints-how-to-use-each-one-correctly", "markdown": "https://wpnews.pro/news/mastering-ollama-ai-endpoints-how-to-use-each-one-correctly.md", "text": "https://wpnews.pro/news/mastering-ollama-ai-endpoints-how-to-use-each-one-correctly.txt", "jsonld": "https://wpnews.pro/news/mastering-ollama-ai-endpoints-how-to-use-each-one-correctly.jsonld"}}