In modern DevOps practices, ensuring seamless deployments, feature rollouts, and application stability is crucial for delivering high-quality software. Traffic splitting is a technique that enables teams to control how user traffic is distributed across different versions of an application, allowing for safe deployments, gradual feature rollouts, and better experimentation.
What is Traffic Splitting?
Traffic splitting is dividing incoming user traffic between different versions of an application or service. It allows developers to test new features, conduct A/B testing, and implement progressive rollouts without impacting the entire user base.
Objectives of Traffic Splitting
1. Minimize Deployment Risks
One of the biggest challenges in software deployment is the risk of introducing bugs, performance degradation, or system failures. Traffic splitting helps mitigate this risk by gradually shifting traffic to a new version instead of deploying to all users at once and allowing teams to monitor performance metrics, error rates, and user feedback before scaling up and reducing the impact of failures by quickly rolling back to the previous stable version if issues arise.
For example, a canary deployment strategy routes 5% of traffic to a new release (v2.0) while 95% remains on v1.0. The traffic share gradually increases if no issues are detected after monitoring for a defined period.
2. Enable Controlled Feature Releases
Traffic splitting allows teams to validate new features with real users before making them globally available. This controlled release approach ensures early detection of usability issues and unexpected behaviors before mass deployment. The ability to compare feature adoption rates, user engagement, and performance impact. A smooth rollout by progressively increasing traffic instead of a single, high-risk release.
For example, a social media platform introduces a new “stories” feature, enabling it for only 10% of users. Based on analytics and feedback, the feature is optimized before a full-scale launch.
3. Improve System Stability
Even with rigorous testing, unexpected failures can occur in production. Traffic splitting enhances system stability by dynamically rerouting traffic away from failing or underperforming versions. It ensures that users experience minimal service disruptions during rollouts and allows faster recovery from failed updates by directing traffic back to the stable version.
Example, a blue-green deployment method maintains two identical production environments. If the new (Green) version fails, all traffic is instantly switched back to the stable (Blue) version, minimizing downtime.
4. Facilitate Experimentation & Testing
Traffic splitting supports various progressive delivery techniques that allow teams to experiment and make data-driven decisions. This includes:
- A/B Testing – Directing users to two different feature versions and measuring engagement to determine the better one.
- Canary Releases – Gradually increase the traffic to a new version based on its stability and performance.
- Blue-Green Deployments – Seamlessly switching traffic between two environments to ensure zero downtime deployments.
For example, an e-commerce company tests two different checkout flows by splitting traffic evenly (50/50) and analyzing which reduces cart abandonment rates. The winning version is then rolled out to all users.
5. Optimize Performance & Cost
Traffic splitting enables intelligent traffic routing to improve system performance and cost efficiency by dynamically distributing load based on server availability and geographical location. It also scales resources up or down to prevent over-provisioning and reduce infrastructure costs. Finally, it directs users to regional instances or edge servers for faster response times and reduced latency.
Example: A video streaming service directs users in North America to US-based servers while routing European users to EU-based servers, optimizing latency and bandwidth costs.
Use Cases of Traffic Splitting in DevOps
Traffic splitting is widely used in modern cloud and microservices architectures to enable safe and flexible deployments.
1. Canary Deployments
Releases a new version (canary) to a few users before full deployment. If successful, traffic is gradually increased; otherwise, rollback occurs. Example: Deploying v2.0 of an API to 10% of traffic while 90% still use v1.0.
2. Blue-Green Deployments
Maintains two environments (blue – current version, green – new version). Traffic is switched instantly to the new version once validated. Example: Directing all traffic to Green (v2.0) while keeping Blue (v1.0) as a backup.
3. A/B Testing
Splits traffic between two variations to analyze user engagement and performance. It helps teams decide which version is more effective based on user behavior analytics. Example: Test two homepage designs (Version A – 50%, Version B – 50%) and select the one with higher engagement.
