# Production-Ready Checklist for Applications on GroqCloud

Deploying LLM applications to production involves critical decisions that directly impact user experience, operational costs, and system reliability. **This comprehensive checklist** guides you through the essential steps to launch and scale your Groq-powered application with confidence.

From selecting the optimal model architecture and configuring processing tiers to implementing robust monitoring and cost controls, each section addresses the common pitfalls that can derail even the most promising LLM applications.

## Pre-Launch Requirements

### Model Selection Strategy

* Document latency requirements for each use case
* Test quality/latency trade-offs across model sizes
* Reference the Model Selection Workflow in the Latency Optimization Guide

### Prompt Engineering Optimization

* Optimize prompts for token efficiency using context management strategies
* Implement prompt templates with variable injection
* Test structured output formats for consistency
* Document optimization results and token savings

### Processing Tier Configuration

* Reference the Processing Tier Selection Workflow in the Latency Optimization Guide
* Implement retry logic for Flex Processing failures
* Design callback handlers for Batch Processing

## Performance Optimization

### Streaming Implementation

* Test streaming vs non-streaming latency impact and user experience
* Configure appropriate timeout settings
* Handle streaming errors gracefully

### Network and Infrastructure

* Measure baseline network latency to Groq endpoints
* Configure timeouts based on expected response lengths
* Set up retry logic with exponential backoff
* Monitor API response headers for routing information

### Load Testing

* Test with realistic traffic patterns
* Validate linear scaling characteristics
* Test different processing tier behaviors
* Measure TTFT and generation speed under load

## Monitoring and Observability

### Key Metrics to Track

* **TTFT percentiles** (P50, P90, P95, P99)
* **End-to-end latency** (client to completion)
* **Token usage and costs** per endpoint
* **Error rates** by processing tier
* **Retry rates** for Flex Processing (less then 5% target)

### Alerting Setup

* Set up alerts for latency degradation (>20% increase)
* Monitor error rates (alert if >0.5%)
* Track cost increases (alert if >20% above baseline)
* Use Groq Console for usage monitoring

## Cost Optimization

### Usage Monitoring

* Track token efficiency metrics
* Monitor cost per request across different models
* Set up cost alerting thresholds
* Analyze high-cost endpoints weekly

### Optimization Strategies

* Leverage smaller models where quality permits
* Use Batch Processing for non-urgent workloads (50% cost savings)
* Implement intelligent processing tier selection
* Optimize prompts to reduce input/output tokens

## Launch Readiness

### Final Validation

* Complete end-to-end testing with production-like loads
* Test all failure scenarios and error handling
* Validate cost projections against actual usage
* Verify monitoring and alerting systems
* Test graceful degradation strategies

### Go-Live Preparation

* Define gradual rollout plan
* Document rollback procedures
* Establish performance baselines
* Define success metrics and SLAs

## Post-Launch Optimization

### First Week

* Monitor all metrics closely
* Address any performance issues immediately
* Fine-tune timeout and retry settings
* Gather user feedback on response quality and speed

### First Month

* Review actual vs projected costs
* Optimize high-frequency prompts based on usage patterns
* Evaluate processing tier effectiveness
* A/B test prompt optimizations
* Document optimization wins and lessons learned

## Key Performance Targets

| Metric | Target | Alert Threshold |
|--------|--------|-----------------|
| TTFT P95 | Model-dependent* | >20% increase |
| Error Rate | <0.1% | >0.5% |
| Flex Retry Rate | <5% | >10% |
| Cost per 1K tokens | Baseline | +20% |

*Reference [Artificial Analysis](https://artificialanalysis.ai/providers/groq) for current model benchmarks

## Resources

- [Groq API Documentation](/docs/api-reference)
- [Prompt Engineering Guide](/docs/prompting)
- [Understanding and Optimizing Latency on Groq](/docs/production-readiness/optimizing-latency)
- [Groq Developer Community](https://community.groq.com)
- [OpenBench](https://openbench.dev)

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*This checklist should be customized based on your specific application requirements and updated based on production learnings.*

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## Quickstart: Performing Chat Completion (json)