The AI Gateway: Centralizing LLM Access and Governance

As developers start integrating Large Language Models (LLMs) into their applications, organizations often face a "wild west" of API keys, unmonitored costs, and potential data leaks. Enter the AI Gateway.

What is an AI Gateway?

An AI Gateway is a specialized proxy that sits between your internal applications and AI providers (OpenAI, Anthropic, Azure AI, Bedrock).

Key Capabilities

1. Centralized Authentication: Manage one set of credentials for providers, while giving internal apps their own identity-based access.
2. Cost Management & Quotas: Implement rate limiting and token budgets by team or application to prevent surprise bills.
3. Prompt and Response Logging: Audit every interaction for security and compliance (PII detection, toxicity filtering).
4. Model Fallback & Routing: Automatically switch to a different model or provider if one is down or too slow.
5. Caching: Cache common responses to reduce costs and latency.

The LLM golden path (practical)

An AI Gateway becomes a true platform capability when it standardizes the “how”:

• an internal SDK / wrapper that enforces auth, quotas, and telemetry
• integration templates (RAG, classification, summarization) with safe defaults
• secrets and environment management (dev/stage/prod)

The goal: a team can integrate a simple use case in hours without re-inventing security.

Security & compliance: decisions you must make

• which data can (or cannot) leave your boundary (PII, secrets, regulated data)
• which policies you apply (redaction, classification, blocking, logging)
• which evidence you retain (audit trails, traceability, retention)

Without explicit decisions, prompt logs become a risk rather than an asset.

Why it's a Platform capability

AI governance shouldn't be re-implemented by every team. The Platform team can provide a "Golden Path" for AI access through a managed AI Gateway.

Conclusion

The AI Gateway is the control plane for the generative AI era. It provides the visibility and guardrails needed to move from experimental AI projects to enterprise-grade, governed AI applications.