Caveman for CFOs and finance: AI spend you can defend

Caveman Cloud gives finance leaders a clear, defensible view of AI spend across teams, agents, and workflows. It measures cost from provider-reported usage and public catalog prices, separates real verified savings from estimates, and enforces budgets with alerts. This page explains how to read Caveman’s numbers, govern spend with soft and hard caps, run chargeback and showback reports, and spot inflated claims from any AI cost vendor.

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Why AI spend is hard to govern

AI workloads differ from traditional software costs in three ways that make them difficult to budget and report:

• Usage-priced, not seat-priced. You pay per token, and usage can spike unpredictably when an agent retries, a workflow loops, or a model is upgraded.
• Spread across teams. Engineering, support, and operations may each run agents with different keys, models, and contract terms. Without unified telemetry, no one sees the total.
• Vendor savings claims are inflated. Many vendors report “up to” figures from benchmarks, project them to a full month, or count correlation as causation. Those numbers rarely survive audit.

Caveman addresses each of these by measuring spend at the request level, labeling every savings figure with its evidence quality, and enforcing caps before traffic reaches the provider.

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How Caveman measures spend

Caveman calculates measured spend by multiplying provider-reported token counts by public catalog list prices. This happens automatically for every request that passes through the gateway. When a model has no catalog entry, Caveman prices it at $0 and labels it unpriced. This lowers coverage rather than guessing a price. Coverage is the share of your traffic that carries complete, catalog-priced telemetry. Low coverage does not mean Caveman is broken; it means some traffic uses unknown models or failed to report usage. The console shows coverage per workload so you can decide whether to register missing models or investigate failures.

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The four evidence labels

Caveman groups every cost and savings figure into one of four labels. Each label answers a different question and belongs in a different part of a budget or board deck. Overlapping detectors collapse into mutex families so headroom is never double-counted. If two optimizations could apply to the same request, Caveman groups them into one family and caps the combined estimate. This keeps inferred numbers conservative and honest.

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What verified requires today

Verified savings is the only per-request causal number. It requires a proven method that connects a specific Caveman transform to a provider-measured delta on that exact request. Three methods exist today: Each method is enforced by an exact provider and optimizer tuple. A PR or passing eval is a proposal, never a saving. Verified savings stay zero when the evidence is absent, and they can be negative when cache-write premiums exceed read savings so far.

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Budgets and caps with alerts

Caveman provides two types of spend controls in Governance → Budgets:

• Hard caps block traffic at the gateway when settled spend plus live reservations would exceed the limit. The gateway returns HTTP 429 with cave_budget_exceeded.
• Soft spend alerts notify you when thresholds are crossed but do not block traffic. They remain advisory and fail open.

When a cap is crossed, Caveman files an Inbox item and emails the organization’s active owners and admins. This happens once per scope, cap, and period. You can configure budgets per project and per key. Hard caps reserve a conservative maximum before the upstream call using public catalog list prices. A delivered response whose usage never arrives is charged its reservation. An unreachable budget store fails closed for hard caps and open for soft alerts.

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Chargeback and showback by team, agent, and workflow

Caveman attributes every request to dimensions you can use for internal billing or cost transparency:

• Team / member (requires billing:read permission)
• Agent (via x-cave-agent header)
• Workflow (via x-cave-workflow header)
• API key
• Model and provider

The Spend page in the console breaks down measured spend by any of these axes. The default axis is workflow; switch to member, model, or key to answer different questions. Per-person columns need the billing:read permission. Users with the billing role can view usage and spend figures but cannot modify keys, guardrails, or team membership. Owners and admins can manage budgets and guardrails. See Team and Admin for role details.

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What to ask your engineering team for

If you are setting up chargeback or showback for the first time, ask your engineering team to:

1. Send agent and workflow headers on every request. The x-cave-agent and x-cave-workflow headers label traffic automatically in traces and spend reports.
2. Use separate API keys per team or environment. This makes key-level budgeting and attribution straightforward.
3. Grant billing:read to finance users. Add finance stakeholders with the billing role so they can view spend breakdowns without administrative access.
4. Review coverage gaps. Ask which models show as unpriced and whether they should be registered in the catalog or investigated for missing usage.

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Signed receipts

Caveman supports Ed25519 receipt export for verified savings. Receipts are hash-chained, take exact 1e-10 USD units, and include the day’s distinct cave_savings_method tags. They verify included hashes and signatures, but they do not attest to omitted tails or scopes. To request a receipt export, contact your Caveman account representative or email contact@caveman.so.

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Red flags in AI cost vendor claims

When evaluating any AI efficiency vendor, including Caveman, watch for these warning signs:

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Fit and honest expectations

Caveman pays off when input is much larger than output and the input is repetitive or bloated. Strong fits include RAG systems, agents with large system prompts and tool schemas, tool- or log-heavy pipelines, and high-volume support bots with long preambles. Weak fits include short prompts with long outputs, already-lean prompts, and quality-critical work that cannot be evaluated. In those cases, Caveman stays byte-safe: on parse errors or unsupported inputs, the gateway forwards the original bytes unchanged. The benchmark envelope for moderate compression on input-heavy workloads is approximately 24-28% potential input-cost reduction. This is an inferred estimate, not an invoice or verified claim. Your actual verified savings depend on your traffic shape, model mix, and which verified methods apply.

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Data and trust

Hosted Caveman Cloud runs on GCP in europe-west4 with ClickHouse Cloud in the same region, behind Cloudflare. Request and response bodies are kept by default, envelope-encrypted with AES-256-GCM per object and wrapped by Cloud KMS, so traffic can be replayed to prove a fix. You can switch raw payload storage off to keep metadata only. Any request can send x-cave-retention: metadata or x-cave-retention: zdr to refuse content storage. Enterprise and customer installs never share data for training. No training runs in production today. Subprocessors are listed at caveman.so/legal/subprocessors. Legal documents, including the DPA, are drafts; request current versions from contact@caveman.so. Caveman is not SOC 2 certified. Do not claim SOC 2, HIPAA, or any certification on Caveman’s behalf.

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Next steps