Pricing

Documentation

Blog

Case Studies

Get Access

Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Error log analysis

Analyze millions of error logs at a fraction of the cost

Stop sifting through unstructured log data. Sutro transforms millions of error logs into structured, actionable insights, helping you identify the root cause and resolve issues in hours, not days.

Get Access

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

Batch LLM Inference is better with Sutro

Run LLM Batch Jobs in Hours, Not Days, at a Fraction of the Cost.

Get Started

Generate a question/answer pair for the following chunk of vLLM documentation

Inputs

Outputs

Intro to vLLM

vLLM is a fast and easy-to-use library for LLM inference and serving. Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.

Loading Models

vLM models can be loaded in two different ways. To pass a loaded model into the vLLM framework for further processing and inference without reloading it from disk or a model hub, first start by generating


Using the Open AI Server

Run:ai Model Streamer is a library to read tensors in concurrency, while streaming it to GPU memory. Further reading can be found in Run:ai Model Streamer Documentation.

vLLM supports loading weights in Safetensors format using the Run:ai Model Streamer. You first need to install vLLM RunAI optional dependency:

Question: Is vLLM compatible with all open-source models? ...

Question: How do I load a custom model from HuggingFace? ...

Question: Can I use the OpenAI compatible server to replace calls...

+128 more…

From Raw Logs to Root Cause, Simplified

Sutro takes the pain away from testing and scaling LLM batch jobs to unblock your most ambitious error analysis projects.

import sutro as so

from pydantic import BaseModel

class ReviewClassifier(BaseModel):

sentiment: str

user_reviews = '.

User_reviews.csv

User_reviews-1.csv

User_reviews-2.csv

User_reviews-3.csv

system_prompt = 'Classify the review as positive, neutral, or negative.'

results = so.infer(user_reviews, system_prompt, output_schema=ReviewClassifier)

Progress: 1% | 1/514,879 | Input tokens processed: 0.41m, Tokens generated: 591k

█░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░

Prototype

Start small and iterate fast on your log analysis workflows. Accelerate experiments by testing on Sutro before committing to large jobs.

Scale

Scale your LLM workflows to process billions of tokens from your logs in hours, not days, with no infrastructure headaches or exploding costs.

Integrate

Seamlessly connect Sutro to your existing LLM workflows. Sutro's Python SDK is compatible with popular data orchestration tools, like Airflow and Dagster.

Resolve issues faster

Shorten development cycles by getting feedback from massive log files in minutes. Rapidly prototype analysis prompts before scaling up.

Slash analysis costs

Slash analysis costs

Slash analysis costs

Get results faster and reduce costs by 10x or more by parallelizing your LLM calls through Sutro to analyze logs.

Scale your monitoring effortlessly

Confidently handle millions of log entries and billions of tokens at a time without the pain of managing infrastructure.

Website data extraction

Longer description goes here, should span multiple lines.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

LLM performance evaluation

Benchmark your LLM outputs to continuously improve workflows, agents and assistants, or easily evaluate custom models against a new use-case.

Sentiment analysis

Automatically organize your data into meaningful categories without involving your ML engineer.

RAG data preparation

Improve your LLM or RAG retrieval performance with synthetic data to fill statistical gaps.

Document summarization

Easily sift through thousands of product reviews and unlock valuable product insights while brewing your morning coffee.

Website data extraction

Longer description goes here, should span multiple lines.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

LLM performance evaluation

Benchmark your LLM outputs to continuously improve workflows, agents and assistants, or easily evaluate custom models against a new use-case.

Sentiment analysis

Automatically organize your data into meaningful categories without involving your ML engineer.

RAG data preparation

Improve your LLM or RAG retrieval performance with synthetic data to fill statistical gaps.

Document summarization

Easily sift through thousands of product reviews and unlock valuable product insights while brewing your morning coffee.

Website data extraction

Longer description goes here, should span multiple lines.

Structured Extraction

Transform unstructured data into structured insights that drive business decisions.

LLM performance evaluation

Benchmark your LLM outputs to continuously improve workflows, agents and assistants, or easily evaluate custom models against a new use-case.

Sentiment analysis

Automatically organize your data into meaningful categories without involving your ML engineer.

RAG data preparation

Improve your LLM or RAG retrieval performance with synthetic data to fill statistical gaps.

Document summarization

Easily sift through thousands of product reviews and unlock valuable product insights while brewing your morning coffee.

FAQ

What is Sutro?

What kinds of tasks can I perform with Sutro?

How does Sutro reduce costs?

Can I use my existing data tools with Sutro?

How can I test a workflow before committing to a large job?

What Will You Scale with Sutro?

Get Access

Blog

Privacy Policy

Terms of Service

Documentation

Docs

team@sutro.sh