AI Gateway for governance in Azure AI apps

As enterprises race to integrate AI into their products and workflows, Microsoft Azure has become a go-to platform, offering access to powerful models through Azure OpenAI, AI Foundry, and other tools. Teams are rapidly building applications like chatbots, summarization tools, code assistants, and more, all powered by Azure’s stack.

But while Azure makes it easy to deploy and scale AI models, governing their use across teams, environments, and applications remains a challenge. From enforcing organizational policies to ensuring ethical usage, many companies find themselves struggling to maintain control over how AI is being used once it’s in production.

The growing challenge of AI governance on Azure

With growing usage, the risks and complexities around governance grow as well. Here are some of the most common challenges teams face when managing AI usage on Azure:

Lack of centralized visibility

Azure makes it easy to call an AI model, but it’s hard to get a centralized view of how those models are being used across your organization. Teams often ship prompts directly from frontend or backend code, with no logging or oversight. There’s no unified dashboard to track prompts, outputs, or user interactions.

No consistent policy enforcement

There’s no out-of-the-box way to enforce input/output guardrails like PII redaction, jailbreak prevention, or prompt structure validation. If one team adds filters and another doesn’t, you end up with inconsistent security and compliance across products.

Difficulty auditing LLM usage

Standard logging tools don’t capture prompt-level detail or LLM-specific metadata. This makes it nearly impossible to answer basic questions like:

• What prompt led to this output?
• What triggered the model call?
• Was the output flagged for issues?

With multiple teams building apps across staging and production environments, each using different models and prompts, governance quickly becomes unmanageable without a centralized system.

An AI gateway purpose-built for governance

To solve these challenges, teams need a purpose-built governance layer that sits between their application and the underlying AI provider.

Portkey is that layer. It is an AI gateway that plugs directly into your entire Azure stack, giving you full control over every prompt, response, and request, without needing to rebuild your infrastructure.

It solves the four biggest challenges teams face: enforcing policies, gaining visibility, enabling auditing, and managing costs.

Centralized policy enforcement

Portkey allows you to define and apply organization-wide guardrails on every prompt and response, regardless of which team, app, or environment is making the call.

You can enforce:

• Prompt validation: Ensure prompts follow a defined structure or block certain inputs entirely.
• Output filtering: Automatically block or redact responses containing unsafe or sensitive content (e.g., PII, profanity, jailbreak indicators).
• Rate limits and usage boundaries: Set usage caps per team, environment, or use case to avoid overuse or misuse.

This ensures consistent governance across your entire AI footprint, without relying on every team to build their filters.

Full-stack observability

With Portkey, every model call becomes traceable and analyzable. You get out-of-the-box observability that’s purpose-built for AI workloads.

• Prompt and response logging: View full request and response pairs, along with model, temperature, and token settings.
  
• Rich metadata support: Attach user IDs, feature flags, or business context to every call for more meaningful analysis.
  
• Token-level insights: Track usage, cost, and latency per prompt — and identify bottlenecks or inefficient prompts.
  
• Dashboards per team or environment: View usage breakdowns by service, user, endpoint, or app.

Portkey also provides support for exporting your analytics data to OTEL-compatible collectors, allowing you to integrate Portkey’s analytics with your existing observability infrastructure.

Feedback and auditing

AI governance isn’t complete without the ability to audit and improve responses over time. Portkey enables:

• Feedback collection: Add thumbs up/down, quality scores, or structured feedback for every output.
  
• Audit trails: Trace exactly what prompt led to what response, when it happened, and who triggered it, essential for compliance reviews.
  
• Post-hoc analysis: Filter and review calls that triggered guardrails, failed policies, or received negative feedback.

FinOps integration

LLM usage costs can spiral quickly, especially with high-volume Azure OpenAI calls. Portkey gives you FinOps, the visibility and controls you need to stay on budget.

• Cost dashboards: Break down spend by team, endpoint, or use case.
• Token usage alerts: Catch expensive calls before they snowball.
• Budget caps: Set limits to block or throttle requests once a quota is hit — per app, per user, or per environment.

With Portkey, you can tie governance directly to cost control, ensuring responsible and efficient AI usage at scale.

Governance on autopilot with Portkey

Azure makes it easy to access powerful AI models, but it leaves teams without the tools they need to govern that usage effectively. As AI apps move from experimentation to production, governance is no longer optional — it’s critical for safety, compliance, cost control, and long-term scalability.

Portkey fills this gap. By acting as an AI gateway purpose-built for governance, Portkey helps teams building on Azure enforce policies, observe usage, manage costs, and audit behavior — all without re-architecting their apps.

If you're building AI apps on Azure and struggling to maintain control, book a demo or get started for free!