# 29 model providers, inference and fine-tuning URL: https://www.getdynamiq.ai/product/models > Use 29 model providers from one SDK, the most-used models through one OpenAI-compatible endpoint, and open models you host and fine-tune. Use 29 model providers from the SDK, call the most-used models behind one endpoint and one credential, or host and fine-tune open models yourself. [Start free](https://app.getdynamiq.ai/signup) · [Talk to the team](https://www.getdynamiq.ai/book-a-demo) _Illustration of the AI Gateway routing requests: a primary hosted model, a fallback, a self-hosted open model for traffic that contains personal data, and a regional deployment._ In short The AI Gateway is one OpenAI-compatible endpoint for the most-used models, resolving credentials server-side so your code holds one Dynamiq key instead of one per provider. Swap models by changing a model slug, with usage metered per organization. The SDK supports 29 model providers through one LLM node interface. For models you want to run yourself, deploy open models such as Llama, Gemma, Mistral and Qwen on managed inference, and fine-tune them with LoRA adapters served dynamically over a shared base model. ## What Models and AI Gateway does One endpoint, many providers The router speaks the OpenAI chat completions protocol; change the model slug and the request goes to a different provider, unchanged otherwise. One credential A single Dynamiq Access Key replaces a provider key per service, and revoking it is one action in Settings. 29 model providers From OpenAI and Anthropic to self-hosted Ollama, every provider node shares the same parameters, streaming and tool-calling shape. Prompt caching by default Calls to Anthropic models and Bedrock reuse cached prompt prefixes by default, so long system prompts and documents cost less and answer faster. Open models you host Deploy Llama, Gemma, Mistral, Qwen and other open models on managed inference behind an OpenAI-compatible endpoint, without managing GPUs yourself. Fine-tuning with LoRA adapters Serve fine-tuned adapters dynamically over one shared base model instead of a separate deployment per adapter. ## One endpoint, one credential Point any OpenAI SDK client at the gateway's base URL with a Dynamiq Access Key instead of a provider key, and everything else about your integration stays the same: same request shape, same streaming format, same response. The model field is a Dynamiq router slug; the gateway resolves which provider serves it, authenticates with a Dynamiq-managed connection on the server side, and relays the request, so your client never holds a provider credential. Usage is metered against your organization's plan in one place, and switching models is changing one string. - Streaming returns server-sent events of chat.completion.chunk objects, exactly what an OpenAI client expects - The router covers chat completions only; call a provider directly for embeddings, audio or image endpoints - Extra request fields pass through to the upstream provider, so provider-specific parameters still work _Illustration of the AI Gateway: an OpenAI SDK client pointed at the Dynamiq router with a Dynamiq access key, calling a model by its router name._ ## Providers behind one node Every LLM is a node that subclasses the same base class, so swapping OpenAI for Anthropic, Gemini or a self-hosted Ollama model changes the class, the model name and the connection; the prompt, parameters, streaming, tools and output shape stay identical. Dynamiq supports 29 model providers, from the major hosted labs to inference platforms such as Groq, Fireworks, Together and Cerebras, plus a generic OpenAI-compatible connection for anything else. - A fallback config runs a secondary LLM automatically on a rate limit or connection error - Vision and PDF input are reported per model, with an override for a model the registry does not know - The same node classes power the AI Gateway underneath, so platform and SDK stay in sync _Illustration of choosing the provider behind an LLM node from a grid of providers, with the selected model highlighted._ ## Open models and fine-tuning Deploy an open model such as Llama 3, Gemma, Mistral or Qwen on a vLLM runtime behind its own hostname and an OpenAI-compatible endpoint, with replica autoscaling between a minimum and maximum you set. Fine-tune LoRA adapters on top of a base model and serve them from one shared deployment that loads an adapter by name, instead of standing up a separate deployment for every fine-tune. Point a request's model field at an adapter alias to route to that specific adapter, or leave it on the base model. - Embedding and speech-to-text models deploy the same way, behind the matching OpenAI-compatible API - Creating an inference deployment also creates a system-managed connection, so workflow nodes call it with no extra setup - Pods and their live logs are visible from the deployment page while a large model downloads and loads _Illustration of a fine-tuning job: a LoRA adapter trained on approved replies, its training and validation loss falling and leveling off together, ready to deploy on its base model._ ## Model providers - OpenAI - Anthropic - Gemini - AWS Bedrock - Azure AI - AI21 - Anyscale - Cerebras - Cohere - Databricks - DeepInfra - DeepSeek - Fireworks AI - Groq - Hugging Face - Mistral - Nvidia NIM - Ollama - OpenRouter - Perplexity - Replicate - SambaNova - Together AI - Vertex AI - watsonx - xAI - Custom (OpenAI-compatible) ## Where teams use it - [Sovereign AI · Run the whole agent platform, the models and every trace inside your own cloud account or data center, operated by your team.](https://www.getdynamiq.ai/use-cases/sovereign-ai) - [Document review and underwriting · Read loan files, underwriting submissions and contracts, check them against your rules, and flag exceptions for a reviewer instead of a rubber stamp.](https://www.getdynamiq.ai/use-cases/document-review) - [Research and knowledge assistants · Answer questions across your documents and systems, grounded in retrieval that respects who can see what, with citations back to the source.](https://www.getdynamiq.ai/use-cases/research-knowledge) ## Works with - [Agent Builder · Design an agent on a visual canvas or in the open-source Python SDK. Both compile to the same engine, so what you build ships either way.](https://www.getdynamiq.ai/product/agents) - [Knowledge · A knowledge base converts your documents into searchable context: ingestion, chunking, embedding and storage, managed for you or pointed at your own vector store.](https://www.getdynamiq.ai/product/knowledge-rag) - [Observability · Every run is traced and replayable, node by node, with the cost, latency and tokens each step used.](https://www.getdynamiq.ai/product/observability) ## Documentation - [AI Gateway overview](https://docs.getdynamiq.ai/docs/platform/gateway/overview) - [AI models router](https://docs.getdynamiq.ai/docs/platform/gateway/ai-models-router) - [LLM providers](https://docs.getdynamiq.ai/docs/sdk/llms/llm-providers) - [Model inference deployments](https://docs.getdynamiq.ai/docs/platform/deployments/model-inference-deployments) ## Questions and answers ### How many model providers does Dynamiq support? 29 model providers are available through the same LLM node interface in the SDK, and the most-used ones are routable through the AI Gateway behind one endpoint and one credential. ### Do I need a separate API key for every provider? No. Calling a model through the AI Gateway needs only a Dynamiq Access Key; the gateway resolves the upstream provider's credentials server-side and your client never holds them. ### Can I run an open-source model instead of a hosted provider? Yes. Deploy an open model such as Llama, Gemma, Mistral or Qwen on managed inference and call it behind its own OpenAI-compatible endpoint, for cost, privacy or fine-tuning reasons. ### How does fine-tuning work on Dynamiq? Train LoRA adapters on top of a base model, then serve them over one shared deployment that loads an adapter by name, instead of standing up a new one per fine-tune. ### What does the AI Gateway not do? It proxies chat completions only. Embeddings, audio and image endpoints, and anything outside the chat completions shape, call the provider directly rather than through the router. ## See an agent on your own workflow. Bring a process and its documents. Our engineers will show you how Dynamiq runs it, in your environment or ours. [Start free](https://app.getdynamiq.ai/signup) · [Talk to the team](https://www.getdynamiq.ai/book-a-demo)