The Production Gap
Many AI implementations fail at scale despite strong pilot performance. The gap between a successful demo and a production system is vast-and often underestimated.
Production-ready AI systems require careful problem selection, rigorous testing, scalable infrastructure, and cost optimization-not just powerful models.
Identifying AI-Ready Problems
Not every problem requires an AI solution. Before implementation, organizations should ask:
- What business challenge needs solving?
- What’s the optimal solution approach?
“You have to think hard – ‘What is the right solution to my problem?’ And not – ‘Oh I have an LLM, where can I use it?’”
Deterministic vs. AI Solutions
Deterministic solutions (traditional code) often outperform AI for suitable use cases:
- Rule-based classification → Faster, cheaper, more reliable
- Template-based responses → Consistent, predictable output
- Structured data queries → SQL beats RAG for precise lookups
Clear business value linkage is essential before proceeding with an AI approach.
Testing AI Systems
Technical vs. Business Metrics
While traditional metrics like ROC and AUC matter, they’re insufficient alone. Business impact testing proves critical because LLMs hallucinate and accuracy varies.
Key insight: A model that’s only 75% accurate but can help you reduce operations cost by 50% is far more valuable than a model that’s 99% accurate but can’t be put into production!
Human-in-the-Loop Approach
Treat AI systems like junior employees:
- Start with supervised oversight
- Begin with smaller, lower-risk tasks
- Gradually increase complexity
- Build confidence before full deployment
This approach mirrors how you’d onboard any new team member.
Handling Subjective Outputs
For generative AI, a four-category evaluation framework addresses subjectivity:
- Context: Is relevant information present?
- Wordiness: Appropriate conciseness/detail balance?
- Authenticity: Genuine, appropriate tone?
- Repetitiveness: Avoids unnecessary duplication?
Gather feedback from at least 5 customer stakeholders to prevent individual bias from skewing evaluation.
Scaling: Production Challenges
Three critical components enable production scaling:
Data Processing Scale
Handling millions of datapoints across formats and content types efficiently. This means:
- Parallel processing pipelines
- Incremental indexing
- Format-agnostic ingestion
- Quality validation at scale
Query Processing Scale
Supporting millions of queries per second while maintaining performance:
- Load balancing and auto-scaling
- Caching strategies for common queries
- Graceful degradation under load
- Response time SLAs
System Infrastructure Scale
Proper compute resource allocation and architecture designed for failure resilience:
- Redundancy at every layer
- Automated failover
- Geographic distribution
- Monitoring and alerting
Vector Database Selection
For our multi-modal semantic search (Elastiq Pixels), we benchmarked billion-scale embeddings across multiple databases.
Why We Chose OpenSearch
We selected OpenSearch because it:
- Functions as a true distributed system - Not just a single-node solution with replication
- Scales horizontally and vertically - Add nodes or upgrade hardware as needed
- Maintains billion-dataset performance - Proven at the scale we need
- Delivers strong price-performance ratios - Cost-effective for our workload
- Enables fast ingestion and reindexing - Critical for keeping data fresh
The Selection Process
We tested against:
- Pinecone
- Weaviate
- Milvus
- Qdrant
- pgvector
Each has strengths, but for billion-scale multimodal search with complex filtering requirements, OpenSearch provided the best balance of capabilities.
Cost Optimization
Understanding token economics proves essential. One token approximates four characters or one word.
Cost Reduction Levers
Model Selection
Match model capability to specific tasks rather than defaulting to premium models everywhere:
- Classification tasks → Smaller, faster models
- Simple extraction → Fine-tuned small models
- Complex reasoning → Premium models only when needed
Governance and FinOps
Implement proper cost controls:
- Quotas per department/project
- API interceptor layers for monitoring
- Token usage dashboards
- Chargeback mechanisms
Architecture Optimization
Decouple components for independent scaling:
- Separate inference from retrieval
- Cache common queries
- Batch similar requests
- Use async processing where latency allows
Team Investment
“Upskill your Solution Architects & Enterprise Architects…they will be your gateway to save a lot of costs – their ROI is high!”
A skilled architect who prevents unnecessary API calls or selects the right model saves more than their salary in AI costs.
Production Checklist
Before going live, ensure you have:
Testing
- Business metric validation (not just ML metrics)
- Edge case handling documented
- Hallucination detection mechanisms
- Human review process for high-stakes outputs
Infrastructure
- Horizontal scaling capability
- Monitoring and alerting
- Disaster recovery plan
- Geographic redundancy (if needed)
Cost Management
- Token usage tracking
- Budget alerts
- Model selection guidelines
- FinOps review process
Operations
- Runbooks for common issues
- Escalation paths defined
- SLAs documented
- Feedback collection mechanism
Conclusion
Stop chasing AI dreams. Start building real-world solutions.
Select AI solutions strategically where they deliver genuine, measurable business value. Production readiness demands:
- Comprehensive testing frameworks - Beyond accuracy to business impact
- Appropriate infrastructure selection - Matched to your scale requirements
- Quality oversight - Human-in-the-loop for high-stakes decisions
- Disciplined cost-performance management - Not just “make it work”
The most advanced model isn’t always the right choice. The right choice is the one that solves your specific problem reliably, at acceptable cost, with appropriate oversight.