The use case: how Effective AI deploys E2B
Effective AI runs its insurance agents inside isolated E2B sandboxes where they execute code to build actuarial models, spreadsheets and rating engines, and run customer-specific workflows. Sandboxes boot from customer-specific templates and snapshots, and pause and resume supports long-running and recurring sessions.
More reported results
Figures published in E2B’s case study. They are not independently measured by usedby.
Scales beyond 1,000 concurrent E2B sandboxes, supporting complex workflows across hundreds of agents
With E2B, the company supports more than 10,000 user sessions each month.
In the customer’s words
A generic sandbox is useful, but our customers need agents that work inside their world. Templates let us start each workflow with the customer’s tools, dependencies, and context already in place.Other companies using E2B
Customers across industries, with public evidence.
Arena (LMArena)Confirmed
Artificial AnalysisConfirmed
ClickUpConfirmed
GensparkConfirmed
GroqConfirmed
GumloopConfirmedGo straight to the source
Based on E2B’s published case study. We check the public evidence; the results are reported by the vendor.
e2b.dev- Source published
- Aug 5, 2026
- Last checked
- Nov 10, 2026
Open source capture What the story claims, and what we checked
What we compared with the page.
- The figure: 14%Checked
Printed word for word on the page, near the name of Effective AI.
- The passage quoted aboveChecked
Copied word for word from the page, near the name of Effective AI.
- The publication dateChecked
Read from the page’s own metadata, never guessed.
- Effective AI uses E2BChecked
Confirmed line.
- The result itselfNot checked
We quote it; we did not measure it.
By building on E2B, Effective AI avoided creating and operating its own sandbox infrastructure, saving the equivalent of one to two full-time engineering hires.