What Is Progressive Delivery? A Quick Overview

Although progressive delivery is often billed as a cutting-edge concept, the idea has been around for quite some time—it’s just gained more mainstream traction lately.

Tech giants like IBM have used “pilot” or “ring” deployment techniques for many years to test changes with a smaller user group before broader rollouts. The goal was to catch issues early and limit the impact in case of problems.

As the internet age dawned, companies like Amazon, Microsoft, and Google adopted similar practices to deal with their online services' increasing scale and complexity.

Progressively rolling out changes enabled them to validate in production before proceeding full speed ahead—a necessity with so many users.

It wasn’t until the mid-2010s that the term “progressive delivery” was coined and started gaining popularity as a formalized strategy and set of principles.

This aligned with the rise of DevOps, microservices, cloud-native architectures, and a shift toward more frequent, iterative software deployments.

Progressive delivery is becoming the industry standard today, with major players like Atlassian, Cloudflare, and PagerDuty leading the charge. Even smaller teams and companies realize the benefits of this low-risk, data-driven deployment approach in the volatile digital landscape.

Understanding how progressive delivery differs from traditional shipping software is crucial for truly grasping its value.

Before its more modern counterpart overruled it, this classic “big bang” deployment approach reigned supreme for decades.

Traditional Delivery: The old way of doing things

With traditional software delivery, new updates get infrequently bundled into massive, monolithic releases. This means that perhaps once every few months (or even longer), a giant deployment simultaneously pushes out all the new features and fixes to 100% of a product’s users.

It’s like dropping a huge boulder into a pond—the impact resonates outward, affecting the entire system simultaneously. If something goes wrong, everyone will experience issues until the update is rolled back.

Progressive delivery: A modern, less risky approach

Progressive delivery, on the other hand, is all about flow. Instead of disruptive rollouts, updates get trickled out incrementally to user subsets.

You might first target 1% of your traffic, then 5%, then 25%, carefully validating the stability and performance at each step before proceeding.

It’s a controlled process where you dip your toes in the water to test the temperature before slowly wading in. If there are concerns, you can pause, make fixes, or easily roll back to a known good state for just the impacted group—without disrupting other users.

This measured, data-driven approach enables you to mitigate risk, collect real-world feedback faster, and deliver an exceptional product experience. In theory, there are no more nerve-wracking releases to keep you up at night.

While the core premise of progressive delivery is straightforward—release gradually instead of all at once—several different techniques fall under its umbrella.

Here are some of the most common types and methods.

Canary releases

With this approach, each new version is rolled out in phases to an increasing percentage of users in a production environment.

You could begin by routing just 1% of your traffic to the new code before validating and bumping up in chunks until you’re 100% deployed.

It’s called a “canary release” after the age-old practice of sending a canary into the coal mine first to detect any dangers before the whole crew goes in.

Blue/Green deployments

This is when you have two identical production environments: Blue and Green.

The Blue environment serves your live traffic, while the Green environment is where you deploy your updated product version. You can then divert some live traffic from Blue to Green to test and validate the changes—such as how real users interact with the changes and if your software holds up.

If all goes well, you can send your remaining traffic to Green, meaning all your users can now use the tweaked software. The Blue environment is then updated and ready for the next product iteration.

Feature flags

Feature flags (or toggles) enable you to embed hooks into your codebase to turn certain features and functionality on or off on the fly.

Let’s say a new feature is already bundled in a release. Instead of making the update live for everyone, you can wrap the change with a flag and safely expose it (or turn it on) to only an initial test segment of your users.

Based on the results, you can roll it out wider or deactivate it via a configuration change. No new code deployment is needed.

Progressive delivery places confidence, visibility, and control into your software delivery process. It’s rapidly becoming a required practice for achieving elite DevOps performance.

Let’s explore the benefits in more detail to understand why many teams have switched.

Mitigate downtime and revenue impact

Even if an issue slips through the cracks, progressive delivery means you can contain the impact to a tiny fraction of your users rather than your entire customer base.

This limits potential downtime, crashes, data loss, or revenue impact compared to a total outage.

Release more frequently

When deploying in an all-or-nothing way, teams naturally become more risk-averse, wanting to bundle lots of testing into each major release.

Since there's less risk exposure, progressive delivery enables you to update more frequently and with smaller batches. Faster feedback loops mean faster iterations.

Validate performance in production

Progressive delivery helps you extensively monitor how your release or update resonates with real users.

Gather performance data, user feedback, and A/B test experiments in a live production environment rather than simulated testing. This means no nasty surprises when things are released more broadly.

Fix forward, don’t roll back

If an issue arises after a progressive deployment, you don’t necessarily have to go through costly rollbacks and redeploys.

You can quickly hotfix and push the updated code to only the impacted percentage of your users.

Enable serverless and distributed deployments

Progressive delivery pairs well with modern cloud architecture patterns like microservices, serverless functions, and service meshes, so you can independently deploy separate pieces rather than a single development stack.

The general principles behind progressive delivery are straightforward. However, some key logistics and tooling are needed to implement them effectively.

Here’s a high-level look at how it works.

