If you can pay the setup cost, Bazel will get you build times ~10s with a warm cache for even massive projects.
For a less complex project (1 programming language, still shipping to all 3 major OSes), with my knowledge and agents I got the bazel conversion done in 2 weeks.
The setup cost for bazel just went down by a lot, and I don't think the industry as a whole is aware of that yet.
Tsgo, oxlint, caching dependencies etc. what linear outlined in their blog post would be more impactful for the average TS project I've worked on.
Even fully cached outputs needs to fetched and read from a remote server[1]. A step n-1 outout fetched from remote cache server need to written to disk and then again read by step n[3] - all disk I/O and network bound operations.
10s may be achievable/realistic goal in the Java/C++ world where Bazel normally seen. In TS eco-system most people would be over the moon to get into ballpark of 1-2m for a decently large monorepo.
We should define Build more clearly here, if you mean running just transpile/compile steps or the full series of steps that includes tests (as the linear post here is talking about). It is hard to see even a small sub-set of a large suite of test that require a virtual DOM or a real browser can run in 10s or less.
[1] Typical for say managed CI setup .
[3] Common run-of-the-mill frontend + backend stacks in different languages etc.
Also, if you have persistent CI workers with a persistent bazel instance, you save on some network roundtrips, but that's obviously harder to set up and make bulletproof.
The final asset/artifact is rarely small either. even best optimized artifacts can be few hundred MB docker image or more commonly multiple image layers running GBs in size .
each step is a network pull then recompute cache if stale and keep going till end .
For the bigger final artifacts, we support using Content Defined Chunking (rolling gear hashing) to only fetch the missing chunks between incremental builds. Binaries executable with stable layout benefits from this quite a lot.
We are definitely not done with all of the improvements here. But since all the major AI labs are using Bazel, we know that the tools can support “Agent Scale”. https://webazel.dev/
Not sure how that would work with building say a docker image, reproducible builds are pretty hard problem to solve, and caching intermediate layers is not always simple or even doable, we typically still need to publish to a registry which is not the cache server.
https://www.youtube.com/watch?v=biYXmAv4Ppk&t=314s should be a good talk to study up on the matter. The speaker is now working at Apple.
Plus, 'with a warm cache' is doing heavy lifting, what's the real cache hit rate for a week of development? Investing in improving the cold build and frequent actions is still important with bazel or any incremental builder.
I'm not sure it's useful to talk about bazel broadly, it's actual performance and behavior comes down to the rules you use. You can configure bazel like turbo/nx and cache tsc/vitest/eslint on each package.json module, and get course cached units that are evicted on every change, or you can use gazelle and target per-file actions which are only invalidated when their dependencies change. But that trades off batching unless you use workers.
And yes, there are a ton of investments going toward Bazel recently to unlock these newer use cases.
I ran an experiment where I migrated a package to bazel then replayed a weeks worth of changes and it saved 20%. That's nothing to scoff at, but not the headline numbers you see after a full hot build.