What is the cheapest way to store and retrieve vector embeddings for two million customer support tickets without breaking the bank
Download run
Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
What is the cheapest way to store and retrieve vector embeddings for two million customer support tickets without breaking the bank
3 real searches · 1 real-search pass
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 3 queries…
Searches and sources
ChatGPT3 real searches · gpt-6
pgvector official documentation HNSW IVFFlat half precision vector storage PostgreSQL Google ↗
Qdrant self hosted quantization documentation storage requirements Google ↗
Pinecone pricing serverless vector database pricing 2026 Google ↗
- Vector database pricing compared: Pinecone, Qdrant, Weaviate, Zilliz, turbopuffer, Supabase, Neon, self-hosted · c17.io
- Vector Database Pricing — Pinecone vs Weaviate vs Qdrant · getaicost.com
- Vector Database Cost Calculator – Pinecone · wati.dev
- GitHub - pgvector/pgvector: Open-source vector similarity search for Postgres · github.com
- EDB Docs · enterprisedb.com
- Installation · qdrant.tech
- Capacity Planning · qdrant.tech
- Supabase pricing · supabase.com
- Vector indexes · supabase.com
Queries
3 queries · click a column to sort
| Query / type | Search | Intent | Query class | Routing format | Cluster | Freq ↓ | Weight |
|---|---|---|---|---|---|---|---|
pgvector official documentation HNSW IVFFlat half precision vector storage PostgreSQL OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Qdrant self hosted quantization documentation storage requirements OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 | |
Pinecone pricing serverless vector database pricing 2026 OBSERVED | Google ↗ AI Mode ↗ | — | — | — | — | 1x of 1 |