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 for current model benchmarks

Resources

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

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