Progressive Delivery

A deployment strategy where a new version of an application is incrementally released to a small, statistically significant subset of users or infrastructure. This allows for real-world validation of performance, stability, and user experience metrics before a broader rollout. Key steps include:

• Deploying the new version alongside the stable version.
• Routing a small percentage of traffic (e.g., 1-5%) to the new version.
• Monitoring key Service Level Indicators (SLIs) like error rates, latency, and business metrics.
• Gradually increasing traffic if metrics are healthy, or rolling back immediately if issues are detected.

A software development technique that uses conditional toggles in code to enable or disable functionality at runtime, without deploying new code. This decouples deployment from release, providing granular control. Primary use cases are:

• Trunk-based development: Merging code into mainline branches with features disabled.
• Controlled rollouts: Enabling a feature for specific user segments (e.g., internal teams, beta users, a geographic region).
• Kill switches: Instantly disabling a problematic feature in production without a rollback.
• A/B testing: Managing the exposure of different feature variants to user cohorts.

A method of comparing two or more versions of an application feature (variant A vs. variant B) by exposing them to different user segments. The goal is to make data-driven decisions based on statistical analysis of a predefined key performance indicator (KPI), such as conversion rate or engagement. Core components include:

• Randomized user allocation to ensure statistically valid cohorts.
• Hypothesis definition (e.g., "Changing the button color to blue will increase clicks").
• Metric instrumentation to track the target KPI for each variant.
• Statistical significance testing to determine if observed differences are real and not due to chance.

Techniques for directing user requests to different service versions for validation.

Traffic Splitting is the practice of routing a precise percentage of live traffic to different backend service versions, often managed by a service mesh (like Istio) or API gateway. It enables precise canary releases and A/B tests.

Shadow Deployment (or dark launching) is a more advanced technique where a new version processes a copy of all live traffic in parallel with the production version, but its responses are discarded. This allows teams to:

• Validate performance and correctness under real load with zero user impact.
• Compare output consistency between old and new versions.
• Identify resource consumption and potential scaling issues.

Critical safety mechanisms that automate failure response during a progressive rollout.

Automated Rollback is triggered when predefined Service Level Objectives (SLOs) are breached (e.g., error rate > 0.1%). It instantly reverts traffic to the last known stable version, minimizing user-facing incidents.

Health Probes are used by orchestrators like Kubernetes to assess application state:

• Readiness Probes determine if a container is ready to serve traffic. If it fails, the pod is removed from the service load balancer.
• Liveness Probes determine if a container is running. If it fails, the kubelet restarts the container.
• Startup Probes indicate when a container has successfully started its initialization. These probes ensure only healthy instances receive traffic during updates.

The foundational practices that make progressive delivery actionable and reliable.

Observability involves instrumenting applications to emit telemetry data—logs, metrics, and traces—that provide deep insight into system behavior during a rollout. Key metrics (SLIs) include latency, throughput, error rate, and saturation.

Release Automation uses GitOps and Continuous Deployment (CD) pipelines to codify the rollout process. Desired states (e.g., traffic split percentages, feature flag configurations) are declared in a Git repository. Automated controllers (like Flagger or Argo Rollouts) continuously reconcile the live environment with this declared state, executing canary steps, performing analysis, and promoting or rolling back based on metrics.

A deployment strategy where a new version of an LLM or its serving infrastructure is released to a small, controlled subset of live user traffic. This allows for real-world validation of performance, latency, and output quality before a full rollout. Key aspects include:

• Traffic Splitting: Routing a percentage of requests (e.g., 5%) to the new version.
• Real-time Monitoring: Observing key metrics like token generation latency, error rates, and output correctness.
• Automated Rollback: Triggering a reversion to the stable version if predefined error thresholds are breached.

