What is the most affordable way to implement retrieval augmented generation without getting locked into expensive managed vector database tiers like Pinecone's standard plan?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the most affordable way to implement retrieval augmented generation without getting locked into expensive managed vector database tiers like Pinecone's standard plan?
1 real search · 1 real-search pass
2026 pgvector PostgreSQL extension documentation HNSW IVFFlat; Qdrant self-hosted open source pricing; Chroma open source deployment; SQLite vector search sqlite-vec documentationGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
2026 pgvector PostgreSQL extension documentation HNSW IVFFlat; Qdrant self-hosted open source pricing; Chroma open source deployment; SQLite vector search sqlite-vec documentation Google ↗
- Qdrant documentation · qdrant.tech
- Chroma documentation · docs.trychroma.com
- LanceDB website · lancedb.com
- BAAI models · huggingface.co
- Nomic models · huggingface.co
- Sentence Transformers · sbert.net
- GitHub - pgvector/pgvector: Open-source vector similarity search for Postgres · github.com
- EDB Docs · enterprisedb.com
- Vector indexes · supabase.com
Queries
1 queries · click a column to sort