Traffic Splitting

Traffic splitting allows for exact percentage-based routing of inference requests. This is typically managed by a load balancer or service mesh (e.g., Istio, Envoy) using configuration rules. For example, you can direct 95% of traffic to the stable model (the champion) and 5% to a new candidate (the challenger). This granular control enables gradual rollouts, where the percentage to the new model is increased incrementally (e.g., 1% → 5% → 25% → 100%) only after verifying performance and stability at each stage.

Effective splitting supports two primary routing modes:

• Stateless (Random) Routing: The default mode, where each request is independently and randomly assigned to a model version based on the configured split. This is ideal for A/B testing aggregate performance metrics.
• Session-Affinity (Sticky) Routing: Ensures all requests from a specific user session are routed to the same model version. This is critical for maintaining consistency in user experience during experiments, preventing jarring behavior changes within a single interaction. Affinity is often managed via a user ID cookie or a session token.

The system must emit detailed telemetry for each traffic path. Key observability features include:

• Per-Version Metrics: Latency (p50, p95, p99), throughput, error rates, and business KPIs (e.g., conversion rate, accuracy) tracked separately for each model variant.
• Centralized Dashboards: Real-time visualization of performance differentials between versions.
• Alerting Integration: Automated alerts triggered if a new version's error rate spikes or latency degrades beyond a defined Service Level Objective (SLO). This data is the basis for go/no-go decisions during a rollout.

A core safety feature is the ability to instantly reroute 100% of traffic away from a failing model. This is often implemented via:

• Dynamic Configuration: The traffic split weights are managed externally (e.g., in a feature flag service or config map), not hard-coded, allowing for changes without service restarts.
• Kill Switches: Pre-configured emergency procedures or automated triggers that revert traffic to a known-stable fallback model upon detection of critical failures. This minimizes the Mean Time to Recovery (MTTR) and user impact during an incident.

Traffic splitting is the infrastructure layer for formal A/B testing and multi-armed bandit experiments. Key integrations include:

• Randomized Assignment: Ensuring users are assigned to model variants in an unbiased, statistically valid manner.
• Hypothesis Testing: Feeding per-variant metrics into statistical analysis tools to determine if observed differences in performance are significant.
• Dynamic Allocation: Advanced systems use multi-armed bandit algorithms to automatically adjust traffic splits in real-time, favoring the better-performing variant to maximize a reward metric while still exploring alternatives.

Beyond simple percentages, advanced splitting supports conditional routing based on request attributes. This enables:

• Canary Releases: Routing traffic from a specific, low-risk user segment (e.g., internal employees, users in a specific geographic region) to the new model first.
• Feature Gating: Using feature flags to expose a new model only to users who have opted into a beta program.
• Request-Based Routing: Sending only specific types of inferences (e.g., low-stakes queries, queries from certain partner APIs) to the new version for initial validation. This allows for targeted testing in a production environment with minimal blast radius.

The primary use case for traffic splitting is a gradual rollout or canary release. Instead of an immediate, high-risk switch, traffic is incrementally shifted from the old model (e.g., 95%) to the new model (5%). Key steps include:

• Start with a tiny percentage of low-risk traffic.
• Monitor key Service Level Objectives (SLOs) like latency, error rate, and business metrics.
• Gradually increase the split (e.g., 5% → 20% → 50% → 100%) only if performance remains stable.
• This phased approach allows for quick rollback if anomalies are detected, minimizing user impact.

Traffic splitting enables rigorous A/B testing (or champion-challenger testing) to statistically compare model versions. Traffic is randomly split between a control group (Model A) and a treatment group (Model B).

• Key Metrics: Define a primary evaluation metric upfront (e.g., click-through rate, conversion rate, prediction accuracy).
• Statistical Significance: Run the test until results reach statistical confidence, ensuring the observed difference is not due to random chance.
• Example: An e-commerce site splits traffic 50/50 between a legacy recommendation model and a new neural model, measuring which drives higher average order value.

Beyond static A/B tests, traffic splitting can be dynamically managed by a Multi-Armed Bandit (MAB) algorithm. This approach balances exploration (testing different models) with exploitation (routing more traffic to the best-performing model).

• Contextual Bandits: Advanced systems use features of the request (user, context) to make smarter routing decisions.
• Real-World Use: A news website uses a bandit to dynamically allocate user traffic between several headline-generation models, continuously optimizing for engagement time. The system automatically routes more traffic to the winning model while still occasionally testing others.

