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AI Systemscomplex complexity
AI Application Architecture
Architecture for production AI applications with model serving, RAG pipelines, evaluation, and cost controls.
Architecture
System Components
Key building blocks of this architecture, layered from infrastructure up
01
LLM Gateway
Unified interface for multiple LLM providers with fallbacks. See OpenAI vs Anthropic.
AI SDKLoad BalancingCaching
02
RAG Pipeline
Retrieval-augmented generation with vector search - see the RAG playbook.
PineconeOpenAI EmbeddingsChunking
03
Prompt Management
Version-controlled prompts with A/B testing.
Prompt TemplatesVersioningEvaluation
04
Evaluation System
Automated evaluation of model outputs.
Human EvalLLM-as-JudgeMetrics
05
Cost Management
Token tracking and cost optimization - see the LLM cost insight.
MeteringCachingModel Selection
Planning
Key Considerations
Important factors to keep in mind when implementing this architecture
Design for model provider changes - abstract LLM interfaces
Implement robust evaluation before production deployment - see the shipping AI features playbook
Plan for cost at scale - caching and model selection matter
Need an AI partner? AI integration service.
Options
Alternatives to Consider
Other approaches that might fit your specific needs
LangChain for rapid prototyping
LlamaIndex for document-focused RAG
Vellum or Humanloop for prompt management
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