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Deploy

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Deploy is the action of pushing the software to the production environment where it becomes accessible to users. A mature deployment process is boring by design - predictable, automated, and reversible. If deploying to production feels risky, your deployment process needs work.

Deployment Strategies​

Modern deployment focuses on minimising downtime and risk. Each strategy makes different trade-offs between complexity, speed, and safety.

Recreate (Replace)​

Shut down the old version entirely, then start the new version. Simple but causes downtime.

  • Best for: Non-critical internal tools, batch jobs, or environments where brief downtime is acceptable.
  • Risk: Complete outage during the switchover window.

Rolling Deployment​

Gradually replace instances of the old version with the new version, one (or a few) at a time. No downtime, but both versions run simultaneously during the rollout.

  • Best for: Stateless applications behind a load balancer.
  • Risk: Requires backward-compatible changes since both versions serve traffic simultaneously.

Blue/Green Deployment​

Run two identical production environments. Deploy the new version to the idle environment (Green), test it, then switch traffic from the current environment (Blue) to Green.

  • Best for: Applications that need instant rollback capability. If Green has issues, switch traffic back to Blue immediately.
  • Risk: Requires double the infrastructure (though cloud auto-scaling mitigates this cost).

Canary Deployment​

rolls out the new version to a small percentage of users (e.g. 1-5%) while the majority continue using the old version. Monitor error rates and performance, then gradually increase the canary percentage.

  • Best for: High-traffic applications where you want real-world validation before full rollout.
  • Risk: Requires sophisticated traffic routing and monitoring. Users on the canary may experience issues.

Infrastructure as Code​

means managing infrastructure through version-controlled code rather than manual processes. This is foundational for reliable, repeatable deployments.

IaC Tools​

  • Terraform: Cloud-agnostic, declarative infrastructure. The most widely adopted tool. Excellent for multi-cloud setups.
  • Pulumi: using general-purpose programming languages (TypeScript, Python, Go) instead of a . Preferred by teams who want full programming language capabilities.
  • AWS CloudFormation / Azure Bicep: Cloud-specific tools. Best when you are committed to a single cloud provider.

IaC Best Practices​

  • Store all infrastructure code in version control alongside application code.
  • Use modules and reusable components to avoid duplication.
  • Implement a review process for infrastructure changes (Terraform plan review before apply).
  • Use remote state with locking to prevent concurrent modifications.

Environment Management​

Environment Parity​

Maintain consistent environments across the deployment pipeline:

  • Development: Local or cloud-based developer environments.
  • Staging / Pre-production: A that mirrors production as closely as possible - same infrastructure, same configurations, same data shapes (with synthetic data).
  • Production: The live environment serving real users.

The goal is to minimise the delta between environments. - where environments silently diverge - is a top cause of "works in staging, breaks in prod" failures.

Secrets Management​

Never store secrets (API keys, database credentials, encryption keys) in code, environment variables checked into source control, or configuration files.

  • Use a dedicated secrets manager (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault, Doppler).
  • Rotate secrets on a defined schedule.
  • Audit access to secrets.

Zero-Downtime Deployment​

For user-facing applications, zero-downtime deployment is a baseline expectation. Key techniques:

  • Database migrations must be backward-compatible: Deploy schema changes that work with both old and new application versions. Use expand-and-contract migration patterns.
  • Graceful shutdown: Applications should finish in-flight requests before terminating.
  • Health checks: Load balancers should only route traffic to instances that report healthy.
  • Orchestration: Container orchestration platforms like handle rolling updates, health checking, and automatic rollback natively.
By Company Stage
  • Startup: Use a (Vercel, Netlify, Railway, Render) for deployment. Push to main, it deploys automatically. Manual infrastructure is acceptable initially - do not over-invest in until you have . Focus on shipping, not infrastructure perfection.
  • Growth Stage: Adopt (Terraform or Pulumi) for all infrastructure. Containerise applications with . Implement multi-environment pipelines (dev → staging → production) with promotion gates. Introduce or deployments for critical services. Set up a secrets manager.
  • Established: Implement multi-region deployments with disaster recovery. Compliance-audited deployment pipelines ( controls, change records, approval workflows). or equivalent orchestration at scale. Dedicated platform engineering team owns deployment infrastructure. Regular disaster recovery drills. Deployment dashboards tracking .

Common Pitfalls​

Anti-patterns to Avoid
  • Snowflake Servers: Environments configured manually and never reproduced. When they break, nobody knows how to rebuild them. eliminates this entirely.
  • No Rollback Procedure: If you cannot roll back a deployment within minutes, you do not have a deployment process - you have a one-way door. Test your rollback procedure regularly.
  • Configuration Drift: Staging and production silently diverge over time due to manual changes. Use and automated configuration management to keep environments in sync.
  • Deploying on Friday at 5pm: Deploy when your team is available to monitor and respond. If your deployment process is truly reliable, any time is fine - but build that confidence first.

Deployment Key Deliverables​

  • Live Production System
  • Configuration Scripts
  • Deployment
  • Rollback Procedure (tested)
  • Environment Configuration Documentation
How AI Can Help: Deployment

AI optimizes the complex logistics of deployment:

  • Smart Infrastructure: AI generates and optimizes scripts for tools like Terraform and Pulumi, ensuring secure cloud configurations.
  • Continuous Deployment: AI-powered tools (including GitLab, GitHub and CircleCI) optimize deployment windows and predict potential failures.
  • Deployment visibility / DORA: Tools like Sleuth provide deployment tracking and -style metrics to improve deployment visibility and delivery performance.
  • Risk Assessment: Tools like Harness perform intelligent verification, analyzing post-deployment behavior to suggest automatic rollbacks if anomalies are detected.