Deploying to production comes with risk. No matter how much quality assurance you perform, there's always a chance new bugs make it to production. In the worst case, bugs can disrupt service for your users, a reminder that failures are inevitable. As Andrew Clay Shaffer puts it, “All systems are in a state of continuous partial failure.”
Progressive delivery (PD) is a strategy to minimize the risk of software release. It's a simple idea: If you deliver the release to all of your users at once, and something goes wrong, everyone will be affected. Instead, you should deliver the release to a subset of users first. That way, if something goes wrong, there stands a better chance of catching the error before it impacts everyone.
As confidence in the release’s stability grows, operators can progressively expand how many users experience the new version. Hence the name “progressive” delivery.
Progressive Delivery is deployed in different ways with different goals. This section summarizes the most common.
Canary deployments borrow their name from the expression “canary in a coal mine.”
Note. Throughout the 20th century, coal miners brought canaries into mines to warn of unsafe conditions. Canaries respire faster than humans and have significantly less body mass. This meant that if a dangerous gas were present in the mine, a canary would die before any person could be affected, affording the miners the opportunity to evacuate. A chirping canary implies safety, while a silent bird signals danger.
In a software development context, the canary refers to the new release that is rolled out to a fraction of total traffic. The canary minimizes risk by acting as a live test. If issues arise (e.g. users are getting errors! carbon monoxide in the mine!), the canary can be rolled back before it impacts everyone.
A typical canary deployment follows these steps:
Canary deployments reduce the “blast radius” of failures, allow quick rollbacks, and validate changes in real production environments. However, they traditionally require traffic routing mechanisms (e.g. load balancers, service meshes) and strong observability tools to detect regressions. These are resources that smaller teams may not always have. For teams with mature automation, Automated Canary Analysis (ACA) can take this further by automatically promoting or halting releases based on pre-defined metrics.
MultiTool brings this level of intelligence to any team, regardless of their maturity. The MultiTool agent routes traffic and automates decision-making so any team can leverage canary deployments without needing enterprise-scale tooling. It provides operators with real-time insights into deployment health while handling rollout adjustments dynamically.
One of the earliest and simplest methods for progressive delivery, test servers and beta releases allow users to opt into new versions before a full production rollout.
Test servers are common in the video game industry, where uses will hop on a smaller, less stable server to try new game modes before they’re generally available.
Beta releases are useful when software is just about ready to launch, but developers want to give their users an opportunity to identify bugs that were missed during the development process before the release goes out. A beta release is an early release of a new software version with the expectation that there might be some rough edges.
These methods give teams a way to validate features in real-world conditions while limiting exposure to regressions. Unlike canary deployments, where traffic is gradually shifted, beta releases and test servers are usually voluntary; users accept potential instability in exchange for early access.
A/B testing (sometimes called split testing) is a technique where two versions of a feature, UI element, or experience are deployed simultaneously to different user segments. Traffic is split between variant A and variant B, allowing teams to measure which performs better based on key metrics like conversion rates, engagement, or error rates.
Unlike PD techniques that focus on stability and risk reduction, A/B testing is used to optimize outcomes—helping teams refine features, pricing models, or user experiences. This method is widely used in marketing, product management, and UX design, where small changes can have measurable impacts on user behavior.
For A/B testing to be effective, teams need robust analytics to track differences between versions. When done right, it provides data-driven confidence in product decisions, ensuring that new changes actually an improvement before they roll out to everyone.
Feature flags allow teams to decouple code deployment from feature release. Instead of tying new functionality to a specific deployment, teams can ship code with features hidden behind a flag, enabling or disabling them dynamically without redeploying.
This flexibility is valuable for gradual rollouts, risk mitigation, and experimentation. Teams can release a feature to a small subset of users, monitor performance, and toggle it off instantly if issues arise. It also enables controlled feature previews, where certain users (e.g. beta testers or enterprise customers) gain early access.
Feature flags are especially useful in multi-service architectures, where different components must be synchronized to avoid breaking changes. However, managing feature flags at scale requires good hygiene—old flags should be removed once a feature is fully rolled out to avoid unnecessary complexity in the codebase.
Ring deployments introduce new releases in predefined stages, or “rings.” Each ring gradually expands availability from a small group to the broader user base. This method is similar to canary deployments, but follows a structured rollout plan rather than relying on real-time monitoring for promotion decisions.
A typical ring deployment strategy follows these steps:
Ring deployments help balance stability and speed, allowing teams to catch potential issues early while ensuring a smooth, predictable rollout.
The MultiTool team categorizes progressive delivery techniques by time scale—how long delivery takes and who the primary stakeholder is.
Much of the industry discussion around progressive delivery focuses on what we call long-horizon progressive delivery, where new features are delivered over weeks or months. The goal is to analyze user behavior, validate hypotheses, and make data-driven decisions about feature adoption.
Techniques like A/B testing, feature flags, and ring deployments fall into this category. They help teams answer questions like “Did we build the right thing?” by comparing different versions of a product and assessing user engagement.
For product and marketing teams, this approach is invaluable—experimenting with UI changes, pricing models, or engagement strategies in a controlled way reduces risk and ensures that major decisions are backed by data. Many feature management tools cater specifically to this model, making it easy to turn features on and off or adjust rollout percentages dynamically.
MultiTool focuses on short-horizon progressive delivery—delivering releases over minutes or hours to ensure application stability. The priority isn’t feature validation—it’s protecting availability and performance during deployments.
For many teams, deploying new code is a high-stakes moment. Despite testing, real-world failures are inevitable, and a bad deployment can mean downtime, lost revenue, and a rush to roll back under pressure.
MultiTool offers a solution that makes short-horizon progressive delivery accessible to every team—not just those with enterprise-scale infrastructure. With intelligent canary deployments, automated rollback decisions, and built-in observability, MultiTool helps teams deploy with confidence. Our agent-based approach dynamically adjusts traffic routing, detecting issues before they impact all users, while keeping operators in control.
Progressive delivery isn’t just about rolling out features—it’s about deploying safely, every time. And while long-horizon techniques have their place, we believe fast, reliable, and low-risk deployments are the foundation of every great software team.
We hope this post helped break down the different approaches to progressive delivery and how they can make releases safer and more predictable. Whether you're fine-tuning features over weeks or rolling out changes in minutes, having a strategy in place helps reduce risk and keep things running smoothly.
If you're interested in exploring canary deployments, the MultiTool beta is free to try—we’d love to hear what you think!
