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ARCHITECTURE

Microservices vs Monolith for AI Systems: What We Learned

A practical comparison based on real production deployments and scalability requirements.

RD
Renee D.
AI Engineer, Botmartz · May 5, 2024 · 7 min read
◈ Open in Google Colab⌥ View Code on GitHub
Read Time
7 min
Failure Modes
5
Code Snippets
3
Runnable Notebook
1
Botmartz AI Insight
Evaluating Retrieval, Chunking, and Generation in Production
Retrieval QualityChunking StrategyHybrid SearchContinuous EvaluationGuardrailsTakeaways
# Microservices vs Monolith for AI Systems: What We Learned When designing scalable AI architectures, teams often face the trade-off between monolithic simplicity and microservices agility. ## Architectural Trade-offs 1. **Monolithic AI Applications**: Easier local debugging, shared memory space, simple CI/CD pipelines, but harder independent scaling of heavy ML inference. 2. **Microservices Architecture**: Separate services for embedding generation, vector search, and model inference allow targeted GPU/CPU auto-scaling and independent deployments.

Closing Takeaways

✓
Measure retrieval precision and recall in isolation before touching the model.
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Chunk along document structure, not arbitrary character counts.
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Combine vector and keyword search — hybrid retrieval beats either alone.
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Treat evaluation as continuous infrastructure, not a launch-week report.
Try It Yourself
A runnable Google Colab notebook with the eval harness and hybrid search code from this post.
◈ Open in Colab⌥ GitHub Repo
#Enterprise RAG#Evaluation#Production AI#LangChain
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RD
Renee D.
AI Engineer at Botmartz, building enterprise RAG and agent systems in production. Contributing to open-source libraries.

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