Rollback Strategies for Maintaining User Trust

Circa 2012-14, at a FAANG company (can’t pin-point for obvious reason 😉), we once faced a choice that could have cost MILLIONS in downtime… 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝. A critical system update was set to go live. Everything was tested, reviewed, and ready. Until a last-minute test showed an unusual error. 𝐍𝐨𝐰 𝐰𝐞 𝐡𝐚𝐝 𝐭𝐰𝐨 𝐨𝐩𝐭𝐢𝐨𝐧𝐬: ↳ Push ahead and risk an outage that could cost millions per minute. ↳ Roll back and delay a major feature for weeks. 𝐍𝐞𝐢𝐭𝐡𝐞𝐫 𝐟𝐞𝐥𝐭 𝐫𝐢𝐠𝐡𝐭. So we took a smarter approach. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐝𝐢𝐝: ➡️ 1. Instead of an all-or-nothing launch, we released to 0.1% of our traffic first. If things went sideways, we could shut it down in real time. ➡️ 2. Pre-prod tests only catch what they’re designed to catch—but production is unpredictable. We used synthetic traffic to simulate real-user behavior in a controlled environment. ➡️ 3. We didn’t just have one rollback plan — 𝐰𝐞 𝐡𝐚𝐝 𝐭𝐡𝐫𝐞𝐞: App-layer toggle – Immediate rollback for end-user impact. Traffic rerouting – Redirecting requests to stable older versions if needed. DB versioning – Avoiding schema lock-in with backwards-compatible updates. ➡️ 4. We set up live telemetry dashboards tracking error rates, latencies, and key business metrics—so we weren’t reacting blindly. ➡️ 5. Before the rollout, we ran a “what-if” drill: If this update fails, how will it fail? This helped us build mitigation paths before they were needed. 𝐖𝐡𝐚𝐭 𝐇𝐚𝐩𝐩𝐞𝐧𝐞𝐝? The anomaly we caught in testing never materialized in production. If we had rolled back, we’d have wasted weeks fixing a non-issue. Most teams still launch software with an “all or nothing” mindset. But controlled rollouts, kill switches, and real-time observability can let you ship fast and safe—without breaking everything. How does your team handle high-risk deployments? Would love to hear that 🙂

This is how you run a company with transparency and (re)gain the trust of your users. Last month we launched a feature aimed at enhancing the user experience on UpdateAI. Unfortunately, it didn't go as planned and ended up making the experience worse for some users. Something that drives me up the wall is when companies (and people) don't just admit when they mess up. Here's what happened and what I did to follow our company values of transparency: THE ISSUE: I received a Slack message from a really valuable customer that they were unhappy with some new functionality. It felt invasive to them, and I don't disagree. Their feedback was not just a complaint, but a crucial learning opportunity for us. MY RESPONSE: - I told them that they were right. - I explained a benefit of that feature to them that we had in mind, but didn't sidestep the fact that the primary motivation of this feature was to help promote our product. - But I also used this discussion as an opportunity to gain more insight from the user. I got down to the root issue of it for them, and created a dialogue on prioritizing a rollback or change to the feature. - In creating a space to discuss the priority of this with them, I also brought them onto "my side" and implicitly set expectations. - Most importantly, I apologized with zero conditions. Manners are so important, and I think it's something that we as a society have started to let slip a bit. THE RESULT: - The customer felt respected and the issue was de-escalated. - Expectations on remediation were properly set. - I gained valuable customer insight. We can talk all day about #customerexperience mapping and playbooks, but I think the number one way to win trust, confidence, and loyalty in your customers is to be open and honest. What has been your experience with transparency in business? I’d love to hear your thoughts and stories in the comments. ----- #customersuccess, #saas

