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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 building complex AI platforms, one of the most critical decisions is choosing the right system architecture. Over the past two years, we deployed both monolithic and microservices-based AI systems. Here is a breakdown of what we learned. ## Performance and Resource Allocation - **Monoliths** are excellent for quick startup, simple deployments, and low-latency internal communication. - **Microservices** shine when different components have vastly different compute requirements (e.g., CPU-bound ingestion vs GPU-bound model inference).

Closing Takeaways

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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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