Conditional toggles used to manage the activation of LLM-related features at runtime, decoupling deployment from release. This is critical for controlling the rollout of:

• New Prompt Templates: Testing updated system prompts or few-shot examples.
• Model Versions: Switching between different foundation model providers or fine-tuned variants.
• Retrieval-Augmented Generation (RAG) Pipelines: Enabling new data sources or chunking strategies. Feature flags allow for instant rollback without redeploying code and enable dark launches where features are tested internally before user exposure.

A statistical method for comparing two or more versions of an LLM component by exposing them to different user segments to determine which performs better against a defined business or quality metric. Common tests include:

• Model vs. Model: Comparing outputs from GPT-4, Claude 3, or a fine-tuned internal model.
• Prompt Engineering: Evaluating different instruction formats or few-shot examples.
• Hyperparameter Tuning: Testing different temperature or top_p settings for generation. Success is measured by Key Performance Indicators (KPIs) like user satisfaction scores, task completion rates, or hallucination frequency.

Techniques for managing the flow and impact of requests to LLM endpoints.

• Traffic Shaping: Controls the volume and rate of requests (e.g., queries per second) to prevent model-serving infrastructure from being overwhelmed, ensuring consistent latency.
• Shadow Deployment (Dark Launching): A new model version processes live user requests in parallel with the production model, but its outputs are discarded and not returned to the user. This allows for:
  • Performance benchmarking under real load.
  • Validation of output correctness against a golden dataset.
  • Zero-risk observation of resource consumption (GPU memory, token usage).

Progressive delivery requires monitoring unique LLM health signals to make automated rollout decisions. Critical Service Level Indicators (SLIs) for LLMs:

• Latency: Time to First Token (TTFT) and inter-token latency.
• Correctness: Semantic similarity scores against expected outputs or rise in hallucination detection alerts.
• Cost: Drift in cost per query due to changes in output length or model pricing.
• Safety/Toxicity: Spike in content filter triggers. Automated rollback is initiated if these metrics breach their Service Level Objectives (SLOs), reverting traffic to the last known stable version.

Supporting infrastructure that enables progressive delivery for LLM microservices.

• Service Mesh (e.g., Istio, Linkerd): Provides fine-grained traffic management for LLM serving pods, enabling canary releases, fault injection, and observability of service-to-service calls (e.g., between an orchestrator and an embedding model).
• API Gateway: Acts as the unified entry point for LLM API requests, handling:
  • Traffic Splitting: Routing requests to different model endpoints.
  • Rate Limiting & Quotas: Enforcing usage policies per user or team.
  • Circuit Breaking: Preventing cascading failures if a downstream model service becomes unresponsive.

A software development technique that uses conditional toggles (flags) to enable or disable functionality at runtime, without deploying new code. This decouples deployment from release, enabling:

• Trunk-based development and continuous integration.
• Instantaneous rollback by disabling a flag.
• Targeted feature exposure to specific user segments (e.g., internal teams, beta users).
• A/B testing frameworks by toggling features for different cohorts.

A statistical method for comparing two or more variants (A and B) of an application feature by exposing them to different user segments. The goal is to determine which variant performs better against a defined key performance indicator (KPI), such as conversion rate or engagement. In Progressive Delivery, A/B testing is often powered by feature flags and traffic splitting to make data-driven release decisions.

The practice of routing a defined percentage of user requests to different versions of a service. It is the core routing mechanism behind canary releases and A/B tests. Implementation is typically done at the ingress or service mesh layer (e.g., using Istio's VirtualService or a cloud load balancer). This allows for precise control, such as sending 5% of traffic to a new LLM inference endpoint while monitoring for hallucination rate increases.

A release strategy that maintains two identical, fully provisioned production environments: Blue (active) and Green (idle). The new version is deployed to the idle environment. Once validated, traffic is switched entirely from Blue to Green. This enables:

• Zero-downtime deployments and instant rollbacks by switching traffic back.
• Reduced risk compared to in-place updates.
• Full-scale testing of the new environment before receiving live traffic.