4. Feature Flags & Progressive Delivery
Traffic is split to enable feature toggles, allowing specific users to experience new features while others use the stable version. It is used for internal testing and beta releases. Example: Rolling out a new checkout flow to 20% of users before enabling it globally.
5. Disaster Recovery & Failover Handling
It reroutes traffic to backup or redundant instances in case of failures and ensures minimal downtime and high availability. Example: If datacenter A fails, traffic is redirected to datacenter B automatically.
Methods of Implementing Traffic Splitting
Depending on the infrastructure, deployment model, and traffic control needs, traffic splitting can be achieved using various techniques.
1. Weighted Routing
Routes traffic to different versions based on predefined percentages. Commonly used for canary deployments and A/B testing.
2. Header-Based Routing
Directs traffic based on HTTP headers like User-Agent or Authorization. Used for feature flagging and targeted testing. Example: Routing iOS users to v2 while Android users stay on v1.
3. Cookie-Based Routing
It routes traffic based on user cookies for personalized experiences and ensures sticky sessions, allowing the same users to stay on a specific version. Example: Users in an experiment group receive a session cookie that keeps them on a particular feature version.
4. Region-Based Routing
It routes traffic based on user location or geolocation policies and is helpful for regional rollouts and compliance requirements. Example: Europe-based users get v2, while Asia-based users stay on v1.
5. Service Mesh-Based Traffic Splitting
Uses service mesh solutions like Istio, Linkerd, and Consul to manage advanced traffic control dynamically. Ensures security, observability, and flexibility in complex deployments.
Best Practices for Traffic Splitting
1. Start Small & Gradually Increase Traffic
Begin with a small percentage (5-10%) of traffic directed to the new version to detect potential issues early. Monitor performance metrics and error logs before increasing traffic further. Gradually scale up based on system stability and user feedback. Avoid sudden traffic shifts, which may overload servers or expose undetected bugs.
Example: A canary deployment routes 5% of users to the new API version. If no issues arise, traffic is increased incrementally.
2. Use Metrics & Observability
Continuously track Key Performance Indicators (KPIs) such as latency, error rates, CPU usage, and user engagement. Use observability tools like Prometheus, Grafana, and New Relic to gain real-time visibility into deployments. Set up alerts and anomaly detection to identify unexpected issues early. Collect user feedback and behavioral analytics to assess the impact of the new version.
Example: If latency increases by 20% during a traffic shift, an alert is triggered, and rollback is considered.
3. Automate Traffic Management
Integrate traffic splitting into CI/CD pipelines for automatic routing and monitoring during deployments. Use Kubernetes controllers, service mesh solutions (Istio, Linkerd), and API gateways to manage traffic efficiently. Automate progressive delivery techniques such as blue-green deployments and feature flag toggling. Implement self-healing mechanisms that dynamically adjust traffic in case of failures.
4. Ensure Rollback Mechanisms
Define clear rollback criteria such as error thresholds, performance degradation limits, or user complaints. Use progressive deployment strategies (e.g., feature flags, canary releases) to revert to the previous stable version if needed quickly. Maintain versioned deployments and rollback automation scripts for quick recovery. Test rollback regularly in staging environments to ensure effectiveness.
5. Apply Security & Access Control
Secure traffic routing using mutual TLS (mTLS), encryption, and API gateways to prevent data leaks and unauthorized access. Restrict access to experimental features using feature flagging tools (LaunchDarkly, Split.io) to limit exposure to internal teams or beta users. Implement role-based access controls (RBAC) to prevent unauthorized traffic manipulation. Monitor API traffic patterns to detect potential security threats or misuse.
Conclusion
Traffic splitting is an essential DevOps strategy that enables safer deployments, better testing, and efficient feature rollouts. By implementing canary deployments, A/B testing, blue-green deployments, and service mesh routing, teams can control traffic dynamically and ensure high availability.
With the right tools, best practices, and monitoring strategies, organizations can achieve risk-free deployments, enhance system stability, and improve user experience.
