Why enterprises need to rethink how employees access LLMs

The excitement around GenAI isn’t slowing down. What started as an experiment in a few innovation teams is now becoming a foundational capability across enterprises. Engineers are building internal copilots, product managers are running rapid prototyping with LLMs, marketers are generating campaign content, and support teams are exploring AI agents — all at once.

As a result, platform and infrastructure teams are facing a new kind of demand: everyone wants access to AI models. Not just ChatGPT — but Claude, Mistral, Gemini, and increasingly, fine-tuned or domain-specific models hosted on private endpoints.

Enterprises are realizing that enabling AI access isn’t just a tooling problem. It’s a fundamental platform strategy decision.

The challenges in centralized provisioning

Most enterprises began their GenAI journey the traditional way: central teams manually provisioned API keys, reviewed use case requests, and managed access to LLMs on a case-by-case basis. This worked when only a few teams were experimenting. But as adoption scales, this model quickly collapses.

• Teams wait days, sometimes weeks, for access.
• IT and platform teams are drowning in provisioning and compliance tickets.
• Ad hoc sharing of API keys, often in insecure or non-compliant ways.
• Shadow usage of public endpoints or consumer tools.

This creates a bottleneck that frustrates both builders and the teams meant to support them. More importantly, it introduces risk, uncontrolled usage, lack of auditability, and inconsistent guardrails across teams and tools.

TL;DR

The irony is clear: enterprises want to encourage innovation, but the process built to protect them ends up slowing everyone down.

To truly enable GenAI at scale, the model has to change. Access needs to move from centralized provisioning to governed self-service.

What self-serve AI access should look like

Self-serve doesn’t mean unregulated. It means giving employees the ability to explore, build, and deploy with AI, within a framework that’s secure, observable, and policy-driven.

• Model access via a unified portal - Employees can discover and use approved LLMs from different providers, all in one place. With an AI Gateway like Portkey, you get a central place to all your LLMs

• Developer-controlled API keys, within org limits - Instead of waiting for infra teams, users generate their own scoped keys tied to organizational usage policies — rate limits, cost caps, and model-specific access rules.
  
• Automated guardrails from day one - Prompt injection protection, content filters, output restrictions, and usage logging are enforced automatically, without manual setup for each team.
  
• Secure, internal deployment - All access flows through a secure gateway inside the enterprise’s network perimeter, no public endpoints, no unsecured tokens.
  
• Cross-provider governance - Whether someone is calling OpenAI or an internal model, the same policies, logs, and usage tracking apply, reducing complexity and risk.

Why self-serve doesn’t mean no control

In traditional models, enforcement is manual and fragmented. One team uses a prompt filter, another doesn’t. One group logs API usage, others bypass it. The result is a patchwork of security and observability, with constant overhead.

With a governed self-serve setup, controls are built into the access layer itself:

• Rate limits and budget caps are applied per team or user automatically.
• Prompt and output guardrails catch unsafe inputs or completions before they reach production.
• Audit logs and traces are captured for every request, across all models, all users.
• Routing rules direct sensitive queries to private or compliant models.

All this happens transparently to the end user. They just generate a key and build. The platform ensures everything downstream stays within policy.

Building the right foundation

Enabling self-serve AI access is a foundational shift in how enterprises approach GenAI adoption. When done right, it enables innovation across thousands of employees without compromising on security, governance, or observability.

Portkey was built exactly for this. As an AI Gateway, Portkey gives enterprises a unified control plane to manage how LLMs are accessed across teams, tools, and providers.

With Portkey, you don’t have to choose between speed and safety. You get both by design. If you're scaling GenAI access across your organization and want to do it right, get in touch with us, we’d love to show you how teams are doing it with Portkey.