Service routing layer

A routing and traffic management layer is central to any progressive delivery implementation.

This dynamically controls how user requests are mapped to different product versions. Depending on your business or product specifications, it could be an ingress controller, service mesh, load balancer, CDN, or other proxy layer.

Deployment environments

Progressive deployments need separate environments for different slices of production traffic rather than a simple “production” versus “not production” environment.

For example, canary and Blue/Green deployment patterns require identical production-like environments.

Staged rollout process

The actual progressive rollout follows an incremental, step-by-step process defined by your team.

With the new version, you might initially route 1% of your user traffic to a canary environment. Then, based on success metrics, you can increment to 5%, 25%, 50%, and beyond.

Monitoring and observability

Following the initial launch, robust monitoring, tracing, and observability tools are used to inspect how the system behaves, what your users do, and whether the deployment meets your internal goals.

This data helps determine whether to push the release further or pump the brakes at every stage.

Configuration management

Progressive delivery relies heavily on configurations and feature flag management. In addition to deploying new code, these enable you to toggle different product functionalities on or off for specific traffic buckets.

Failsafe and rollback capabilities

If issues get detected during a progressive rollout, quick and straightforward rollback mechanisms help automatically revert that traffic area to a stable and reliable version.

Fault injection testing can validate these fail-safes. In fault injection testing, you deliberately introduce errors to see if your system can handle and recover.

Like any practice, progressive delivery isn’t perfect. It comes with its own set of hurdles to overcome, which teams should know before starting the approach.

• It can be complex: Implementing progressive delivery may require new tooling, processes, and deployment environments—more than a traditional setup. Managing and configuring separate environments, traffic routing rules, feature flags, and monitoring multiple environments can be complex.

• Doesn’t work with all architectures: Progressive delivery works best with modern microservices or service mesh architectures. Distilling monolithic applications without proper service boundaries can be trickier for incremental deployments.

• There’s added testing: Smaller, more frequent releases mean more testing—you need to validate each traffic percentage as the changes are rolled out. The added load can be challenging without good test automation.

• Might impact performance: Depending on how it’s implemented, progressive delivery can introduce performance penalties, such as increased latency, network hops, data replication, and more. Proper load testing is crucial.

• Requires team training: Progressive delivery represents a cultural shift from infrequent, larger deployments to an incremental, data-driven delivery process. Helping teams understand the processes, responsibilities, and mindset change is vital.

Despite these challenges, progressive delivery has improved deployment reliability, velocity, and quality metrics when carried out thoughtfully.

The investment is worthwhile for product teams shipping important software systems at scale.

Adopting progressive delivery can be transformative, but it’s not something to rush into.

Implementing the approach is as much about cultural changes as it is about new processes. It’s a profoundly impactful practice when teams fully commit to the mindset and principles.

Here are some best practices to set yourself—and your business—up for success.

Start small

Like any significant process change, you should start with a low-risk pilot project. Pick a non-critical service and use it to roll out a minor update slowly. Get hands-on experience, work out the kinks, and build your expertise before scaling further.

Involve the whole team

Progressive delivery impacts far more than just deployment processes.

Ensure you align all your key stakeholders—developers, product teams, operations, engineers, QA, and leadership. Getting universal buy-in helps avoid silos and bottlenecks.

Automate when you can

Manual processes aren’t usually the best approach for progressive delivery—it’s an extensive practice with many intricacies.

Instead, you should automate everything where possible. This includes deployment pipelines, testing, configuration management, traffic routing, monitoring, and rollback or roll-forward mechanisms.

Research and invest in proper tooling before you get going.

Consider your architectural gaps

Evolve your application architecture for better service boundaries and isolation where needed.

Single development stacks are trickier to work with for staged rollouts. Aim for deployment modularity.

Monitor, monitor, monitor

You can’t fly blind—observability is paramount with progressive delivery.

Track a comprehensive bundle of service-level objectives (SLOs), indicators (SLIs), and user experience metrics. Use this data to validate your release’s health and how you progress in an objective way.

Build resiliency

Design your system components from the get-go to safely handle different versions running in parallel.

Decouple your feature deployments from the full-scale release. Use fault isolation, circuit breakers, dark traffic mirroring, and other features or techniques.

Embrace data over opinions

Rather than based on gut feelings, objective production metrics must drive all progressive rollout and roll-back decisions.

There's no debate if the numbers don’t clear your quality gates—failed releases are automatically pulled.

Amplitdue’s advanced analytic and experiment features enable product teams to measure the impact of their progressive delivery strategies against crucial business metrics, like user engagement and conversion rates.

By experimenting with and comparing different rollout approaches or analyzing A/B test results within Amplitude, you can make informed decisions about proceeding with further iterations or broader deployments.

• Use A/B testing and feature management to see how separate, smaller groups react to your changes and if your update holds up in production.

• Enable the platform’s segmentation features to define specific user groups and target these segments with different rollout strategies.

• Correlate user behavior with system performance metrics. Analyze if new features or updates are causing any performance issues by examining user sessions and interactions.

• Capture user feedback through surveys, in-app messages, or other channels. This information and behavioral data inform changes to your progressive delivery strategy.