What We Promise, In Writing

Removes security objections. Prompts and outputs pass straight through the gateway and are not stored by default.

Exact visibility into costs and savings.

Prevents unexpected bills.

Builds trust with funded AI companies.

Easier purchasing decision. No retainers, and you can leave anytime.

A defensible discount against your existing provider and model pricing.

Same models and features, with a clearly documented migration path.

Evidence of authorized API supply, lawful resale where applicable, and clear data-handling terms.

SLAs, failover, monitoring, and transparent status reporting.

See your actual spend, baseline costs, and realized savings.

Stronger economics for teams spending $10,000–$1 million or more monthly.

Direct, measurable financial incentive: at least 20% lower cost per request after our fee, or you pay nothing.

Minimal code changes. Switching is usually one base URL and one API key.

The cheapest suitable provider for the requested model.

Lower-cost equivalent models, only when approved.

Additional cost savings from prompt caching and batch processing.

Enterprises can retain their provider accounts.

Choose where your requests are processed.

Savings are measured per equivalent successful request, accounting for usage mix, quality, latency and error rate.

Your teardown shows current spend, avoidable waste, projected savings, implementation effort and how confident we are.

Miss 20% after our fee and you pay nothing. Verification rules are in our guarantee terms.

Every change is yours, with documentation, tests and rollback instructions.

We can optimize inside your existing architecture. A migration happens only if you approve it.

No retainers, no long-term contracts, no surprises. Our standard fee is 10% of verified savings, agreed in writing before kickoff.

We trace API spend back to the files, functions, agents, customers and workflows behind it.

We remove redundant tokens, control output length and make prompt prefixes reusable.

Provider prompt caching, safe semantic caching and asynchronous batch processing.

Workload-specific quality evaluations decide which requests can go to less expensive models.

We catch duplicate requests, runaway tool loops, wasted retries and uncontrolled spend.

We compare official providers, negotiated pricing and commitment discounts.

Fine-tuning, distillation, self-hosting or quantization, only when your workload economics support them.

Test datasets, quality targets and failure thresholds are set before any model substitution.

Changes go out behind feature flags and canary traffic, with quality gates and fast rollback.

Savings are reconciled with your provider invoices, not just token counts. See our savings methodology.

You hear about it when cost, latency, model behavior or cache effectiveness drift.

Least-privilege access, no stored prompts or outputs by default, documented deletion and a DPA on request. See our Trust Center.

Versioned changes, test outputs, the baseline methodology, your approvals and monthly reports, all on record.

Start from a read-only usage export or the telemetry you already have. No sales-heavy process.

Dollars saved, margin improvement and payback, ready to forward to your CFO or investors.

Already on LiteLLM or Portkey? apicuts is OpenAI-compatible, so it plugs in like any other provider.

Optional monitoring, re-audits and new optimizations as your usage grows, with no lock-in.

One accountable engineer owns your implementation, incidents and change reviews.

How we measure savings, and its limits, is public in our savings methodology.