This morning, much of the world woke up to the dreaded BSOD (Blue Screen of Death), causing a global outage of IT systems due to a single content update from CrowdStrike. Having worked with deployment strategies in the past at large organizations like Mercedes and even within our startup, I've always ensured we utilized one of these rollout strategies: Canary Releases: Select a subset of users as "canaries" and deploy the update to them. Monitor KPIs, errors, and performance for any issues. If the canaries do not encounter problems, gradually move into a general availability (GA) release. In some cases, a canary release can be turned into a phased rollout strategy for extremely risky deployments. Rolling Deployments (Phased Rollouts): This is the one I've always favored since it's easier to automate. You gradually and incrementally replace older versions of your application. You can follow a linear, exponential, or logarithmic release path. You still reap some of the benefits of the canary process through a phased approach, buying you lead time to catch and fix errors. Blue-Green Deployments: This is the strategy we use here at Tara AI. We maintain two identical environments. All users are routed to the blue environment. The new version goes to green, where it undergoes thorough testing. Once we have the all-clear, traffic is switched over to the green environment, and the blue is archived. There is zero downtime and granular rollback capability. Some other steps we would take during any updates to our customers: - There was always a documented rollback plan. We documented everything from the version to the estimated recovery time and probable SLA impact. - We listed known and unknown risks right before deploying to customers. Often, organizations are fully aware of what they're doing; someone just forgets to communicate key information. - We used multi-stage CI/CD pipelines with fail-safes that checked core vitals. This slowed our releases but ensured data integrity, customer experience, and performance. - We over-communicated rollout updates. During rollouts, communication was constant with key stakeholders.

We reviewed 13 CI/CD pipelines. 11 had ZERO rollback strategy. Let’s be blunt. That’s not CI/CD. That’s gambling with production. In the last 6 months, my team at Infra360.io reviewed 13 production-grade pipelines. 🚫 No versioned artifacts. 🚫 No traffic shifting. 🚫 No automated rollback triggers. 🚫 No database rollback plans. Just blind confidence that every release would “somehow” work. Here are the real gaps nobody talks about: 1. 𝐀𝐫𝐭𝐢𝐟𝐚𝐜𝐭𝐬 𝐀𝐫𝐞 𝐍𝐎𝐓 𝐈𝐦𝐦𝐮𝐭𝐚𝐛𝐥𝐞   → Teams rebuild during rollback, introducing new variables. Pro Tip: Use artifact repositories like Artifactory or ECR. Your rollback should be a redeploy, not a rebuild. 2. 𝐙𝐞𝐫𝐨 𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐢𝐧 𝐏𝐥𝐚𝐜𝐞 → One bad deploy and 100% of traffic hits it. Pro Tip: Implement blue/green or canary rollouts with Argo Rollouts or Flagger. Control exposure like a pro. 3. 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 𝐀𝐫𝐞 𝐎𝐧𝐞-𝐖𝐚𝐲 𝐓𝐫𝐢𝐩𝐬 → Code rollback is useless if schema changes can’t roll back. Pro Tip: Integrate Flyway or Liquibase for proper schema versioning and rollback scripts. 4. 𝐍𝐨 𝐂𝐨𝐧𝐟𝐢𝐠 𝐚𝐧𝐝 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐕𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠 → Rollback happens but config stays broken. Pro Tip: Use GitOps to version everything—including configs and secrets. 5. 𝐑𝐨𝐥𝐥𝐛𝐚𝐜𝐤 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐬 𝐇𝐮𝐦𝐚𝐧 𝐈𝐧𝐭𝐞𝐫𝐯𝐞𝐧𝐭𝐢𝐨𝐧 → And that usually happens at 2 AM, under pressure. Pro Tip: Automate rollback triggers based on SLO breaches, error rates, and health checks. If you can’t undo a deployment in under 60 seconds, your pipeline isn’t fast. It’s dangerous. Fast delivery means nothing without fast recovery. Would you trust your last deploy to auto-recover? ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 𝐒𝐨 𝐎𝐭𝐡𝐞𝐫𝐬 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧.