FinOps for AI agents

# Cost dashboards tell you what your agents wasted last week. ThumbGate stops the waste before it fires.

Most AI-spend platforms — Finout, Vantage, the new "AI FinOps Assistant" wave — focus on showing you the bill after the agent ran: cost allocation, anomaly detection, unit economics finance can trust. A few (Helicone's rate limits, Revenium's Economic Control) add coarse runtime enforcement keyed off $ thresholds or request counts. None of them stop the wasted tool call by understanding why it would have failed.

That's the layer ThumbGate occupies. Every PreToolUse gate that fires is a Claude / GPT call your agent did not make — input tokens you didn't spend, output tokens you didn't spend, retry loop you didn't trigger. The savings are computable, conservative, and now surfaced as a number on your CLI.

"74% of CIOs say their role will be at risk if their company does not deliver measurable business gains from AI within the next two years." — CIO Online, 2026

"Measurable" is the operative word. A token-spend dashboard tells finance how much got burned; it doesn't tell the CIO board what was averted. `thumbgate cost` prints a single conservative dollar figure backed by the gate-block count from your machine — not "what enterprises like you saved." That's the artifact that survives a 2026 budget review.

## One command, one number

Once ThumbGate is installed and gates have been firing, this is what an operator sees. The figures below are an illustrative example — your numbers are computed from your own machine's `gate-stats.json`, not from ThumbGate traction data:

    $ thumbgate cost

    💰 ThumbGate cost-savings — cumulative
    ──────────────────────────────────────────────────
      Tool calls blocked : 247
      Tool calls warned  : 12
      Tool calls passed  : 3,401
      Top blocker        : no-mocked-db (138 blocks)

      Tokens you did NOT spend
        Input  : 494K
        Output : 148K
        Total  : 642K

      Estimated $ saved  : $3.95

The methodology is intentionally conservative: 2,000 input + 600 output tokens per blocked call, a Sonnet-heavy model mix (80% Sonnet 4.5, 15% Opus 4.6, 5% Haiku 4.5), Anthropic published prices. The goal is "you almost certainly saved at least this much" — not "let's flatter ourselves." Override the mix with `--mix '{"claude-sonnet-4-5":1.0}'` if your stack is different.

## Prevention vs. reporting

Capability | Reporting-layer FinOps | ThumbGate (runtime gates)
--- | --- | ---
See what agents spent last week | ✅ | Partial (via dashboard)
Allocate spend to teams / features | ✅ | Per-gate breakdown via `byGate`
Stop a known-bad tool call before it hits the model | ❌ | ✅ — PreToolUse gate fires, no API call made
Promote a one-off failure into a persistent gate | ❌ | ✅ — feedback loop + lesson DB
Surface gate candidates from silent failures (where no human ever gave thumbs-down) | ❌ | ✅ — unsupervised silent-failure clustering, on by default
Print conservative $ saved per day | ❌ | ✅ — `thumbgate cost`
K8s pod-level allocation, finance-grade reporting | ✅ (that's their core) | ❌ (not our layer)

The two layers compose. ThumbGate prevents the wasted spend; a reporting FinOps tool tells finance what the remaining spend was for. Picking ThumbGate doesn't mean you don't also need cost visibility — it means the visibility number gets smaller.

## Why the savings are real, not theoretical

1. Every block is one fewer round trip. A blocked tool call doesn't reach the model. There's no "ThumbGate intercepted but the request still cost you" — the agent's tool-call execution is replaced with the gate's verdict, and the agent's next reasoning step takes the verdict as context instead of the failed result.
2. The avoided retry loop is the bulk of the saving. Failed tool calls don't just cost the call — they cost the model's next reasoning turn (which sees the failure and tries again), and often a third turn (which tries a different approach). Conservative 2k input + 600 output assumes one retry; in practice it's often more.
3. The numbers come from your local `gate-stats.json`. Not from a marketing model, not from "what enterprises like you saved." Your machine, your gates, your blocks.
4. You don't have to give thumbs-down for the system to learn. ThumbGate's unsupervised silent-failure clustering runs by default — it mines your conversation logs for tool calls that failed (non-zero exit code or matched error patterns) but never got an explicit thumbs-down, clusters them by tool + argument shape, and surfaces them as gate candidates. The same false-positive-rate guardrail that vets human-feedback candidates vets these. Lazy users still get the savings. Opt out with `THUMBGATE_SILENT_FAILURE_CLUSTERING=0` if you want HITL-only.

## Get the number on your machine

    npx thumbgate init        # wire the PreToolUse hook
    # ...let your agent run for a few hours...
    npx thumbgate cost        # see what the gates were worth

Or as JSON, if you want to ship it to a dashboard:

    npx thumbgate cost --json | jq .savings.dollarsSaved

"The category isn't 'FinOps for AI' — it's 'gates that stop the spend so FinOps has less to report on.' One sits behind the other."

The free CLI is real. Pro adds the personal dashboard, recall, exports, and managed adapters. Enterprise is the hosted dashboard, org-wide rule library, and the operator the Agent Manager doesn't have to be themselves.

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## Related reading

- ThumbGate for Codex in the Enterprise — the same prevention story for the OpenAI×Dell distribution wave.
- ThumbGate for the Agent Manager — the role inside the org that owns "what are our agents costing us."
- Dashboard demo — the $ saved number rendered against demo data, so you can see the shape before installing.