Cost-Performance Trade-offs: Finding the ROI

In production AI, you aren't just optimizing for "Best." You are optimizing for "Value." Is it worth paying an extra $500/month for a vector database that is 10ms faster? Is it worth increasing your RAM usage by 4x to gain 2% better retrieval accuracy?

In this final performance lesson, we look at the Economics of Vector Search.

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1. Comparing the Cost of Indexing

Different indexing strategies (Module 3) have wildly different costs:

• Flat Index (Exact):
  • Cost: 0 RAM/Storage overhead.
  • Performance: 1/10 Speed.
  • Value: Use for datasets < 50,000 items.
• HNSW (Approximate):
  • Cost: High RAM overhead (3-5x the size of the vectors).
  • Performance: 100x Speed.
  • Value: Necessary for datasets > 1M items.

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2. Dimensionality vs. Bill

If you move from a 384-dim embedding to a 1536-dim embedding, you are literally Quadrupling your database bill in most managed services.

The Question: Does the 1536-dim model actually improve your user's experience by 4x? Often, it doesn't.

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3. High-Precision vs. Quantized (PQ)

Product Quantization (PQ) can reduce a 1536-dim vector to the equivalent of ~16-32 bytes.

• Save: 90% in storage/RAM costs.
• Lose: 5-10% in recall accuracy.

In many consumer apps (e.g., "Find similar recipes"), a 5% loss in accuracy is invisible. In Medical or Legal apps, it's unacceptable.

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4. Total Cost of Ownership (TCO) Calculator

When choosing a vector database strategy, calculate your TCO: TCO = [Embedding API Cost] + [DB Monthly Fee] + [Worker Compute for ingestion]

The Optimization Lever:

• If Embedding API is 80% of your cost -> Move to local embeddings.
• If DB Fee is 80% of your cost -> Apply Product Quantization or move to a self-hosted Chroma instance.

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5. Summary and Key Takeaways

1. Flat for Small: Don't over-engineer with HNSW for tiny datasets.
2. Quantize for Scale: If your RAM bill is too high, use PQ.
3. Accuracy is a Variable: Define what "Good enough" looks like before you chase the "Best" benchmarks.
4. Local vs API: The speed and cost of embedding (the query side) is often a bigger bottleneck than the database itself.

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Exercise: The Budget Audit

1. You have a budget of $100/month.
2. You have 500,000 documents.
3. Each document needs to be searchable by sentence.
4. The Task: Research the cost of Pinecone vs. AWS OpenSearch vs. a local Chroma instance on a $20/month VPS.
5. The Decision: Which one allows you to stay under budget while maintaining 90% recall?

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Congratulations on completing Module 14! You now understand the technical and financial physics of vector search.