One solution for a tricky or high-risk deployment is to leverage a canary deployment. In a canary deployment, new features are rolled out to a small subset of your customer base with no fanfare. If things go well: hooray! Canary deployments are ideal for times when a deployment might go sideways or the feature might not work as expected. By utilizing a canary release, you have minimized the risk, as impacting a small fraction of your users is a lot better than affecting all of them. This post will discuss when development teams might choose to use canary deployments in their applications, what is a canary deployment, common pitfalls to avoid, and how modern tooling can help ease the rollout of canary deployments. Where does the term canary deployment come from? You may have heard of the term "canary in the coal mine." Early coal miners would keep caged songbirds in the mine with them, because these small birds are highly susceptible to toxic gases. If the canaries suddenly died, the miners knew that there was a dangerous gas release, and they could evacuate the mine before they succumbed to the deadly gas. A canary deployment can be performed when there is concern about the risks involved. Perhaps you are rolling out new cloud infrastructure that has not yet been tested with the production environment, or maybe there are performance concerns—the updates might cause the website's performance to drop to a terrible crawl. Perhaps the change is small, but it could not be tested for security vulnerabilities. A failed rollout to your entire customer base could end in disaster in all of these cases. Many teams practice canary-style releases without explicitly calling them that. Common variations include: Blue-green deployments: Two production environments run in parallel. The blue environment serves the stable version, while green runs the new version. Traffic is gradually shifted from blue to green, limiting risk and enabling fast rollback. Experimental deployments: New behavior is exposed to a defined group of users, often with explicit awareness or opt-in. These are typically broader than canary deployments and focus on measuring user impact rather than system health alone. Gradual rollouts (progressive delivery): After validating a release with a small subset of users, traffic is incrementally increased (for example, 5% → 25% → 100%) while monitoring key metrics like error rates and latency. Shadow deployments: The new version runs in parallel and receives a copy of real production traffic, but responses are not served to users. This is useful for validation and performance testing without customer impact. Imagine this situation: You are about to launch a new feature that is considered high-risk for some reason. The team has opted for a canary deployment, releasing it to a limited group of users first. Achieving a successful deployment in this scenario demands meticulous planning. Deployment audience: Who gets the update? This requires a basic knowledge of your audience and understanding of who would benefit the most from the change. If your new version is an overhaul of the mobile dashboard, and the canary release is delivered to desktop users, how can you determine if the deployment was successful? It is certainly useful to have some desktop users in the cohort (in case something breaks on desktop), but in order to best understand the results of the deployment, you want to ensure that users interact with the features of the new version. Duration: How long will the test run? An hour? A week? Depending on the type of deployment, you may know the success/failure quickly. (Did the database correctly sync with the servers?). However, sometimes it may take days to determine success. (Is website performance faster during busy periods?). Metrics: What will the team track during the deployment? What metrics will be considered a success? What metrics would be branded a partial success? When does the team admit failure and roll back the deployment to fix bugs? Feature flags: For some canary deployments, using feature flags can be helpful. Feature flags are tools in your code that manage the deployment of application features to specific audiences. If your team is planning to use canary deployment extensively, it may be useful to integrate feature flags into your codebase. Deployment risks With any release, there is a risk of failure. Using a canary deployment mitigates the risk by reducing the blast radius of a failure to a small subset of users—however, even canary releases have risks. Among these are: Service outages: If the deployment goes bad, the application may slow down or even cease to work for the selected users. Security breaches: The new release may introduce a security vulnerability that was not discovered in testing. Rollback mechanism: Many canary releases do not have a formalized rollback plan, should things go wrong. Data loss: If the canary rollout is using a new database or a new interface to the database, any issue with the rollout may introduce data inconsistencies or data loss. For any release, it is important to consider the risks and how they will be mitigated. While the canary release lowers the risks, having plans for rollback, handling data loss etc. should be made prior to the release. During any release, it is critical to monitor your systems in case of any trouble during the deployment. Canary releases are no different, although the experimental nature of the release means there is a higher likelihood of issues arising: Error rates: Are your systems throwing more errors than typically seen in production? System metrics: Keep a close eye on memory, CPU, database, and API queries, and other resource metrics. We've all been a part of a release where the new code introduced a memory leak, or suddenly API usage was pegging 400% of normal. By monitoring these in the canary release, the issues can be quickly remediated for the canary users before releasing to the entire base. Traffic distribution: Is the canary release being properly served to the selected users? Are users accidentally crossing between the two environments? Handling these issues early ensures that the new build is completely isolated from the old environment. Security metrics: Is the login behavior different in the canary build? Are users interacting with this build differently than the previous version? Could this imply a potential threat? Monitoring these metrics during the deployment and after the release can help your team better understand the wins (and potential improvements to your app) that occur during the canary release. In addition to traditional monitoring, there are now AI tools that can be used to compare your two environments, quickly parsing the data and determining differences from the old version and the new software version that might be difficult to see in traditional logging. When dealing with a high-risk release, a canary deployment to a small subset of users is a great way to mitigate exposure in production. To be successful in your canary release, be sure to discuss how you will choose the cohort of users, monitor the release, and plan a rollback strategy prior to the release. Then, during the deployment process, your team can track the essential metrics to understand if the canary deployment was a success. Should the deployment go well, the team can plan to add users to the new version of the software, slowly deprecating the old version. Developers who use canary releases for risky deployments are able to test problematic code or infrastructure to a small user base, pushing the product ahead while mitigating the risk of an outage.