Everyone wants to move fast, but not everyone knows how. It sounds simple: automate more, release often, catch problems earlier. But in practice, it’s complicated. Speed is fragile. It depends on hundreds of small things working together such as your CI, your tests, your telemetry, your rollback path, and how much trust your team has in all of it.
Over the years I’ve worked on large systems at Microsoft, Google, Salesforce, Tableau, and T-Mobile, and on smaller teams where everything was built from scratch. Earlier, at the Institute for Disease Modeling, we ran large-scale epidemiological simulations on HPC clusters, basically supercomputers, before moving workloads to the cloud. Whether it was data pipelines, developer platforms, or consumer products, the challenge was always the same: how do you move fast without breaking everything?
Releasing fast isn’t about typing faster or skipping QA. It’s about shortening the distance between writing a line of code and knowing it’s safe in production. It’s about how quickly you can detect a problem, roll back, and try again. The best teams aren’t fearless. They’re steady because they’ve built systems that make it safe to move.
At Microsoft, when we shipped new features for Bing, we started with 0.3 percent of traffic. That sounds tiny, but at that scale it was plenty with millions of queries a day, enough to see real signal without risking the system. At Google, on Gemini’s developer tools, we sometimes began closer to ten percent. The numbers weren’t sacred; they were dictated by confidence. Some launches went from 0.3 to 100 percent in a single day. Others crept from 0.3 to 25 to 100 over a few weeks. The pace wasn’t set by process; it was set by how long it took for telemetry to appear on dashboards and for us to trust what we saw. At that size, data can take a day or two to roll in. You move at the speed of truth.
Startups don’t have that problem. They see what happens right away - a spike in errors, a message from a user, or a Slack alert. That closeness is their advantage. When feedback is instant, you don’t need ceremony. You just fix it.
Speed isn’t something a CI system gives you. It’s something you build around it. You can use GitHub Actions, Buildkite, Jenkins, whatever you want. The tool doesn’t matter as much as having a workflow that actually works for your team. What matters is that you can trust the system, that it runs the right checks, and that you can reproduce most of it locally before you even push a commit. Docker helps with that. If your local setup behaves like your CI environment, you save yourself an entire cycle of guesswork.
Back when I worked on MSN, our build used to take twenty-four hours. A single bad check-in could cost everyone a full day. That’s why "don’t break the build" was almost a religion. Some teams would even make you wear a funny hat or post your name on a board if you did. It sounds childish, but the idea made sense: when one person slows the whole team, everyone feels it. The wall of shame wasn’t the point. The point was that catching issues early helps everyone move faster.
A good release isn’t one that looks fancy on a dashboard. It’s one that happens quietly, automatically, without anyone hovering over it. You write code, you test it locally, you push it, and the system takes it from there. It runs lint, type checks, and deeper tests you can’t afford to run locally, and if everything’s green, it ships. You shouldn’t need a meeting for that.
Most of the pain in releasing fast comes from bad tests and slow pipelines. I’ve seen test suites hang for hours, flaky tests that waste days, and dependencies between teams where your build breaks because someone else’s tests fail. Whether it’s culture, tooling, or just a black-box pipeline, the result is the same: time lost and trust eroded. Logging and telemetry matter as much for your build system as they do for production. You need to see how long each stage takes, where the bottlenecks are, and which tests are dragging you down. There’s usually an easy win hiding there. Not everything can be shortened, but you can parallelize, split workloads across containers, or move slow integration tests to a nightly run. Any test that takes more than a few seconds is worth questioning.
Tests that run in watch mode or trigger on file changes are a huge help. When type checks or lint errors show up while you’re still coding, you fix them immediately instead of waiting for CI to fail later. That’s the idea behind "shift left." If you picture the entire software process as a line, code on the left, production on the right, then shift left just means catching problems earlier in that line. The further right an issue gets, the more expensive it becomes to fix. Finding something during coding or pre-commit takes seconds. Finding it after deployment can take hours, with more people involved. The goal is to push every form of feedback as far left as possible, where it’s still cheap and fast to act on it.
Speed isn’t just how fast you can push code; it’s how quickly you can know if something went wrong. Every release should tell you what happened, not make you guess. After a deploy, you check staging first. There should be zero errors, all tests passing, and no unexplained warnings. If there’s a warning, it should already be tied to a bug so whoever looks at it knows it’s known and being worked on. That kind of traceability matters when hundreds of people are touching the same system.
You also watch the basics: latency, crash rates, availability are the things that actually matter to users. For search results, we targeted under 300 milliseconds end-to-end. Each downstream dependency had its own smaller budget: 100 ms here, 200 ms there. In a distributed system it’s like a train passing through stations. Each service has a small window to respond. If it misses that window, you move on, unless it’s critical. At Bing, for example, search results had to arrive, but ads or side answers could be skipped if they were late. It kept the system responsive without blocking on slower dependencies.
You need alerts wired into all of this: Slack, email, text, pager, whatever your setup is. The best systems tell you before the user does.
Good dashboards are underrated. Bad ones slow you down because you can’t tell what broke. Great ones let you glance, see the problem, and move on. The goal is to automate as much of that feedback as possible so humans aren’t the bottleneck. When your deploys can move from check-in to staging to production automatically, with telemetry watching the path the whole way, that’s when shipping becomes easy.
Having a solid rollback mechanism is just as important as releasing. You can’t move fast if recovery is slow. Blue-green deployments, feature flags, and one-click rollbacks make mistakes survivable. The faster you can undo something, the braver you can be about shipping again. Every system should have a clear escape hatch. Rollback shouldn’t require a war room.
Releasing fast isn’t just a technical problem; it’s a hygiene problem. Every flag, every flaky test, every commented-out block of code adds friction. At Google we deleted old feature flags about two weeks after a release. Flaky tests were tracked, tagged, and fixed after three failures. Each one got a bug filed automatically, and on-call engineers were expected to watch them. At smaller startups I’ve kept the same principle even if the tooling was lighter: fix or delete, but don’t ignore. Every ignored failure is debt with interest.
There’s a misconception that moving fast means writing sloppy code. It’s the opposite. The only way to move fast for long is to have good habits. You can hack your way to a few quick releases, but you can’t sustain it without discipline. Clean code, reliable tests, clear ownership is what speed is built on.
When a team really learns to release fast, it doesn’t feel like speed anymore. It feels calm. No adrenaline, no late-night deploy drama, no heroics. You merge, the pipeline runs, the telemetry lights up, and you go back to work. The system takes care of you because you’ve taken care of it. That’s the quiet truth about shipping fast. It’s not about risk or bravado. It’s about trust, in your process, in your code, and in each other.