Solution · AI FinOps
AI FinOps & Model Routing: Cost Per Successful Workflow, Not Cost Per Token
Routing proxy + quality floors + budget-by-workflow + outcome attribution — so the CFO can see cost per resolved case, not cost per token.
What this solution does
AI FinOps puts every AI call behind a routing proxy that logs user, team, workflow, task type, model, tokens, latency, and downstream business result. Routing rules or lightweight routers enforce a quality floor per workflow; budget policies attach to workflows, not seats. Outcome attribution ties spend to a named business KPI. Result: 'what did we get for it?' becomes answerable in a dashboard, not a spreadsheet.
The buyer problem
- ›Nontechnical staff already 'past the pilot phase' with unpredictable AI subscription spend
- ›No way to route lower-priority work to cheaper models
- ›AI cost measured by seat or MAU — neither reflects value
- ›Fragmented logs across Copilot, Anthropic, OpenAI, and API gateways
- ›CFO can't answer 'what did we get for the AI bill?'
Good-fit signals
- Multi-provider AI spend already in production (Copilot + API-based agents + engineering assistants)
- Growing API usage and hard budget scrutiny from finance
- At least one workflow with a measurable business outcome (resolution rate, cycle time, etc.)
- Willingness to centralize AI traffic behind a proxy
Pilot scope
In scope
- +Cross-provider log normalization (user, team, workflow, task, model, tokens, latency, outcome)
- +Workflow-to-call mapping
- +Quality floors per workflow (accuracy, latency, containment)
- +Shadow routing (observe-only) → active routing
- +Budget caps + alerts by workflow, not by seat
- +Outcome attribution dashboard: cost per successful workflow
Out of pilot scope
- −Replacing your ITFM / cloud-billing platform
- −Full FinOps program build
- −Model foundation choice for greenfield use cases (separate advisory)
Timeline: 6–10 weeks · Investment: Fixed-fee build; optional monthly optimization retainer
Systems we typically integrate
- Anthropic / OpenAI / Azure OpenAI / Bedrock
- GitHub Copilot / Cursor / Codeium
- Microsoft 365 Copilot
- Internal API gateways (Kong, Istio, LiteLLM, custom)
- Snowflake / BigQuery / Postgres (usage warehouse)
- Looker / Tableau / Metabase (dashboards)
Where humans stay in the loop
- Routing-rule changes require platform-owner review
- Budget-cap overrides logged with justification
- Model tier upgrades for a workflow gated by the process owner
- Vendor swaps reviewed with procurement + security
KPI scorecard
| Metric | Typical baseline | Pilot target |
|---|---|---|
| Cost per successful workflow completion | Not measured | Baseline set + trending down |
| % AI spend tied to a named business KPI | <20% | >80% |
| Routed-call mix to non-frontier models | <10% | 40–70% where quality allows |
| Budget variance vs plan | ±30–50% | ±10% |
Risks and the controls we put in place
Fragmented logging blocks attribution
Log normalization layer is delivered first; workflows without traceable outcomes are surfaced for redesign.
Poor quality labels
Quality floors set per workflow with the process owner; shadow-routing period validates floors before active routing.
Teams resist model constraints
Router is transparent — teams see routing reasons + can escalate to frontier when needed; no black-box downgrade.
Router itself becomes a latency bottleneck
Proxy runs in-region, warm-cached, with a fallback direct-call path when quality floor demands it.
How we help
Shadow first, active second
We route in observe-only mode long enough to validate quality floors before real traffic switches.
Budget by workflow
'Copilot seats' is a procurement view. 'Cost per resolved ticket' is a management view — we deliver both.
Outcome attribution built in
Every routed call is tied to a downstream business signal so ROI is a query, not a debate.
Vendor-neutral
We route across Anthropic, OpenAI, Azure OpenAI, Bedrock, and self-hosted equally — no channel-partner bias.
Frequently asked questions
Isn't this just LiteLLM or an API gateway?+
Those are components. The engagement wires them into workflow-level quality floors, budget policies, and outcome attribution — the part that actually answers the CFO's question.
Will routing degrade quality?+
Only within the quality floor you set per workflow. Shadow routing validates the floor before real traffic switches.
Can we route across cloud + self-hosted?+
Yes. Self-hosted (vLLM, Ollama, Bedrock custom) is a first-class routing target.
How is this different from cloud FinOps?+
Cloud FinOps covers infrastructure. AI FinOps adds model choice, quality floors, and workflow-level outcome attribution — none of which show up in a cloud bill.
How fast do savings appear?+
Typical clients see 30–60% cost-per-workflow reduction within one quarter of active routing, without measurable quality loss.
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