Launch day gets all the attention. But the first 30 days after launch are where projects either stabilize or spiral. Most of the issues I've fixed in rescue projects weren't bugs that existed at launch — they were bugs that appeared in week 2, when real users did things nobody tested for.
Here's the 30-day plan I run on every client project. If your developer doesn't offer something like this, ask for it.
Week 1 (days 1–7): watch everything
The goal of week 1 is simple: catch what testing missed.
Day 1: production smoke test. Not staging — production. Run the pre-launch checklist items 13, 18, 19, 20, and 23 against the live URL. Verify the payment flow with a real card. Check that error tracking is receiving events, not just configured.
Days 2–3: monitor error volume. The first 48 hours of real traffic will surface issues automated tests never catch. A properly configured error tracker should be filtering noise and alerting on real problems. Check it twice a day.
Days 4–7: collect user friction. Not feature requests — friction. Where do users get stuck? Where do they contact support? Where do they drop off? If you have analytics, look at the funnel. If you don't, ask your first 10 users directly: "What confused you?"
Deliverable: a ranked list of issues found, categorized as critical (blocks usage), important (degrades experience), or minor (cosmetic). Fix critical issues same-day. Important issues go into week 2.
Week 2 (days 8–14): fix and measure
Week 1 was observation. Week 2 is action.
Fix the important issues from week 1. Not feature requests — the friction points. A button that doesn't work on Safari. A form that times out on slow connections. An email that lands in spam. These are the things that lose users in the first week.
Establish performance baselines. Run Lighthouse on the 5 most-visited pages. Record Core Web Vitals from real user data (not lab tests). Record API response times at p50 and p95. These numbers are your baseline — you'll compare against them in month 2, month 6, month 12.
Check server costs against projections. If you estimated €30/month hosting and you're seeing €90 after a week of real traffic, investigate now. Common culprits: unoptimized database queries, missing image optimization, serverless functions that cold-start too often.
Deliverable: all critical and important issues resolved. A baseline document with performance numbers, error rates, and hosting costs.
Week 3 (days 15–21): edge cases and scale
By week 3, the obvious issues are fixed. Now you're looking for what breaks under stress.
Edge cases. What happens when a user submits a form twice? What if they open the app in two tabs? What if they have 500 items instead of the 10 you tested with? What if their name has an apostrophe? These aren't hypothetical — they're the issues that show up at scale.
Load patterns. If your product has predictable traffic spikes (Monday morning for a B2B tool, evenings for a consumer app), monitor those periods specifically. Check that database connections don't max out, that rate limits are set correctly, and that caching is working.
Third-party dependencies. Check uptime and performance of every external service: payment provider, email sender, analytics, CDN. If any of them had an incident during your first two weeks, verify your error handling actually worked.
Deliverable: edge case fixes deployed. A note on any scaling concerns to address before the next traffic milestone (10× current users).
Week 4 (days 22–30): handoff and maintenance plan
Week 4 is about making sure the project survives without crisis-mode attention.
Documentation update. Update the README, deployment guide, and any runbooks with what you learned in weeks 1–3. The production configuration probably differs from what was planned — document the actual state.
Monitoring tuning. By now you know what errors are real and what's noise. Adjust alert thresholds. Remove false positives. Set up a weekly error digest email so you catch regressions without checking daily.
Dependency audit. Run a security scan on dependencies. Set up automated alerts (Dependabot, Renovate) for critical vulnerabilities. You don't need to update everything — just ensure you'll know when something needs attention.
Maintenance plan. Define what ongoing support looks like:
- Who monitors errors? How often?
- What's the response time for critical bugs?
- When will dependencies be updated?
- Who handles the next traffic milestone?
Deliverable: a one-page maintenance plan. Not a contract — a shared understanding of what "keeping this running" means.
What this costs
The 30-day stabilization typically takes 15–25 hours of developer time spread across the month. For a project with a €10K–€20K build cost, that's roughly €1,500–€3,000 in post-launch support.
Compare that to the cost of a preventable outage in month 2, a GDPR compliance gap discovered by a user complaint, or a performance regression that tanks your conversion rate before you've gathered enough data to diagnose it.
Every rescue project I've worked on had no stabilization period. The developer shipped, sent a final invoice, and disappeared. Two months later, the founder was searching for someone to fix what broke.
The question to ask your developer
Before launch, ask: "What does the first 30 days after launch look like?"
If the answer is "we'll fix bugs as they come up" — that's not a plan. That's hoping nothing breaks.
If the answer is a structured week-by-week approach with specific deliverables — you're working with someone who has shipped before and knows what happens next.
Planning a launch and want a structured stabilization period built in? Let's talk — post-launch support is part of every project I ship, not an afterthought.