AI FinOps
Lower AI unit cost before the provider invoice arrives.
AI Warden turns the LLM Gateway into a FinOps control point: compress tokens, cache safe repeats, route work to the right model, enforce budgets by user, team, agent or product, and allocate spend to the business workflow that created it.
Runtime FinOps
Control spend while the request is still in flight.
Dashboards after the fact are useful, but they cannot prevent waste. AI Warden sees every LLM request before it reaches the model, so cost policy can become an operating control.
Every chat, API call, hosted agent run and MCP workflow passes through the gateway where token demand, model route, budget scope and policy outcome are known in real time.
Token compression and avoided spend
Token compression is not a cosmetic optimisation. It is a savings engine.
Large prompts, conversation histories, retrieved documents, logs and JSON payloads can explode cost. AI Warden can apply compression in the gateway so applications and agents do not each need to reinvent cost controls.
- Compress repeated conversation history and oversized context.
- Compact structured JSON, logs and retrieval snippets before model egress.
- Measure tokens before and after compression to prove avoided cost.
- Keep policy scanning and DLP inspection in the request path so savings do not bypass control.
Flexible budget hierarchy
Budgets should match how the enterprise is accountable for AI.
AI Warden can apply budgets and thresholds at the level that matters: tenant, department, cost centre, team, product, project, environment, user, hosted agent, service principal, provider, model alias or route. Actions can warn, downgrade, require approval, restrict tools or block.
- Per-user limits for broad employee chat adoption.
- Per-agent budgets for business-owned workflows and autonomous agents.
- Per-product budgets that align spend with internal business services.
- Provider and model-route controls for vendor exposure and committed-spend management.
Showback, chargeback and unit economics
Turn raw token counts into business reporting.
The gateway links each call to principal, delegated user, hosted agent, provider, model, route, token count, compression result, cost estimate, policy outcome and request log. That gives FinOps teams the detail needed for showback and chargeback.
- Spend by department, cost centre, product, user, agent and provider.
- Month-end forecast using token velocity and budget thresholds.
- Anomaly detection for premium models, long responses and looped agents.
- Cost per chat, summary, ticket, report, tool run or workflow.
- Route and model comparison by latency, errors, tokens and estimated cost.
- Proof of savings from compression, caching, routing and prompt improvement.
FinOps operating model
Govern adoption without turning cost control into a blocker.
AI Warden lets organisations offer AI broadly while keeping spend measurable and governable. The point is not to stop useful AI; it is to make every useful workflow cheaper, attributable and predictable.
- Launch employee chat with per-user budgets and route-level defaults.
- Give departments agent budgets before they publish business-owned agents.
- Review high-cost prompts and workflows with engineering owners.
- Use governance controls to prove budget policies exist for regulated products.
| Question | AI Warden answer |
|---|---|
| Who spent it? | User, delegated user, agent, service principal and group. |
| Why was it spent? | Route, workflow, model alias, request log and tool call context. |
| Could it be cheaper? | Compression, cache, model right-sizing and prompt analysis. |
| Who owns it? | Product owner, agent owner, budget scope and review evidence. |
Business outcome
Achieve material AI savings without slowing adoption.
AI Warden gives FinOps teams the levers to reduce token demand, shift work to the right model, cap runaway usage and report AI unit economics at the level the business understands.