The AI Production Readiness Checklist

Everything you need to verify before launching AI to production.

You’re about to ship an AI feature. The demo works. Stakeholders are excited. The deadline is tomorrow.

What should you verify before going live?

This checklist is what we use at Rotascale and what we recommend to clients. It’s comprehensive but practical - designed for teams shipping real AI products, not academic exercises.

Print it. Share it. Use it as a gate for every AI launch.

Quick Reference

The Five Gates

Evaluation - Do you know if it works?
Monitoring - Will you know when it breaks?
Fallbacks - What happens when it fails?
Cost Controls - Can you afford it at scale?
Operations - Can you respond to incidents?

A feature should not launch until all five gates are cleared.

Gate 1: Evaluation

Before launch, you need evidence that the feature works. Not “it looks good in demos” - actual measured performance.

Baseline Metrics

✓	Requirement	Notes
☐	Success criteria defined	What does "good" mean for this feature?
☐	Test dataset created	Representative inputs with expected outputs
☐	Baseline metrics documented	Current performance before launch
☐	Acceptance thresholds set	Minimum acceptable performance levels

Failure Mode Testing

✓	Requirement	Notes
☐	Edge cases tested	Unusual inputs, boundary conditions
☐	Adversarial inputs tested	Prompt injection, jailbreak attempts
☐	Empty/malformed inputs tested	Graceful handling of bad data
☐	Hallucination evaluation	Measured rate on test set

Regression Framework

✓	Requirement	Notes
☐	Automated eval suite	Can run evals without manual effort
☐	CI/CD integration	Evals run on every change
☐	Regression blocking	Failed evals prevent deployment

flowchart LR
    subgraph "Evaluation Gate"
        TC[Test Cases] --> AS[Automated Suite]
        AS --> CI[CI/CD Pipeline]
        CI --> GT{Pass?}
        GT -->|Yes| NEXT[Proceed]
        GT -->|No| BLOCK[Block Deploy]
    end

    style BLOCK fill:#fee2e2
    style NEXT fill:#dcfce7

Gate 2: Monitoring

You need to know when things go wrong - before users tell you.

Performance Monitoring

✓	Requirement	Notes
☐	Latency tracking	p50, p95, p99 response times
☐	Error rate tracking	Failed requests, timeouts, exceptions
☐	Token usage tracking	Input/output tokens per request
☐	Throughput tracking	Requests per second/minute

Quality Monitoring

✓	Requirement	Notes
☐	Output quality scoring	Automated quality assessment
☐	Hallucination detection	Flag suspicious outputs
☐	Drift detection	Alert on distribution changes
☐	User feedback capture	Thumbs up/down, explicit feedback

Alerting

✓	Requirement	Notes
☐	Alert thresholds defined	When to page vs. notify
☐	On-call routing configured	Who gets alerted when
☐	Escalation paths defined	If first responder unavailable

Gate 3: Fallbacks

AI systems fail. Your architecture needs to handle failure gracefully.

Failure Handling

✓	Requirement	Notes
☐	Timeout configured	Don't wait forever for responses
☐	Retry logic implemented	With exponential backoff
☐	Circuit breaker configured	Stop cascading failures
☐	Graceful degradation defined	What happens when AI is down

Fallback Options

✓	Requirement	Notes
☐	Backup model configured	Alternative provider/model
☐	Cached responses available	For common requests
☐	Human escalation path	When AI can't handle request
☐	Static fallback option	Worst-case user experience

flowchart TD
    REQ[Request] --> PRIMARY[Primary Model]
    PRIMARY -->|Success| RESP[Response]
    PRIMARY -->|Timeout/Error| RETRY[Retry with Backoff]
    RETRY -->|Success| RESP
    RETRY -->|Fail| BACKUP[Backup Model]
    BACKUP -->|Success| RESP
    BACKUP -->|Fail| CACHE[Check Cache]
    CACHE -->|Hit| RESP
    CACHE -->|Miss| HUMAN[Human Escalation]
    HUMAN --> RESP

    style PRIMARY fill:#dcfce7
    style BACKUP fill:#fef3c7
    style CACHE fill:#fef3c7
    style HUMAN fill:#fee2e2

Gate 4: Cost Controls

AI at scale is expensive. You need controls before you need them desperately.

Budget Management

✓	Requirement	Notes
☐	Cost per request estimated	Expected token usage × pricing
☐	Monthly budget defined	What's the spending cap?
☐	Cost tracking implemented	Real-time spend visibility
☐	Budget alerts configured	Alert at 50%, 75%, 90%

Rate Limiting

✓	Requirement	Notes
☐	Per-user rate limits	Prevent abuse by individuals
☐	Global rate limits	Cap total system throughput
☐	Hard spending cap	Automatic shutoff at limit

Cost Optimization

✓	Requirement	Notes
☐	Prompt optimization	Minimize token usage
☐	Response caching	Cache common responses
☐	Model tiering	Cheaper models for simple tasks

Gate 5: Operations

When something goes wrong at 2am, can you respond?

Documentation

✓	Requirement	Notes
☐	Architecture documented	How the system works
☐	Runbooks created	Step-by-step incident response
☐	Rollback procedure documented	How to revert changes
☐	Contact list current	Who to call for what

Incident Response

✓	Requirement	Notes
☐	On-call rotation established	Someone is always responsible
☐	Severity levels defined	What's P1 vs P2 vs P3
☐	Communication plan	How to notify stakeholders
☐	Post-mortem process	Learn from incidents

Rollback Capability

✓	Requirement	Notes
☐	Previous version available	Can revert to last known good
☐	Rollback tested	Actually tried the procedure
☐	Feature flag available	Can disable without deploy

The One-Page Summary

AI Production Readiness - Quick Check

EVALUATION
☐ Test dataset exists
☐ Baseline metrics documented
☐ Automated eval suite runs in CI/CD
☐ Deployments gated on eval pass

MONITORING
☐ Latency, errors, tokens tracked
☐ Quality scoring in production
☐ Drift detection enabled
☐ Alerts configured with on-call

FALLBACKS
☐ Timeouts and retries configured
☐ Backup model available
☐ Graceful degradation defined
☐ Human escalation path exists

COST CONTROLS
☐ Budget and spending cap defined
☐ Rate limits implemented
☐ Cost tracking enabled
☐ Alerts at 50/75/90% of budget

OPERATIONS
☐ Runbooks written
☐ On-call rotation established
☐ Rollback procedure tested
☐ Post-mortem process defined

LAUNCH DECISION
☐ All five gates cleared → Ship it
☐ Any gate failed → Fix first

Don’t ship AI without this checklist. Seriously.

Need help getting production-ready? Our platform covers evaluation, monitoring, and cost controls out of the box. See how Rotascale helps.