Run builds and tests like a production service

EngFlow is Build Management at AI Scale: operating builds and tests like production infrastructure.

Build and test infrastructure was already under pressure. AI increases the rate of change and brings the operating wall forward: more code, more builds, more tests, more artifacts, more failures, more infrastructure demand. The best engineering teams can assemble powerful build and test infrastructure. The hard part is operating it reliably, with visibility and cost control, as demand grows. EngFlow operates it for you: execution, observability, reliability, and cost control, across Bazel, CMake, and Buck2.

Trusted by teams who can’t afford build downtime

AI brings scale forward

Coding is the dominant enterprise AI use case by nearly an order of magnitude (a16z, April 2026).

AI increases change velocity and build volume. More diffs, branches, tests, edge cases, and artifacts. More “contributors”—humans and agents—touching your repo and triggering builds. The platform that “mostly works” under moderate load becomes a single point of failure at enterprise concurrency.

Build Management at AI Scale is now a platform requirement, not an optimization project.

What breaks when builds cannot scale

Most organizations try to solve slow builds by adding CI runners, larger machines, or caching tweaks. That helps until the failure modes change:

Remote execution/caching outages block engineering
Upgrades and config changes become high-risk events
Flakiness and nondeterminism erode trust in CI signals
Costs spike unpredictably as build volume rises
Debugging becomes archaeology: “Why did this fail?” is hard to answer fast

These aren’t performance problems. They’re management problems.

What Build Management at AI Scale means

Build Management at AI Scale means operating build and test as production infrastructure: execution, telemetry, forensics, reliability, and cost control. It is the operating model for build and test, so organizations can safely build, test, diagnose, and release code even when they are writing at AI speed.

It is not CI/CD (workflow orchestration). It is not a cache (a component). It is not Bazel hosting. It is how you run the build platform as a service.

Outcomes you can measure

Speed is what you notice first.
Certainty is what you buy.

But the strategic win is operational: fewer platform incidents, safer upgrades, and faster root cause when something goes wrong—because the system is managed, not improvised.

Snowflake (50% AWS costs save)

AWS costs for build infrastructure cut by nearly half

17M builds a week

Selenium (10x)

From 5 hours to 30 minutes

10× faster

Brave (8x)

Builds sped up from hours to under 15 minutes

8× faster

Replay.io (4x)

4-hour builds reduced to under an hour

4× faster

Viasat (24x)

24x faster builds and tests, from 24 hours to 1 hour

24× faster

How EngFlow operates builds and tests at AI scale

Execute builds and tests remotely across a cluster to eliminate workstation/runner bottlenecks.

Cache results so redundant work disappears across developers and CI.

Capture build data centrally so failures become explainable and patterns become visible. The Build and Test UI records every invocation, local or remote, and supports analysis across invocations.

Operate the platform with production discipline: make scaling, upgrades, and onboarding routine.

"EngFlow speeds up builds by 8×, from hours to even under 15 minutes, regardless of geographic location."

Mihai Plesa

DevOps Manager, Brave Software

The EngFlow platform

One platform for scaling builds and tests: remote execution, remote caching, build and test observability, and CI runners, operated as production infrastructure.

Remote Execution

Turn build/test execution into a scalable service. Distribute work across a cluster; keep engineers in flow even under heavy concurrency.

Build and Test UI

Build and Test UI (build forensics). Make build failures explainable. Inspect invocations, search across builds, and analyze trends so teams debug faster and prevent repeat incidents.

Remote Caching

Stop rebuilding what your org has already built. Reduce redundant compute and stabilize iteration time as repos and artifacts grow.

Bazel Invocation Analyzer

Bazel Invocation Analyzer (free, open source). Get concrete recommendations from your Bazel profile, in the web UI or the CLI.

CI Runners

Start every GitHub Actions or Buildkite job with a warm Bazel server and warm caches. In EngFlow’s benchmark, time to first action dropped from about 3 minutes to about 30 seconds.

Built for real infrastructure

Whether you’re standardizing on Bazel or supporting mixed build systems, EngFlow is designed for for platform teams who need predictable operations, not heroics.

Heterogeneous Environments

Support for diverse hardware, operating systems, and toolchains within the same cluster.

Enterprise Security

SOC 2 Type 2 audited. SAML authentication, closed-network operation, SBOM generation, customer-managed encryption keys. Deploy to your cloud, an EngFlow-managed cloud, or on-prem.

Global Scale

High performance at scale. Clusters that scale from one core to more than 100,000, and a fleet of over 1 million cores managed by EngFlow.

Over 3 billion build and test actions and 50 million build invocations each week.

Built by build experts

EngFlow’s team includes sixteen people who helped create Bazel at Google and continue to contribute to it.

Ulf Adams (CTO, Co-Founder)
Led the development and open-sourcing of Bazel at Google.

Helen Altshuler (CEO, Co-Founder)
Led Bazel customer adoption and co-created BazelCon.

“If builds and tests are becoming shared, business-critical infrastructure in your organization, you’re already in the Build Management at AI Scale category. The question is whether you’ll operate it, or keep hoping it holds.”