LLM Gateway: secure AI and your data (without blocking teams)

An LLM Gateway is not just another proxy: it’s where you enforce governance, security, and observability on AI usage—right inside your workflows.

When AI lands in organizations, the risk is not “having a chatbot”. The risk is letting LLM calls spread without control, without audit, and with sensitive data leaving your boundaries.

An LLM Gateway answers one question: how do you industrialize AI usage inside workflows (DevSecOps, run, delivery) while keeping control?

1) Centralize LLM calls to standardize

Without a gateway, each tool integrates its own provider, keys, logging, and limits—leading to duplication and blind spots.

With a gateway you get:

one entry point,
multi-model routing (OpenAI/Anthropic/Gemini/Azure OpenAI…),
quotas per team/product,
usable audit logs.

2) Enforce governance in the flow

Governance works when it’s by design:

redaction / masking (PII, secrets),
allow/deny lists per model,
policies per context (environment, product, role),
prompt guardrails (length, injection patterns, allowed sources).

3) Measure: cost, risk, adoption

The gateway becomes a metronome:

consumption (tokens),
quality (p95 latency, errors),
risk surface (exfiltration, sensitive content),
adoption by workflow.

Conclusion

In Argy, the LLM Gateway is a platform capability: it integrates with modules, audit, and quotas—so AI is an accelerator, not a gray area.

If you want to frame AI without slowing teams down, request a demo or explore Argy’s automations.