Traffic splitting is the core mechanism behind blue-green deployment. Two identical environments run in parallel: 'Blue' (stable, v1.0) and 'Green' (new, v1.1).

• All production traffic is routed to Blue.
• v1.1 is deployed and fully tested in the idle Green environment.
• The load balancer's traffic split is instantly switched from 100% Blue to 100% Green.
• If issues arise, the split is immediately reverted to 100% Blue, achieving a near-instantaneous rollback. This pattern ensures zero-downtime updates and eliminates version mismatch issues.

Traffic can be split based on user attributes, not just randomly. This allows for targeted, low-risk validation with specific cohorts before a global rollout.

• Internal Users: Route 100% of employee traffic to the new model for internal dogfooding.
• Low-Risk Cohorts: Initially release to a small, low-value user segment.
• Geographic Rollout: Deploy the new model to a single region or data center first (e.g., 100% of EU traffic) to contain any regional failures.
• Example: A social media platform rolls out a new content moderation model to 100% of traffic in Canada first, monitoring policy violation rates before expanding to other regions.

Traffic splitting is used to validate not just model accuracy, but also infrastructure performance and cost under real load.

• Shadow Mode Precursor: A small traffic split (e.g., 1%) to the new model on fresh infrastructure can uncover scaling, latency, or memory issues before a larger commitment.
• Cost Comparison: By splitting traffic, teams can directly compare the inference cost per request between a large, accurate model and a smaller, optimized model, informing cost-performance trade-off decisions.
• Hardware Validation: Splitting traffic between endpoints on different hardware (e.g., CPU vs. GPU, x86 vs. ARM) tests performance and compatibility.

A/B testing is a controlled experiment methodology that uses traffic splitting to compare two or more model variants. It measures which version performs better against a predefined objective metric, such as click-through rate or conversion.

• Randomized Assignment: Users are randomly assigned to variants to ensure statistical validity.
• Hypothesis-Driven: Tests a specific hypothesis (e.g., "Model B increases user engagement").
• Statistical Significance: Requires running the test until results are statistically significant to avoid false conclusions.

A canary release is a risk mitigation strategy where a new model version is initially deployed to a small, non-critical subset of traffic or users. Performance and stability are monitored closely before a full rollout.

• Phased Validation: Starts with 1-5% of traffic, incrementally increasing.
• Early Failure Detection: Limits blast radius if the new version contains bugs or performs poorly.
• Metric-Based Promotion: Proceeds to wider release only if key Service Level Objectives (SLOs) like latency and error rate are met.

In shadow mode (or dark launch), a new model processes live production traffic in parallel with the current champion model, but its predictions are logged and not used to affect user-facing decisions.

• Zero-Risk Evaluation: Allows for performance comparison on real-world data without impacting the business.
• Data Collection: Generates a labeled dataset of the new model's predictions on live data for offline evaluation.
• Infrastructure Testing: Validates that the new model's serving stack can handle production load.

Blue-green deployment is an infrastructure strategy that maintains two identical production environments. Traffic is routed entirely to one environment (e.g., Blue, running v1.0), while the new version is deployed to the other (Green, running v1.1).

• Instantaneous Switch: A load balancer configuration change instantly redirects all traffic from Blue to Green.
• Zero-Downtime Updates: Enables seamless version upgrades with no service interruption.
• Fast Rollback: If issues are detected, traffic can be switched back to the Blue environment immediately.

A Multi-Armed Bandit (MAB) is a dynamic optimization algorithm that automates traffic splitting. It balances exploration (testing different model variants to gather data) with exploitation (routing more traffic to the best-performing variant) in real-time.

• Adaptive Allocation: Continuously adjusts traffic percentages based on ongoing performance metrics.
• Regret Minimization: Aims to minimize the opportunity cost of not always using the optimal variant.
• Contextual Bandits: Advanced versions use features of the request (context) to make smarter routing decisions.

Traffic mirroring (or request duplication) is a technique where a copy of each live production inference request is sent to a secondary system, such as a model in shadow mode. The primary response is returned to the user without delay.

• Passive Observation: The mirrored model's performance does not affect the user experience.
• Load Impact: Doubles the inference load on the backend, requiring capacity planning.
• Data Fidelity: Ensures the evaluation environment receives perfectly identical input data as production.