Building with large language models (LLMs) often leads to complications like managing API keys and unexpected costs. Pydantic AI Gateway (PAIG) aims to simplify this by providing a streamlined way to access multiple AI providers with a single key, eliminating the delays associated with universal schemas that many other gateways use. Instead of waiting for updates when providers release new features, PAIG allows users to send requests in the provider's native format immediately. This direct approach is complemented by observability features through integration with Pydantic Logfire, which tracks usage and spending. The gateway supports multiple models from well-known providers like OpenAI, Anthropic, Google, and AWS, with more integrations planned. Users can set spending limits at various levels to prevent unexpected charges, and they benefit from fast performance due to PAIG’s deployment on Cloudflare's edge network. This setup minimizes latency, ensuring quick responses even when requests are made from distant locations. PAIG is open-source at its core, but some features, such as the UI and SSO support, are closed source. For developers already using Pydantic AI, switching to PAIG requires only a simple code adjustment. It also supports various SDKs, allowing users to maintain existing code while leveraging PAIG's capabilities. The team behind PAIG emphasizes transparency and control, rejecting the notion of a "one schema" for all providers, which can obscure functionality and hamper progress. With a focus on observability and budget control, PAIG aims to be a reliable tool that simplifies the management of AI integrations and reduces costs.