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Wardin is a policy-enforcing LLM gateway combined with finance-grade cost allocation and productivity attribution tied to business outcomes — three categories that are normally three separate vendor products, built as one system.

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The core problem

Most teams deploying AI at scale stitch together a gateway + observability tool + guardrail service + FinOps tool + productivity tool. Each sits outside the request path, producing dashboards that lag reality, and none of them enforces anything. Wardin sits in the request path — between your code and every LLM provider. That means:
  • Budgets are enforced before a token is spent, not discovered in a report
  • PII is redacted before it leaves your network
  • Policies (model allowlist, injection detection) block non-compliant calls at the source
  • Every request is attributed to a team, user, and session for ROI tracking

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How it works

You point your existing SDK at https://gw.wardin.ai instead of https://api.anthropic.com. No client code changes.

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Key concepts

Virtual Keys

Scoped API keys that proxy to your real provider credentials — with per-key budgets, rate limits, and policies.

Budgets

Hard spending limits enforced via Redis atomic counters before a request is forwarded.

Policies

Model allowlists, prompt injection detection, PII redaction — enforced in-path.

Caching

L1 exact-match and L2 semantic caching reduce spend without changing your prompts.

Sessions

Requests grouped into sessions so cost-per-task is a real metric — built for agentic clients.

Signed Receipts

Every request emits an ED25519-signed, hash-chained receipt — tamper-evident audit evidence.