Your decisions, running in production.

Sutro is a platform for building, optimizing, and running reliable AI Functions.

Upload an unlabeled dataset, add your feedback on hard cases, and Sutro engineers a prompt that reliably executes your task using off-the-shelf models.

app.sutro.sh/functions/aviation-classifier
Prompt
Is this hacker news post related to aviation?
Example 1 of 5

The Department of Defense Is Gearing Up for a World War Entirely in Space

Is this related to aviation?

What is an AI Function?

A reliable AI Function repeatedly and consistently makes the same decisions as a human expert would on a given task. Below are examples of AI Functions our customers are building.

01

Classifier

Score a lead based on your team’s criteria.

02

Judge

Give pass or fail assignments on agent traces.

03

Extractor

Remove personally identifiable information from medical records.

04

Entity resolver

Determine whether two businesses are the same.

05

Matcher

Determine whether a candidate is a good fit for a role.

06

Router

Send support tickets to the right team or person.

Building an AI Function isn’t about scaling general intelligence. It’s about teaching a model exactly how your organization wants a task performed.

How are AI Functions run?

AI Functions can be run continuously as new inputs arrive, or in batch mode for large datasets.

Event streamProcess inputs as they arrive
ƒ
Batch processingMap many input rows to many output rows Cost-efficient at scale
INPUT ROWS OUTPUT ROWS ƒ ƒ ƒ ƒ ƒ

In production today

Sutro is powering critical, production AI workloads with many happy customers today. Sutro processes millions of decisions, and billions of tokens per month, often in environments with strict data privacy and security requirements.

01

Applied AI teams

Agents, evals, matchers, and other production AI workflows.

02

Data & ML engineers

Data enrichment, entity resolution, classification, and extraction.

03

Training data providers

Data filtering, labeling, tagging, and dataset preparation.

“Sutro saves our team countless hours, and it gives us the invaluable ability to measure, optimize, and prevent regressions against our domain expertise. It’s a must-have for serious AI developers.”

CTO Series B AI Data Marketplace
Request customer case studies →

Simple pricing

Platform access starts at

$500 / month

(includes $100/mo inference credits)

Enterprise and self-hosted plans are tailored to your needs and scale.

See full pricing →

Batch inference

Machine-time pricing means you only pay for the compute your workload actually consumes. Sutro is surprisingly affordable for large datasets.

Estimated price $10.00
Number of records 1 million

Frequently asked

Is this an evals product?

Yes and no. Sutro helps evaluate quality and reliability, but unlike other evals products, it directly influences model/agent behavior from feedback.

Can Sutro use image inputs and other modalities? Can it call tools?

Yes. Sutro supports images, PDFs, and web search capabilities today. Additional modalities and custom tool calling are available on a per-request basis.

What models does Sutro Functions use?

Functions are typically model-agnostic, and we support a mix of open-source and proprietary models. It's common that small-open source models outperform larger proprietary models when building AI Functions. Sutro helps automatically find the best model for your needs.

Can I self-host Sutro? Do you support BYOC and BYOK?

Yes. Sutro can be managed as SaaS or entirely self-hosted. You can bring your own provider keys and cloud credentials in either case.

How does Sutro compare to prompt engineering, fine-tuning, or RL?

AI Functions accumulate edge cases by nature of the volume they run at and the diversity of possible inputs. Sutro systematically discovers these edge cases and learns generalized rules optimized to cover as many of them as possible. Manual prompt engineering is typically not scalable in these scenarios.

Sutro Functions are designed to be extremely data efficient and portable. Functions learn in-context, meaning no weight updates are needed and can easily be updated. Fine-tuning and reinforement learning typically require more data and require retraining for updates, making them more rigid and less portable.

Scale your expert decisions with Sutro Functions today.