Let's face it - killing features is hard. You've poured time and resources into building something, only to watch it slowly gather dust while your team debates whether it's worth keeping around.
But here's the thing: holding onto underperforming features is like keeping that treadmill in your bedroom that's become an expensive clothes hanger. It takes up space, requires maintenance, and prevents you from building something your users actually want. The trick is knowing when to pull the plug - and doing it in a way that doesn't alienate the handful of users who still love it.
The writing's usually on the wall long before anyone wants to admit it. Your user engagement data starts telling a story nobody wants to hear: daily active users are dropping, retention rates are tanking, and that feature you launched with fanfare six months ago? People spend about 3 seconds on it before bouncing.
The real kicker is the resource drain. Every feature you maintain needs ongoing development, testing, and support. When you're spending more time fixing bugs than you have users actually using the feature, it's time for some hard conversations. I've seen teams burn through sprints maintaining features that serve less than 1% of their user base - resources that could've gone toward building something game-changing.
Market shifts happen faster than most product teams can adapt. What seemed innovative last year might be table stakes today - or worse, completely irrelevant. Keep tabs on what your competitors are doing, but more importantly, listen to what your users aren't saying. Silence around a feature is often more telling than complaints.
A/B testing can settle internal debates pretty quickly. Pinterest's engineering team has this down to a science: run a test where some users see the feature and others don't, then watch what happens to your key metrics. If removing the feature doesn't hurt (or heaven forbid, actually improves things), you have your answer.
The tricky part isn't deciding to sunset a feature - it's doing it without burning bridges. You need a solid plan, clear communication, and probably a few alternatives ready to go. Trust me, the users who do rely on that feature will be vocal when you announce the sunset.
Before you make any big moves, you need data that tells the whole story. Performance metrics are your canary in the coal mine - they'll show you when a feature is dragging down your entire system.
Here's what to track:
Resource usage (CPU, memory, database calls)
Response times compared to other features
Error rates and crash reports
Maintenance hours per sprint
Lenny Rachitsky's framework for feature sunset decisions is spot-on here. He suggests creating a simple checklist that weighs usage against maintenance burden - if the scales tip toward burden, it's time to go.
Customer feedback tells you things metrics can't. Sure, only 50 people use that export feature, but if those 50 people are your biggest enterprise clients, you might want to think twice. Dig through support tickets, user reviews, and those angry tweets. As senior PMs on Reddit will tell you, complaints often reveal dependencies you didn't know existed.
Your error logs are a goldmine of deprecation candidates. Look for patterns:
Features that consistently throw errors
Compatibility issues with new updates
Performance degradation over time
Security vulnerabilities that keep popping up
The key is combining all these data points into a story that makes sense. If you're seeing low usage, high maintenance costs, increasing errors, and customer indifference all pointing to the same feature - well, the decision makes itself.
Ripping off the band-aid might feel satisfying, but it's a terrible deprecation strategy. The teams that do this well treat feature sunset like a product launch - with planning, communication, and careful execution.
Start with the data story. Users need to understand why you're making this change, and "because we said so" doesn't cut it. Share the usage stats, explain the resource constraints, and be honest about where you're focusing instead. Transparency builds trust, even when delivering bad news.
Your rollout plan should give users time to adjust:
Announcement phase (30-60 days out): Email users, update docs, post in-app notifications
Warning phase (2-4 weeks): Show deprecation warnings, offer migration tools
Sunset phase: Remove access, but keep data exportable for a grace period
Clean-up phase: Archive code, update documentation, celebrate
The phased approach isn't just about being nice - it's about learning. Each phase gives you feedback on whether you're moving too fast or if users have found workarounds you need to support. We use feature flags at Statsig to gradually roll back access, which lets us monitor impact in real-time and adjust if something goes sideways.
Migration paths are non-negotiable. If users depended on that feature for their workflow, you better have an alternative ready. This might mean building export tools, partnering with third-party solutions, or even keeping a stripped-down version alive for power users.
The work doesn't end when the feature disappears. The first few weeks after sunset are critical for understanding whether you made the right call - and learning how to do it better next time.
Watch your metrics like a hawk. Compare engagement, retention, and support ticket volume to your pre-deprecation baseline. A slight dip is normal as users adjust, but any dramatic drops mean you missed something important in your analysis. Feature sunset done right should feel like cleaning out your garage - suddenly everything else works better.
User feedback post-deprecation falls into three buckets:
The relieved: "Finally! That thing was so confusing"
The adapted: "The new workflow is actually better"
The angry: "You've ruined everything"
Pay special attention to that last group. They'll tell you exactly what use cases you overlooked and what dependencies you missed. Sometimes you'll need to bring a feature back (it happens), but more often you'll find ways to address their needs without resurrecting the whole thing.
Run a team retrospective while memories are fresh. What worked? What caught you off guard? Document everything - from the metrics you wish you'd tracked to the communication channels that worked best. At Statsig, we've built these learnings into our experimentation processes, making each sunset smoother than the last.
The best outcome? Nobody notices. When you successfully sunset a feature, most users adapt without complaint, your metrics improve, and your team has more time to build things people actually want. That's when you know you've nailed it.
Sunsetting features isn't about admitting failure - it's about making room for what's next. The best product teams treat deprecation as a regular part of their workflow, not a crisis to be managed. They use data to spot declining features early, plan thoughtful transitions, and learn from each sunset to do better next time.
If you're looking to level up your deprecation game, start small. Pick one underperforming feature and run through this process. Track everything, over-communicate with users, and give yourself time to learn. You might be surprised how much clarity comes from letting go of what's not working.
Want to dig deeper? Check out Lenny Rachitsky's newsletter for product strategy gold, or explore how teams at Pinterest and Google approach feature lifecycle management. And if you need better data to make these calls, well, that's exactly what we built Statsig for.
Hope you find this useful!