What is a deployment strategy?

A deployment strategy is the method an engineering team uses to release new code or model versions to production. It defines how traffic shifts, what happens on failure, and how much additional infrastructure runs during the transition. The six core strategies are recreate, rolling update, blue-green, canary, A/B testing, and shadow.

What are the six deployment strategies?

Recreate (big bang), rolling update, blue-green deployment, canary deployment, A/B testing, and shadow deployment. Each trades off between downtime risk, rollback speed, infrastructure cost, and complexity. The comparison table above shows all six side by side with cost profiles.

What is the best deployment strategy?

There is no single best strategy. Use recreate for internal tools. Rolling updates for standard Kubernetes services. Blue-green for production releases needing instant rollback. Canary for AI model swaps where cost per inference matters. Shadow for safety-critical AI validation. The decision framework table above maps each to specific situations.

What are the 4 phases of deployment?

Planning (choose strategy, set cost ceiling, define rollback criteria), staging (deploy to pre-production, validate), release (execute the strategy in production), and monitoring (observe metrics, costs, and errors post-release). The monitoring phase is where CloudZero adds the cost dimension most teams miss.

How do deployment strategies in devops affect cloud costs?

Blue-green doubles infrastructure during cutover. Canary adds proportional cost based on traffic split. Shadow doubles all compute. Rolling updates have minimal overhead. These cost implications are real and show up on the cloud bill. See the cost-impact table above for specifics.

How do deployment strategies apply to AI model deployments?

AI model deployments follow the same patterns but at higher cost scales. Blue-green of a GPU inference cluster doubles the most expensive line item on the bill. Canary previews cost-per-inference differences between model versions. Shadow doubles AI spend for the test duration. CloudZero, The AI ROI Company, tracks these at the deployment level so the cost of each release is visible in real time.

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