FinOps for AI agents

Nobody can tell you what your AI actually costs.

Not per agent, not per team, not per outcome. Companies are scaling agents against one line on a provider invoice, and finance is already asking what it bought. That is the next thing governance has to solve.

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The problem

Three questions your CFO will ask this year.

All of them are fair. None of them can be answered today.

  • What did AI cost us last quarter?

    One line on an invoice. No breakdown, no owner, and no way to connect it to anything the business got.

  • Which team spent it?

    Shared keys and shared assistants produce one shared bill. The money is real, but nobody owns it.

  • Was any of it worth it?

    You can count what the agents did. You cannot see what it was worth. A task that failed four times costs the same as one that worked.

Why it is still unsolved

This is harder than a dashboard.

Plenty of tools show you a total. Almost none can tell you whose it was, because by the time the cost appears, the context that explains it is gone.

Tokens are not cost

Caching, context reuse and model tiers mean two runs that look identical can bill very differently. Counting tokens gives you an estimate, not an answer.

The bill has no names on it

Providers invoice a company, not an agent. By the time the cost reaches finance, there is nothing left to show who caused it.

Attribution needs identity

To charge a cost back, you have to know which person, which agent and which permission produced it. You need that at the moment it happens, not months later.

That is where we already sit

Every governed call passes through Palma with its identity, its permissions and its policy decision attached. Cost is the one field that record is missing.

The Palma console showing connected MCP servers, surrounded by live agent metrics
  • Tokens are not cost

    Caching, context reuse and model tiers mean two runs that look identical can bill very differently. Counting tokens gives you an estimate, not an answer.

  • The bill has no names on it

    Providers invoice a company, not an agent. By the time the cost reaches finance, there is nothing left to show who caused it.

  • Attribution needs identity

    To charge a cost back, you have to know which person, which agent and which permission produced it. You need that at the moment it happens, not months later.

  • That is where we already sit

    Every governed call passes through Palma with its identity, its permissions and its policy decision attached. Cost is the one field that record is missing.

Where it starts

Some of the picture already exists.

Usage is measured per tool and per client today — calls, success rate, latency and token count, with failures broken out by cause. It is not cost attribution yet, but it is the raw material that makes attribution possible.

  • Token count and call volume per tool
  • Failures split by cause — connection, credential, rate limit
  • Usage per client and per session over time
Palma insights showing per-tool token counts, an error breakdown by category, and tool calls and tokens over time

What we're building

Cost, governed like everything else.

Access, policy and audit already follow the person through one connector. Spend is the next thing to add.

  1. 01

    See what an agent actually costs

    Cost per run, per agent and per tool call, instead of a monthly total you split by guesswork.

  2. 02

    Charge it back to the team that caused it

    Spend follows the same identity and groups that already control access, so finance gets a breakdown by team and business unit without anyone building a spreadsheet.

  3. 03

    Let teams set their own budgets

    Today one admin types the same number into every field, because doing it properly takes days. The teams that own the work should own the limit, and it should enforce itself.

  4. 04

    Stop paying for work that produced nothing

    Failed actions, repeated retries and duplicate calls cost the same as the ones that worked. Once you can see them, you can stop them.

  5. 05

    Know what scaling will cost before you scale

    Forecast from real usage, so taking agents from one team to the whole company is a plan rather than a guess.

For what you can control today, see reducing the cost of AI agents.

Book a demo

See what governed AI agents look like.

A 20-minute demo on your stack. We'll show Palma working with the agents, tools and identity provider you already run.

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Common Questions

Quick answers about Palma.ai's enterprise MCP platform

What is Palma.ai in one sentence?

Palma.ai is the enterprise governance layer for MCP — it gives every person and agent a single governed connector carrying the tools and Skills they're entitled to, enforces policies on the actual arguments of a call, pauses high-risk actions for approval, and records everything in a tamper-evident audit trail.

What does MCP governance mean?

Deciding which person may use which tool, with which arguments, with whose approval — and being able to prove it afterwards. MCP itself covers how a client authenticates to a server and how a tool is described and called; it does not decide which person may use which tool, hold a risky call for a human, or keep the record an auditor asks for. Palma adds that layer: one governed connector per person, assigned by identity-provider group, carrying the tools and Skills they are entitled to into whichever assistant they already use.

Does my team have to set up MCP servers themselves?

No. Connectors are assigned by IdP group through Entra or Okta, so a joiner gets theirs on day one and a leaver loses it the moment the group changes. Every MCP server your team approves arrives through that same connector — no per-user install, no config files, no credentials sitting on a laptop.

What's a Skill, and why does it matter?

A tool is a verb — "send an email". A Skill is the playbook that tells an agent when and how to use the verbs it already has: how your team actually closes the books, runs an incident review, or qualifies a lead. Skills are versioned, scanned before they're served, and scoped like any other piece of enterprise software — so your best operator's process reaches everyone else's agent.

Does it work with the AI clients we already use?

Yes — everything is served over MCP, so the same connector, Skills and policies follow the person into whichever assistant they open, whether that's Claude, ChatGPT, Copilot, Cursor or something else. Switching tools doesn't mean re-approving, re-installing or re-auditing anything.

How do we prove what an agent actually did?

Every tool call is attributed to the person it was done for, the agent that did it, and the application it ran in — with the arguments, result, duration and cost. The audit trail is tamper-evident and verifiable offline with your own key, so your auditor doesn't have to take our word for it, and it streams to the SIEM you already run.

How is Palma.ai deployed — SaaS, on-prem, VPC?

Palma.ai is designed for enterprise environments: typically VPC or on-prem, including fully air-gapped, depending on your regulatory and security needs. The MCP layer and governance plane run on your infrastructure, so sensitive business data doesn't have to move into multi-tenant SaaS. We can also host it for you if you prefer.