AI Cost OptimizerAvailable now

Cut your AI spend. Same models, same answers.

A drop-in gateway that sits in front of every large-language-model provider you already use. Bring your own keys, point your SDK at one endpoint, and semantic caching, accuracy-first routing, same-model arbitrage, and batch orchestration shave the bill on every call. Every optimization is output-preserving — and you only pay a share of what you actually save.

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Bring your own keys · Every major provider · Billed only on proven savings

Drop-in, bring-your-own-keys

One endpoint in front of every provider.

Keep your own provider accounts and your own keys. Change a single base URL and your existing OpenAI- or Anthropic-shaped calls flow through the optimizer unchanged — every response stays faithful to what the model would have returned. Your prompts, your keys, and your data stay under your control.

  • Bring your own keys (BYOK) — your provider accounts, your negotiated rates, your data-processing terms
  • OpenAI- and Anthropic-compatible endpoints — usually a one-line base-URL change, no rewrites
  • Works across every major provider — OpenAI, Anthropic, Google Vertex / Gemini, AWS Bedrock, Azure
  • Output-preserving by default — optimizations never change the answer your model would have given

Where the savings come from

Four independent levers, each provably output-preserving, stacked on every request. Turn them on individually or all at once.

  • A brass key turned in a dark machined gate; dozens of light threads converge through one aperture into a single beam.

    Drop-in BYOK gateway

    Point your SDK at one endpoint and keep your own provider keys. No rewrites, no proxy lock-in, no data detour — requests pass straight through with your identity and your rates, and you can route around the gateway any time.

  • A row of identical dark glass vials on a shelf; one glows green and its light shows faintly in every neighbour.

    Semantic caching

    Semantically-equivalent prompts serve a cached answer instead of paying for the same generation twice. A three-stage equivalence gate — vector match, cross-encoder, then an LLM judge — guarantees the cached reply means the same thing, so you never serve a stale or wrong answer to save a few cents.

  • A brass laboratory balance holding level: a small glowing green sphere on one pan, a larger dim sphere on the other.

    Accuracy-first model routing

    A lightweight judge routes each request to the cheapest model that will still get it right, and arbitrages identical calls to the lowest-cost equivalent provider or region. Accuracy comes first; cost comes second — never the other way around.

  • Dozens of small glowing spheres packed into one dark tray gliding along a rail, a few lone spheres trailing behind.

    Batch orchestration + live savings

    Latency-tolerant work is folded into provider batch lanes for up to 50% off, and a real-time dashboard shows exactly what you saved — per model, per provider, per route — with the naive-versus-actual cost on every single request.

The request path

Five gates, before a cent is spent.

This is the real order the gateway evaluates. Every decision is recorded with the reason it was made and a decision id you can look up.

Processing now—req/s
Past 24 hours—
Past 30 days—
Gateway latency—ms p50

Cached — never reached a modelRouted — sent to a cheaper modelSent as asked — full priceHeld — stopped by your rules

Held is red because something was refused — by you. A held request stopped at one of two gates: abudget or rate ceiling you set, or a governance rule — DLP, a policy, a model you have not approved. Either way it was never sent and never billed. These are your rules, not ours: Tessarac enforces the limits you configure and never blocks a request on its own judgement.

01

Budget & limits

Your ceilings, enforced in the request path, not reconciled at month end. Nothing overspends quietly.

02

Cache

Two questions worded differently are one question. The second is answered from the first, for nothing.

03

Optimise & route

Trimmed of what the model never needed, then graded. Easy work goes to a small model, hard work does not.

04

Policy & DLP

Your governance rules, applied before a provider sees a token: redacted, refused, or stopped outright.

05

Meter

Priced against the real rate card twice over: what it actually cost, and what the same work would have cost untouched.

Benchmarked vs LiteLLM

The same models. Up to 80% less.

We sent identical prompts through the AI Cost Optimizer and through a LiteLLM gateway running the same premium models, and priced both from one shared price book. LiteLLM load-balances across the deployments you list — it doesn't grade prompt difficulty, route to the cheapest model that can answer a prompt, or verify an answer. Those are exactly the levers that move the bill.

80.6%

lower total cost in auto mode across the benchmark suite

<250ms

to serve a cached answer — versus seconds of regeneration

$0

upstream cost on every request served from cache

Auto-mode savings vs. pinning each model

  • claude-sonnet-4-6−82.3%
  • claude-sonnet-5−76.8%
  • claude-opus-4-8−90.5%
  • claude-opus-5−84.6%

Savings against running that same premium model, pinned, on LiteLLM. Auto mode upgrades only the prompts that need it.

Cost levers, side by side

Difficulty-based model routing—
Answer-adequacy verification—
Full-response cache ($0, no upstream call)~
Same-model cheapest-deployment routing~
Prompt / RAG token trimming—
In-flight request coalescing—

✓ Tessarac  ·  — not offered by LiteLLM  ·  ~ offered by LiteLLM with setup or in a different form

Internal benchmark, Aug 2026 — identical prompts through each gateway; cost computed independently from one shared price book; a cache-served response priced at $0 upstream. Auto-mode routing + caching on Tessarac vs. the same premium models pinned on a LiteLLM gateway. Savings vary by workload and traffic mix.

Gainshare pricing

You only pay a share of what you save.

No seat licenses, no per-token markup, no upfront commitment. The optimizer measures the difference between what each request would have cost at list price and what it actually cost after optimization — then bills a share of the proven savings. If it doesn't save you money, it doesn't cost you anything.

  • Measured, auditable savings — every request carries its naive-versus-actual cost line
  • No savings, no charge — pricing is a share of proven savings and nothing else
  • Self-host or managed — run it inside your own cluster with offline licensing, or use the hosted gateway
  • Secure by construction — cache encrypted at rest, authenticated ingest, and network isolation throughout

Pick your workload, move the slider

What does your traffic actually cost?

Everything below is shown gross and net, our 25% share included.

$60,000/mo
$5K/mo= $720,000 a year in provider spend$400K/mo
Gross saved / mo
$47,100
Our 25% share / mo
$11,775
Net saved / mo
$35,325
Net reduction
58.9%
You'd pay / mo
$24,675
Net saved / year
$423,900

net saved — yours to keepour 25% share of the savingstill paid to your providers

We take 25% of what we actually save you, billed against the measured saving and nothing else. No seat licences, no minimum, and nothing at all in a month where we save you nothing. Every figure above is shown gross and net so the fee is never a surprise line at the bottom of an invoice.

Measured. Agentic and coding traffic barely caches — roughly 1% or lower, because almost every prompt carries a different file, diff or stack trace. Nearly all of this saving is prompt optimisation and routing: cutting what the model never needed to read, then grading the request and sending it to the smallest model that still clears your bar. This is the workload most of our traffic is, so it is the number we stand behind.

Stop overpaying for AI.

A drop-in gateway that cuts your LLM bill on every call — output-preserving, BYOK, and billed only when you save.

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