AI Infrastructure Checklist
The complete pre-flight checklist for deploying production AI systems. Nothing ships until everything checks out.
Architecture
Foundation decisions that determine everything downstream.
Define clear system boundaries
Each service has a single responsibility with well-defined inputs and outputs.
Design for failure
Every external dependency (LLM APIs, databases, third-party services) has a fallback path.
Establish data flow contracts
Schema validation at every boundary. No untyped data passes between services.
Plan for horizontal scaling
Stateless services that can scale independently based on load.
Choose infrastructure ownership model
Decide upfront: managed services vs self-hosted. Document the rationale.
Security
Non-negotiable safeguards before any system touches production data.
Encrypt data at rest and in transit
TLS for all connections. Encrypted storage for all persistent data.
Implement API authentication and rate limiting
Every endpoint requires authentication. Rate limits prevent abuse and cost overruns.
Audit LLM input/output
Log all prompts and completions. Flag and review anomalies.
Sanitize all user inputs
Prevent prompt injection, SQL injection, and XSS at every entry point.
Manage secrets properly
Environment variables or secret managers. Never hardcoded. Rotated regularly.
Define data retention policies
How long is data kept? Who can access it? When is it deleted?
Monitoring and Observability
You cannot fix what you cannot see.
Track LLM latency and token usage
Per-request latency, token consumption, and cost tracking.
Monitor error rates by service
Alerting thresholds for each service. Escalation paths defined.
Set up structured logging
JSON logs with correlation IDs. Searchable and filterable.
Implement health checks
Every service exposes a health endpoint. Load balancers route around failures.
Track business metrics
Not just uptime. Track the metrics that matter: leads processed, tasks completed, accuracy rates.
Deployment and Failover
Ship confidently. Roll back instantly.
Automated deployment pipeline
Push to main deploys to staging. Manual promotion to production.
Zero-downtime deploys
Blue-green or rolling deployments. No maintenance windows.
One-command rollback
If something breaks, revert to the last known good state in under 60 seconds.
Database migration strategy
Forward-only migrations with backward compatibility. No breaking schema changes.
Disaster recovery plan
Documented recovery procedures. Tested quarterly. RTO and RPO defined.
Testing and Quality
Confidence comes from evidence, not hope.
Unit tests for business logic
Core logic is tested in isolation. No test depends on external services.
Integration tests for API contracts
Every API endpoint has tests that verify request/response contracts.
LLM output evaluation
Automated eval suites that test model outputs against expected behavior.
Load testing before launch
Simulate peak traffic. Identify bottlenecks before users do.
Manual QA for user-facing flows
Automated tests catch regressions. Human review catches UX issues.
Need help checking these boxes?
We build production AI infrastructure that ships with every item on this list already handled. One call to scope it out.
Get In Touch