Why LLM deployments fail after the demo
Most LLM prototypes work in a controlled environment. Production introduces traffic, latency, authentication, rate limits, data privacy, cost spikes, and monitoring problems.
Core production components
A production LLM application needs more than a prompt and an API call.
- Application API layer
- Authentication and access control
- Prompt and configuration management
- Vector database if using RAG
- Logging and observability
- Rate limiting and cost controls
- Fallback and error handling
RAG infrastructure
Retrieval-Augmented Generation requires embedding pipelines, document processing, vector storage, retrieval logic, and relevance monitoring.
- Document ingestion
- Chunking strategy
- Embedding generation
- Vector database
- Retrieval evaluation
- Data refresh workflow
Security and cost control
LLM apps can leak data or burn budget quickly if not designed carefully. Every production deployment needs controls.
- Protect API keys
- Filter sensitive data
- Add user-level access controls
- Monitor token usage
- Set budgets and alerts
- Log safely without exposing private data
Need expert help?
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FAQ
What is needed to deploy an LLM app in production?
You need API infrastructure, authentication, observability, scaling, prompt/version control, data security, vector storage if using RAG, and cost controls.
Do LLM apps always need GPUs?
No. Hosted APIs may not require GPUs, while self-hosted models or private inference often do.