Governed Latent Execution for agent and application workloads

Reuse stable LLM work.
Govern what executes again.

CachePilot sits between your workloads and model providers to keep stable context cache-eligible, detect reuse-breaking drift, enforce execution policy, and prove expected versus actual reuse with auditable receipts.

  • OpenAI Responses
  • Anthropic Messages
  • Claude/Fable adapter routing

BYOK · Content-free telemetry by default

Controlled benchmark · 12 requests per arm
baselinegoverned
policy modeobservegovern
mutation receiptnonecache breakpoint
provider cache reads0126,973
full-price input138,91424
Measured cost outcome: $1.4413 → $0.3280 (77.2%)
Governance changed the cache eligibility; provider-reported usage established the actual reuse. The injected breakpoint is visible in X-CP-Mutations. Read the methodology →
Reuse without losing control

Caching is a mechanism. Governance decides whether reuse is valid.

CachePilot does not promise to force opaque provider caches. It makes requests eligible for reuse, records the conditions, verifies the upstream result, and keeps policy attached to the execution.

Reuse is the mechanism

Stable instructions, tools, and context should be paid for and processed once when the provider surface supports it.

Governance decides validity

A hit is only good when the reused state is still policy-correct, attributable, and safe for the current workload.

Cost is an outcome

Lower input cost can follow valid reuse, but latency, drift, receipt completeness, and execution correctness may lead the decision.

Sound familiar?

Hosted model execution fails quietly before it fails expensively.

A reusable request keeps executing again.

The stable prompt, tool definitions, and context are present on every call, but one reordered field or missing breakpoint turns intended reuse into full execution. The provider accepts the request either way.

Nobody can explain the miss.

A cache-rate chart says performance changed, but not whether the request was eligible, which stable prefix moved, or whether the upstream provider actually reported cached tokens.

Execution policy drifts with application code.

Prompts, params, budgets, and tool access change with no gate or receipt trail. Cost may move, but the earlier warning is lost reuse eligibility and an unexplained change in execution shape.

How it works

Prove it on one workflow before you trust it with the fleet.

01

Route one workflow

Swap the base URL, keep BYOK, and start in observe mode on OpenAI Responses or Anthropic Messages.

02

Run it twice

The first identical call is the warmup. The second lets CachePilot compare expected reuse with actual cached input tokens.

03

Govern only with evidence

Enable repair and policy after the stable-prefix, reuse, latency, drift, receipt, and cost evidence supports it.

What operators get

Evidence for the execution decisions you have to make.

Reuse eligibility

See whether the request had a stable, cache-eligible prefix before treating an upstream miss as a cost problem.

Expected versus actual reuse

Compare the reuse CachePilot expected with the cached input tokens the provider actually reported on the repeated call.

Safe reuse repair

Stabilize supported request prefixes, prompt cache keys, and Anthropic cache breakpoints without pretending an opaque provider hit can be forced.

Governed execution

Apply output budgets, model and tool policy, supported parameter repair, and observe-or-govern modes at the proxy boundary.

Receipts and drift alarms

Pin a golden run and keep X-CP-* proof of the prefix, policy, mutations, budget, and cache-related behavior on each response.

Attribution and outcomes

Connect reuse, latency, tokens, and cost to a project and route without storing prompts or outputs by default.

Find out whether one workflow is actually reusable.

Start with two identical calls in Free Observe, or run a dedicated 14-day design pilot that compares baseline and governed execution. Cost is included in the evidence, but it is not assumed to be the leading indicator.