Espresso
Espresso AI uses ML to optimize Snowflake and Databricks warehouses, cutting costs by up to 70% with pay-per-savings pricing.
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What is Espresso?
Espresso AI is a data warehouse optimization platform that automates performance engineering using machine learning and large language models to reduce Snowflake and Databricks costs by up to 70%. The platform runs autonomous intelligent agents continuously like a 24/7 team of world-class data engineers, optimizing how your data warehouse scales, schedules, and runs queries without requiring manual tuning from your team.
Key features include three specialized agents: the Autoscaling Agent uses predictive models for smarter multicluster scaling per workload, the Scheduling Agent routes queries in real time to reduce idle compute and maximize utilization, and the Query Agent (currently in private preview) optimizes SQL using LLMs with formal verification to preserve correctness. Setup is fast and easy—requiring only one SQL command and some config changes—and the system optimizes transparently in the background after initial setup.
Espresso AI is designed for organizations with significant Snowflake or Databricks spend who want to dramatically increase data warehousing efficiency without sacrificing performance. It's ideal for data engineering teams, CTOs, Heads of Engineering, and anyone responsible for cloud data warehouse costs who wants autopilot cost savings without operational overhead.
Espresso pricing
Pricing model: Paid
Espresso AI charges only on realized savings with no onboarding costs, no minimums, and no commitments. If the platform doesn't save you money, you won't be charged anything. There is a money-back guarantee period that lets you see savings live in production before committing to pay. No specific free tier or paid plan prices are disclosed—pricing is based entirely on a percentage of the savings achieved.
Espresso pros
- Saves up to 70% on Snowflake and Databricks spend
- Fully autonomous agents run 24/7 without manual intervention
- Pay only on realized savings—no onboarding costs
- No minimums or commitments required
- Guaranteed ROI—if no savings, no charge
- Setup takes only 10 minutes with one SQL command
- Zero maintenance required after initial setup
- Optimizes transparently in the background
- Works with all workloads and query sources continuously
- Increases performance while cutting costs
- Predictive autoscaling reduces idle compute
- Real-time query routing maximizes utilization
- LLM-based SQL optimization with formal verification
- No operational overhead for data engineering teams
- Immediate measurable cost savings from day one
Espresso cons
- Query Agent only available in private preview
- Currently focused on Snowflake and Databricks only
- Requires changing database connection config
- Needs one SQL command to run for setup
- No standalone dashboard mentioned for users
- Savings estimate provided only after signup
- Limited to data warehouse optimization use case
- Small team may limit enterprise support capacity
Frequently asked questions about Espresso
How much can I expect to save with Espresso AI?
Espresso AI cuts Snowflake and Databricks bills by as much as 70%. Customers have reported savings of over 50% and even cutting their bill in half. Once you sign up, you'll receive a savings estimate telling you in advance how much you can expect to see.
How accurate are Espresso AI's savings estimates?
The models are continuously calibrated with production Snowflake data to ensure savings numbers are accurate. The best way to judge accuracy is to compare the upfront savings estimate to actual savings in production when Espresso first turns on. Users can also run A/B tests after a few months by shutting Espresso off for a week and comparing actual spend to the savings calculation.
How does Espresso AI track and charge for savings?
The models calculate savings by analyzing workloads one query at a time. When your team makes optimizations, Espresso sees it in the logs—you'll be running fewer queries, faster queries, or using smaller warehouses. This enables accurate understanding of which savings came from Espresso's optimizations versus yours, and charging accordingly.
How long does setup take?
Setup takes only 10 minutes. All you need is running one SQL command and changing some configs. It's significantly easier than hiring 3 data engineers or migrating to a different platform.
Does Espresso AI require maintenance after setup?
No, there's zero maintenance required after the initial setup. It's truly set it and forget it for autopilot cost savings without adding operational overhead for your team. The agents run autonomously and optimize your data warehouses in real-time.
What data warehouses does Espresso AI support?
Espresso AI currently supports Snowflake and Databricks. The first product is a data warehouse optimization platform initially available for Snowflake, and the platform now optimizes both Snowflake and Databricks costs.
Is there a guarantee if Espresso AI doesn't save me money?
Yes, there is guaranteed ROI. Espresso only charges on realized savings—if they don't save you money, they won't charge you a dime. There are also no onboarding costs, no minimums, and no commitments. Additionally, they offer a money-back guarantee period.
How does Espresso AI optimize queries?
Espresso uses a system of intelligent agents targeting specific layers: the Autoscaling Agent uses predictive models on metadata logs for smarter multicluster scaling, the Scheduling Agent routes queries in real time to reduce idle compute, and the Query Agent optimizes SQL using LLMs with formal verification to preserve correctness.
Does Espresso AI work with all my query sources?
Yes, Espresso AI sits between your tools and your Snowflake warehouse, automatically optimizing every query you and your tools send. It provides continuous optimization across all workloads and query sources transparently in the background.
Who is the Espresso AI team?
Espresso AI is built by elite AI and performance engineers from Google, Apple, and MIT. The founders have worked on natural language processing in Google Search, core systems performance engineering in Google Cloud, and early code-LLM research in Google Deepmind. The company has raised over $11 million in funding from investors including Daniel Gross, Nat Friedman, Matt Turck at FirstMark, and industry leaders like Tristan Handy from dbt Labs.