Move traffic from frontier to local. Only once it's earned the trust. Lakuna maps your real workloads against local model candidates, proves quality with deterministic checks, then progressively migrates qualified traffic away from expensive frontier calls.

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The Routing

Every request
takes a side

Qualified work runs on local models. Everything still under evaluation stays on the frontier until the evidence says otherwise.

INCOMING REQUESTS LAKUNA ROUTER LOCAL MODELS ONLY WHEN QUALIFIED FRONTIER MODELS BASELINE EVIDENCE FEEDS BACK
Process

How Lakuna works

01

Start on frontier

All traffic begins on the frontier model. Nothing moves until it's proven safe to move.

02

Map real workloads

Work-types start from a benchmark seed, then re-form around your actual traffic.

03

Qualify candidates

Deterministic verification — exact-answer scoring, same result every run — scores each model against each cluster. A/B auditions extend it where answers aren’t checkable.

04

Migrate traffic

Only clusters that clear the quality bar move to local models — progressively, and measured the whole way.

Try It

What could you
stop paying for?

Pick your setup and see which local model could take that work off the frontier.

I am a , I want to , and I also need to .

Frontier — today

—

Cost / 1M tokens
—
Cost for the firm
—
With Lakuna

—

Cost / 1M tokens
—
Cost for the firm
—
—lower cost per year
Heldto the quality bar you set
—of this work runs locally

Illustrative estimate — assumes a 500-person firm and a $9.00 / 1M blended frontier rate. Example figures for exploring the idea, not measured customer results.

The Market

Model-maxxing is
expensive by default

AI spend is accelerating, and production traffic stays concentrated on the frontier — even for the work that doesn't need it.

$37B Enterprise GenAI spend in 2025, up from $11.5B — a 3.2× increase in one year.
88% Of enterprise LLM API usage stays with three providers: OpenAI, Anthropic, Google.
75% Of usage is guidance, information, and writing — not frontier-research-grade problems.

Sources: Menlo Ventures 2025 Enterprise GenAI Report · OpenAI, "How people are using ChatGPT," 2025

Rationale

Why this, why now

Routing research shows complementary models beat any single strongest model. Lakuna packages the measurement-and-transition system that serious teams currently stitch together by hand.

Trust before traffic

No traffic moves to a local model until the matrix says it's earned it.

Cheap to maintain

Only affected cells get invalidated when a model, prompt, or runtime changes.

A learning loop

The matrix shows exactly where local models fall short — pointing straight at what to train next.

Early access

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