ML.ai

ML.ai Inference learns your workload and routes it right

Each request goes to the most efficient model, frontier or tuned, that clears your quality bar. Nothing ships until it’s verified.

Start a pilotBook a call
Lower cost per request
30–45%
Quality, never traded away
≥ baseline
Faster time to response
15–40%

Your requestClassifying

Summarize this support request: my order arrived damaged and I need a replacement.

  1. Classify
  2. Route
  3. Verify

Cheapest route that clears your bar

  • Frontier modelScoring$0.18
  • Standard routeScoring$0.10
  • Tuned modelScoring$0.06

Waiting on the verdict

Trusted by leading teams

Pepsi, Heineken, American Express, FanDuel, adidas, McDonald's, Nissan, BT Group, NeoSapien, McKesson, American Electric Power, A. O. Smith, AWS, Tata Motors, Stellantis, Iron Mountain, Suntory, DISH Network.

Inference sits between your app and the model stack

Every step gets routed to whichever model clears your acceptance bar for the least cost, and nothing ships until it's verified against that bar.

Your application stays yours: no rewrite, no new SDK. ML.ai only takes on the three jobs in the middle of the stack.

  • RouteMatch effort to the task
  • VerifyHold the acceptance bar
  • LearnTune what repeats
Read the docs

A tuned model that gets better the more your workload runs

Run it in shadow, check the results against your acceptance bar, then move traffic once it clears that bar. The route keeps improving after that too.

We mirror your traffic first

Your provider keeps serving traffic while ML.ai shadows a copy to measure cost and quality.

Nothing about your production path changes yet.

Every request earns its own decision

Nothing ships on a hunch. Each request is routed, checked and logged against rules your team set.

Routing that matches effort to the task

A support summary and a checkout bug don't need the same model. ML.ai reads the request and sends it to the cheapest route that can still handle it.

Nothing ships until it clears your bar

New routes run against your accepted baseline first. Only the ones that meet it get to serve real traffic.

Your data rules, applied before routing

Redaction, retention and access boundaries run at the input, not as an afterthought once the request has already left.

A trace for every answer, not a black box

See which route it took, what checks it passed, and why, down to the individual request.

Most of your traffic doesn't need your best model

ML.ai finds the requests that can run cheaper and moves them, without touching the ones that can't.

30–45%Typical cost reduction

Routine requests go to a cheaper route. Hard ones still get the frontier model

Both are checked against the same acceptance bar before anything ships.

Before routing$0.18 / request
With ML.ai$0.10 / request
  • Routine work · standard model≈70%
  • Complex work · frontier model≈30%

  • 30–45%

    Lower cost per request

    Reserve the frontier model for the work that actually needs it.

  • ≥ baseline

    Quality, never traded away

    Every cheaper route still has to clear your acceptance bar first.

  • 15–40%

    Faster time to response

    Lighter routes finish quicker, so simple requests come back sooner.

What could your workload save?

Your workload sets the starting point. Your pilot establishes the result.

NeoSapien

42%lower monthly AI bill, zero drop in quality

NeoSapien runs a voice-first assistant with five moving parts. ML.ai took over the routine ones and kept the hardest on the best models.

  • 28%

    Faster responses

  • 21 days

    To full rollout

  • 0

    Quality regressions

“We stopped picking models one by one. ML.ai learned the ones we needed and ran them cheaper than we could.”

Aryan YadavCo-Founder & CTO, NeoSapien
$50,000

$5,000$250,000

You save (30–45%)

$15,000 – $22,500

$180,000 – $270,000 per year

Start saving $15,000/mo

Control follows every request

Keep routing within the boundaries you define.

  • Input PII controls
  • Prompt-injection checks
  • Output moderation
  • PII egress & audit trail

Private deployment is scoped with the ML.ai team. The globe is a conceptual routing illustration, not a service-region map.

The same routing principle, three different workloads

The task determines the effort in each of the examples below.

Understand the request

Classify and summarize a support message into a clear next step.

Make information usable

Return clean fields that can be checked against a defined schema.

Reason through complexity

Use a higher-effort route when the problem needs more reasoning.

Pick one workload.Prove it in thirty days

Cost, quality and rollout targets are agreed up front, before a single real request moves.

  1. Week 01

    01

    Baseline

    Instrument your current workload and log its cost, latency and quality as-is.

    A number to beat

  2. Week 02

    02

    Targets

    Set the cost, quality and rollout numbers that would make this a pass.

    Pass or fail, agreed in advance

  3. Week 03

    03

    Live slice

    Route a small share of real traffic through ML.ai and compare it against baseline.

    Real traffic, not a demo

  4. Week 04

    04

    Go / no-go

    Check the live slice against your targets and decide whether to roll out.

    Your data, not our pitch

Discuss a pilot

Enterprise security with fine-grained controls

  • SOC 2

  • ISO 27001

  • FedRAMP High

  • Inputs and outputs are always your IP
  • Zero data retention enforced with model providers
  • Comprehensive audit logs
  • Every session runs in its own VM
  • RBAC / SSO / SCIM / OIDC
  • Private networking available

FAQs

You stay in control, and ML.ai has to earn the business.

Is Inference the same thing as ML.ai?

ML.ai is the company. Inference is this specific product: the router that watches your traffic, learns it, and shifts work to cheaper models once they pass your quality bar. Say "ML.ai" when you mean the company, "Inference" when you mean this router.

Is the demo on this page hitting a real model?

No, it's a mockup. Nothing on this page calls an API, and no key is required. It's there to show you what the routing and the trace look like, not to run your prompt.

Will I need to rewrite how my app talks to models?

No. Shadow mode runs on top of your existing calls and SDK, so nothing changes in your code until you decide to move traffic. Where the production handoff plugs in gets worked out with your team during the pilot.

Can I pick the exact model behind a route?

Not directly. You pick a tier, Standard or High, and ML.ai decides which model handles it and swaps it if a better one becomes available. That's the point: you stop babysitting model choice.

What if the savings don't show up for my workload?

Then you don't pay for a route that isn't earning its keep. The 30–45% figure is a target we design toward, not a promise, and the pilot exists specifically to test it against your real traffic before you commit to anything.

Bring the workload.Let the evidence decide

Run your workload through ML.ai Inference. See the routing, the guardrails and the bill before you commit.

Start a pilot