Baseten never picks the model for you ’s job is picking so you don’t have to

On Baseten you choose the model every time. ML.ai picks it per request, automatically, and that choice is where the savings come from.

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Baseten, model serving

How Baseten works

Choose a modelYou name the exact slug

Deploy itTruss package on GPUs you choose

Wait for the cold start30 to 90 s for large models

Serve itBilled per GPU minute

ML.ai ML.ai picks the model per request

main.py

1client = Client(base_url=MLAI_URL)

2r = client.chat.completions.create(

3 model="auto",

4 messages=msgs)

5# scored on cost and quality per call

Routing decision model=auto

Fix this failing test

Tuned open model

Mid-size open modelskipped

Frontier modelskipped

Cleared the quality bar

Who picks the model: watch the same request hit both

On Baseten, a person picks, deploys and waits for a model before anything runs. On ML.ai, the request is scored once and lands on a live model.

Same request, both products

“Fix this failing test”

Baseten

1Choose a model
open-70b-instruct
2Deploy it
GPU you chooseTruss package
3Wait
Cold start30 to 90 s

Deploying…
Request waits on deployment

ML.ai

1Optimizes for cost + quality
Tuned open modelPicked
Mid-size open modelSkipped
Frontier modelSkipped
2Already running
Tuned routeLive

No deploy step, running from step one

What and Baseten each do

serve.py

1client = Client(base_url=BASETEN_BASE_URL)

2resp = client.chat.completions.create(

3 model="open-70b-instruct",# you name it, every time

4 messages=[{"role": "user", "content": task}])

Baseten

A model-serving platform where you call a hosted model by its exact slug or deploy your own onto GPUs you choose. A person picks every model in advance.

route.py

1client = Client(base_url=MLAI_BASE_URL)

2resp = client.chat.completions.create(

3 model="auto",# scored per request

4 messages=[{"role": "user", "content": task}])

ML.ai routes each request across 40+ models behind one endpoint, picking the cheapest one that meets the quality bar.

Which one fits your workload

Serve a model you chose

Baseten

Use this if

You’ve already decided which exact model should run, and the problem left is serving it reliably: packaging, GPU allocation, autoscaling, uptime.

Let the router choose

Use this if

You want the model choice made automatically, per task, because picking the right model every time is the real cost problem, not hosting one.

see how ML.ai compares to Factory.ai on autonomy

Every claim, side by side

The same categories, compared row by row. Every line on both sides traces back to a real doc, pricing page, or published benchmark.

Category

What it is

Baseten

A model-serving platform with no automatic selection: you name the model you want, every time, whether calling a Model API or a model you’ve deployed.

ML.ai

An automatic, per-task router in front of 40+ models under one drop-in API endpoint.

Nobody names the model per request.

Baseten

Truss (open-source, MIT) packages a model’s code, dependencies, and hardware needs; deployed via the Baseten CLI.

ML.ai

Not applicable.

ML.ai routes to hosted models rather than packaging your own for deployment.

Operating model

Baseten

You do, always.

Model APIs require you to name an exact model slug; Chains requires you to write the Python code that decides which deployed model handles which step.

ML.ai

ML.ai’s router does, automatically, scored against cost and quality for that specific task.

No code is written per request.

Baseten

Per-token for shared Model APIs; per-GPU-minute for dedicated deployments (T4 through B200), billed even while a replica is warm but idle.

ML.ai

Usage-based on top of a workspace plan; a completion fee applies only to spend past the plan’s included execution credit.

read the full ML.ai pricing breakdown

Baseten

Scale-to-zero is supported, with a 15-minute idle delay before shutdown; a cold start on the next request runs roughly 30-90 seconds for large models, and billing runs per minute during that startup window even before it’s serving.

ML.ai

Not applicable in the same sense.

Requests route to already-hosted models rather than a replica you scaled to zero.

Compliance & deployment

Baseten

SOC 2 Type II and HIPAA compliant, with an independent third-party HIPAA assessment report available.

ML.ai

SOC 2 Type II, ISO 27001, GDPR + CCPA, HIPAA on request, PCI DSS on dedicated capacity.

Baseten

Three self-serve-documented modes: fully managed Baseten Cloud, single-tenant VPC, or fully self-hosted in your own cloud account.

ML.ai

Enterprise tier: BYOK, VPC, or on-prem, with data residency, currently pilot-only, arranged with sales rather than self-serve.

Who it fits

Baseten

ML/infra engineers who already know which model to run and need production-grade serving without owning Kubernetes or Docker directly.

ML.ai

Teams that want the model choice made for them, automatically, because the cost problem is picking right every time, not hosting.

Where each one falls short

ML.ai’s own limits sit right beside theirs, with the same weight.

Where Baseten is limited

Where is limited

Pricing mixes per-token and per-GPU-minute billing, which third-party reviews describe as hard to forecast.

A dedicated replica accrues cost whether or not it’s actively serving.

ML.ai is currently a sales-assisted, 30-day proof-first pilot, not a self-serve signup.

There is no public API you can start calling today the way you would with a Model API.

No business-user layer: every documented workflow (config.yaml, Truss, CLI, Docker) assumes engineering fluency, not a no-code dashboard.

The 30-45% cost reduction is a design target ahead of general availability, not a measured average across customers yet.

The benchmark figures (95.0% on SWE-bench Verified) are measured, but on the ML.ai coding agent in our own runs, not on a customer deployment.

At sustained high throughput, the per-replica markup over raw GPU rental means renting GPUs directly can undercut Baseten’s dedicated-deployment pricing, per third-party comparisons.

No public GPU, region, or autoscaling documentation exists yet for ML.ai.

That operational detail is the kind of thing Baseten documents in depth and ML.ai does not, today.

or compare ML.ai to Entelligence.ai’s routing model

FAQs

Does Baseten pick the cheapest model for a task automatically, the way ML.ai does?

No. Every path on Baseten (Model APIs, dedicated deployments, Chains) requires a person to name or deploy the exact model in advance. ML.ai’s automatic, cost-based selection has no equivalent on Baseten today.

Can I run ML.ai and Baseten together?

Not today. There is no documented, supported way to add a Baseten-hosted model as a destination ML.ai’s router can choose. They’re alternatives at the same layer, not a connected pair.

Which one do I need if I already have a fine-tuned model to deploy?

That’s Baseten’s core use case. Truss and the Baseten CLI exist specifically to package and serve a model you’ve already built. ML.ai doesn’t do that job.

Is ML.ai available today the way Baseten is?

Not in the same self-serve form. ML.ai is currently run as a sales-assisted pilot. Baseten’s Model APIs and dedicated deployments are self-serve today.

Try today, or talk to us about what is next

Try ML.ai in the editor you use today and approve every change it makes.

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