Notebook results: hours. Reliable production: months. Costs scale linearly, latency frustrates, hallucinations erode trust, prompt regressions break silently. Each has proven solutions.
Provider abstraction with failover.| Reasoning | GPT-4/Opus | $0.01-0.03 | 2-8s |
| Chat | 4o-mini/Haiku | $0.0002 | 0.5-2s |
| Classify | FT-3.5/Mistral | $0.0005 | 0.3s |
| Embed | embed-3-sm | $0.00002 | 0.1s |
| Code | GPT-4/Sonnet | $0.005 | 1-5s |
$45K to $12K monthly by routing 70% to smaller models and caching 15%. Quality improved.
Disciplined engineering beats sophisticated models. Instrument everything, right-size always.
Deploying LLMs in production requires a gateway pattern with semantic caching (saving 30-50% on costs), multi-provider fallback for reliability, and prompt version control since 70% of incidents stem from prompt regression. The smallest model meeting quality requirements should be used, as GPT-4 class models are overkill for roughly 60% of production use cases.
Key Takeaways
- Smallest model meeting quality — GPT-4 overkill 60%
- Semantic caching saves 30-50%
- Every call needs fallback
- Prompts need version control
- Tokens under 30% of total cost
Frequently Asked Questions
Key Terms
- Semantic Caching
- Vector matching for cached responses.
- Prompt Engineering
- Designing instructions for model outputs.
- Hallucination
- Model generating incorrect but confident text.
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Production LLMs need gateway patterns, caching, fallback, cost management, quality monitoring. From dozens of deployments serving millions of requests.