Azion AI Inference is a serverless platform for deploying and running AI models on a distributed architecture. It provides an OpenAI-compatible API for easy migration, supports LLMs, VLMs, embeddings, and reranking models, and offers LoRA fine-tuning for domain customization. Scale automatically without GPU management while maintaining low-latency responses globally.

Choose from a catalog of open-source models for text and code generation, vision-language tasks, embeddings, and reranking. The catalog evolves as new models become available, and you can fine-tune supported models with LoRA for your specific domain.

Yes. AI Inference uses an OpenAI-compatible API format, so you can migrate existing applications by updating the base URL and credentials. Keep your current SDKs and integration patterns—no code rewrite required.

Yes. AI Inference supports LoRA (Low-Rank Adaptation) fine-tuning, allowing you to specialize models for your domain without full retraining. This reduces compute costs while improving accuracy for specific tasks like customer support, code generation, or domain-specific Q&A.

Use AI Inference with SQL Database Vector Search to store embeddings and retrieve relevant context for RAG applications. This built-in vector search means no separate vector database to manage—SQL and vectors in one service.

Yes. AI Inference powers agent patterns like ReAct and tool-calling workflows when combined with Applications, Functions, and external APIs. Azion provides templates and guides for LangChain and LangGraph-based agent architectures.

Migration is straightforward due to OpenAI-compatible APIs on both platforms. Update your base URL to point to Azion AI Inference endpoints, migrate any LoRA adapters, and integrate with Azion Functions. If you use Cloudflare Vectorize, migrate to Azion SQL Database Vector Search for built-in vector storage.

AI Inference uses straightforward per-request pricing without abstract units like "neurons." You pay for inference requests based on model and token usage—no idle costs, no capacity commitments. This transparency makes cost forecasting predictable compared to neuron-based billing models.

Create an AI Inference endpoint, then integrate it into your request flow using Applications and Functions. This adds AI capabilities to existing APIs and user experiences with distributed execution, managed scaling, and automatic failover.