In-House RAG vs Managed Retrieval: Bedrock Knowledge Bases vs Vertex AI Search vs Custom
Building your own RAG stack gives you control and portability at the cost of engineering time. Managed retrieval services get you to production faster and inherit the hyperscaler ops budget, at the cost of lock-in and less capability tuning. The right choice depends on how differentiated your retrieval needs are.
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What we're comparing
- In-house RAG
Custom stack: your choice of vector DB (Pinecone / Weaviate / Qdrant / pgvector / Milvus) + your own chunking + your own hybrid retrieval + your own re-ranker. Full control, full ownership.
- Managed retrieval
Hyperscaler-managed service: AWS Bedrock Knowledge Bases, Google Vertex AI Search, Azure AI Search. Point at your data source, they handle chunking, embedding, storage, and retrieval.
Side-by-side
| Attribute | In-house RAG | Managed retrieval |
|---|---|---|
| Time to first production RAG | 4-12 weeks | Days to 2 weeks |
| Chunking control | Full — semantic, structural, custom | Limited — provider defaults + a few knobs |
| Retrieval strategy | Hybrid + re-ranker + custom filters | Vendor default (usually hybrid + re-ranker) |
| Cost model | Vector DB + compute + eng time; scales with query volume | Per-query + per-GB indexed; scales with usage |
| Lock-in | None (portable) | High (tied to hyperscaler) |
| Access controls | Metadata-filter or ACL propagation as you build it | Vendor-provided; check whether it satisfies your model |
| Best for | RAG is core to your product; custom chunking / re-ranking is required; multi-cloud or on-prem constraints | RAG is a supporting capability; you already run on the vendor cloud; time-to-value matters most |
When to use which
- Use In-house RAG
You are building a product where retrieval quality is a competitive differentiator, or you have constraints (multi-cloud, air-gapped, unusual data types) that managed services do not handle.
- Use Managed retrieval
You need RAG as supporting infrastructure for an existing workflow, you already live on one hyperscaler, and standard retrieval quality is sufficient.
FAQs
Can we start managed and migrate later?
Yes but the migration cost is non-trivial — you rewrite ingestion, re-embed the corpus, port ACL logic. Design for portability from day one if you might migrate: canonical source-of-truth outside the vendor, versioned embeddings, thin abstraction over retrieval calls.
What is the quality gap in practice?
Well-implemented in-house RAG typically outperforms managed on domain-specific corpora by 10-30% recall@10 (measured with a labelled query set). Managed wins by 10-40% on time-to-first-production. Choose based on which axis matters more for your context.
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Next steps
- Take the assessment— See where you stand