LLM Gateway

Model access.
Production control.

Choose supported model providers through one OpenAI-compatible endpoint. Keep access policy, sensitive-data inspection and usage accountability in a vendor-agnostic boundary layer.

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Governed model accessGoverned

01
AuthenticateUser or service principal
02
Resolve the modelApproved provider and model alias
03
Apply policyFirewall, scanning and optional DLP
04
Record the decisionTokens, estimated cost and request logs

Identity · Policy · EvidenceWorkflow illustration

The approved model path

Make model access easier
to use and easier to govern.

Centralize credentials

Hold provider credentials in the control plane, encrypted at rest. Applications authenticate to AI Warden instead of distributing raw vendor keys.

Enforce inline controls

Apply rate, concurrency and token policies. Scan requests and responses for configured sensitive-data and security patterns.

Account for usage

Track input and output tokens, latency, decisions and estimated cost. Apply compression policies and monthly budgets for human users.

A familiar integration

Keep the client.
Govern the route.

Use a supported OpenAI-compatible client, point it at AI Warden and request an approved model alias. The gateway resolves the upstream provider and applies the effective policy.

One approved endpoint for supported model traffic
User and service-principal authentication
Provider and model configuration in the control plane
Request and response decisions in the audit log

Python / OpenAI-compatible clientExample

from openai import OpenAI

client = OpenAI(
    base_url="https://your-gateway.example/v1",
    api_key="YOUR_AIW_TOKEN"
)

response = client.chat.completions.create(
    model="approved-model-alias",
    messages=[{
        "role": "user",
        "content": "Summarize this report."
    }]
)

Usage under control

Control demand.
Understand the cost.

Token-aware policies

Set request-rate, concurrency and token limits at the gateway. Apply policy overrides to the relevant providers and principals.

Compression with a record

Eligible prompts can be compacted after request-side security scanning. Request logs record compression estimates and tokens saved; results depend on the prompt and policy.

Budgets for human users

Set monthly user budgets with group inheritance and user overrides. Warning and freeze thresholds help control further model access.

Cost visibility

Configured input and output rates turn usage into estimated cost. Use provider invoices for final billing reconciliation.

Questions, answered

A clearer path to adoption.

Can we use our existing DLP service?

Yes. Optional ICAP integration adds request and response inspection to the LLM Gateway. Enabled checks fail closed on inspection errors. Validate the integration with your DLP service and deployment requirements.

Does the gateway support streaming?

Supported model responses can use SSE streaming. With ICAP response inspection enabled, LLM output is buffered for inspection before approved content is delivered, which changes time to first response.

Are usage costs final billing figures?

No. Costs and compression savings are estimates based on configured token rates. Final billing remains with the model provider.

See AI Warden in action

Make your next AI rollout a governed one.

Bring your models, tools and security requirements. See how AI Warden puts them under one control plane.