⏱️WHN Compute Transparency

Statsig Product Updates
< All updates
10/29/2024

Vineeth Madhusudanan

Product Manager, Statsig

Warehouse Native Compute Transparency Dashboard

Statsig Warehouse Native now lets you get a birds eye view across the compute time experiment analysis incurs in your warehouse. Break this down by experiment, metric source or type of query to find what to optimize.

Common customers we've designed the dashboard to be able to address include

  • What Metric Sources take the most compute time (useful to focus optimization effort here; see tips here)

  • What is the split of compute time between full loads vs incremental loads vs custom queries?

  • How is compute time distributed across experiments? (useful to make sure value realized and compute costs incurred are roughly aligned)

You can find this dashboard in the Left Nav under Analytics -> Dashboards -> Pipeline Overview

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This is built using Statsig Product Analytics - you can customize any of these charts, or build new ones yourself. A favorite is to add in your average compute cost, so you can turn slot time per experiment into $ cost per experiment.


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OpenAI OpenAI
Brex Brex
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Ancestry Ancestry
At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities.
OpenAI
Dave Cummings
Engineering Manager, ChatGPT
Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly.
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Karandeep Anand
President
At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It’s also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us.
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Mengying Li
Data Science Manager
We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion.
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SVP, Data & Platform Engineering
We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig.
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Partha Sarathi
Director of Engineering
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