Adjacent tools exist, and major platforms are bolting on cost features. But none of them prevent a breach before spend, none rebalance a fleet by business priority, and none produce a finance-grade set of books. The gap isn't a feature — it's an architectural posture.
No one is wrong; everyone is partial. Gateways were built for developers. FinOps was built for cloud cost. Observability was built for traces. Routers were built for per-call quality. Platform-bundled features were built for vendor lock-in. None of them were built to be the financial system of record for an autonomous fleet.
Gateways have already won the request-path real estate at most teams. They tag, log, retry, route, and apply spend-capped keys. For developer-grade observability and basic per-key quotas, they are good enough.
We integrate with gateways where it makes sense — point Portkey at Tarmac, or run Tarmac alongside Kong, and keep their tracing while we own the cap. The CFO relationship is ours.
FinOps platforms have built a decade of muscle on the cloud bill: tag, allocate, forecast, chargeback. The new "FinOps for AI" tier extends the same model to LLM spend — read CSP and provider bills, allocate, report.
FinOps tools are great post-hoc. Tarmac is the pre-hoc. The two are complements: many of our customers feed Tarmac receipts into CloudZero or Vantage for cross-line-item allocation. Tarmac is the only one that prevents the line item from becoming a problem in the first place.
Observability tools are how engineers ship agents that work. They trace each run, attach evals, surface regressions. For debugging an agent's behaviour, they are essential.
Tarmac and an observability tool are read/write halves of the same picture. You ship every authorization to your observability stack from Tarmac's webhook stream; we don't try to replace tracing or evals. We own the decision.
Routers optimize a single call — they ask "given this prompt, which model gets the best quality-per-dollar?" and forward it. Useful for teams who care about per-request unit economics on commodity work.
A router can be Tarmac's lane — we pin the family, the router resolves the specific model, Tarmac controls the tier. We are happy to sit on top of one or to replace one for teams who don't need that flexibility.
Hyperscalers and big-tent platforms are bundling cost controls into their stacks. If you live entirely inside Bedrock, or entirely inside Vertex, the built-in budget feature is the path of least resistance.
Tarmac's value compounds with provider diversity. A bundled control plane has every reason to keep you on one provider; Tarmac has every reason to let you span the best of each. Customers tell us this is the single biggest reason they buy.
"You can't enforce a budget you can read. You enforce a budget you sit in front of."// Jed · founder · Tarmac
Every gap above traces to a single architectural choice: prevention requires sitting in the request path with an atomic hold against the live budget, and producing an append-only ledger that becomes the source of truth for both engineering and finance. Tools designed for other purposes can't be retrofitted into that posture without rewriting their core.
The control surface is downstream of the spend. The "cap" is a counter that fires an alert when it's already too late. The dataset is a copy of the provider's bill — interpreted, allocated, reconciled by hand.
The control surface is upstream of the spend. The cap is enforced by an atomic hold in the same transaction that authorizes the run. The dataset is the authoritative ledger — the provider invoice reconciles to it, not the other way around.
Customers ask. We're happy to walk you through the analysis. It's not the engineering hours — it's the design partner pressure, the on-call burden, and the compliance work that engineering teams routinely under-estimate.
Inference is becoming the dominant cost of AI. Agents are going into production fast. And the spend is getting out of hand at a rate no adjacent vendor is moving to govern. "FinOps for AI" has emerged as a named discipline precisely because the pain is now industry-wide. The first mover to own the enforcement-plus-finance combination can own the category narrative.
Global AI spending is on the order of $2.5T in 2026; inference now accounts for roughly two-thirds. The inference market scales from $106B in 2025 toward $255B by 2030 at a ~19% CAGR.
Gartner expects 40% of enterprise applications to embed task-specific AI agents by the end of 2026. Roughly 31% of enterprises already run at least one agent in production.
The median enterprise's monthly LLM bill is growing about 7.2× year-over-year into 2026. A single multi-step agentic decision cycle costs $0.10–$1.00 against $0.001 for a simple call — cost compounds with autonomy.
If you already have observability, FinOps, and a gateway — keep them. Add Tarmac as the layer between the agent and the spend, and the layer between the spend and the books.