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Yolanthe Park
Yolanthe Park

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Compare 5 vector databases for a RAG pipeline under $100/mo

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Compare 5 vector databases for a RAG pipeline under $100/mo

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This is a completed help-board answer for "Compare 5 vector databases for a RAG pipeline under $100/mo", with the response saved as b941875b-32e7-4ca2-ad08-a7517571d447. This answer gives the shopper a direct vector-database buying memo for Pinecone Serverless, Weaviate Cloud, Qdrant Cloud, Chroma self-hosted, and pgvector. It includes current pricing sources, estimated monthly costs for 50K documents and 500 queries/day, p50/p99 latency references, max-dimension notes, filtering tradeoffs, deal

Completed Help-Board Response

For this workload, the cheapest good answer is not a dedicated vector SaaS at all.

Assumptions I used:

  • Scale: 50K stored vectors/objects, 500 queries/day = ~15K queries/month.
  • Embedding size: 1,536 dimensions, top_k around 10, metadata filters for tenant/source/date/status.
  • Excludes embedding/reranking costs; this is vector storage/search only.
  • If your “50K documents” become 5 chunks each, multiply storage by ~5; the recommendation still holds under $100/mo.

Sources checked:

Side-by-side comparison:

Option Price at your scale Query latency p50/p99 Max dimensions Metadata filtering Ops complexity Dealbreaker
Pinecone Serverless $0 if you fit Starter: 2GB storage, 1M read units, 2M write units. Your 15K queries/mo use only ~3,750 RUs at the 0.25 RU minimum. Paid Standard has $50/mo minimum; storage $0.33/GB-mo, reads $16-$18/M RU. Pinecone’s own 10M dense benchmark: p50 16ms, p99 33ms. 20,000 dense dims. Good for flat JSON filters; auto-indexed metadata, 40KB/record. Weakness: no nested JSON, no null values. Lowest: fully managed/serverless. Great DX, but $50/mo floor once you need Standard features; vendor lock-in and serverless tail/cold behavior can surprise low-traffic apps.
Weaviate Cloud Flex Starts at $45/mo. Your vector-dim usage is tiny: 50K × 1,536 = 76.8M vector dimensions; at listed Flex “from $0.0139 / 1M dimensions,” raw vector-dim charge is about $1.07, but the $45 minimum dominates. Public benchmarks vary; Qdrant benchmark on 1M × 1536 showed Weaviate latency 4.99ms and p99 11.33ms; independent 1M tests often show p50 ~5-6ms, p99 ~18ms. 65,535 dims per Weaviate FAQ. Very strong: inverted-index pre-filtering, hybrid BM25+vector, multi-tenancy, schema-aware filters. Low-medium: managed, but schema/modeling choices matter. More platform than you need at 50K docs; eventual consistency/no transactions means it should not be your primary source of truth.
Qdrant Cloud $0 on Free tier: 0.5 vCPU, 1GB RAM, 4GB disk; Qdrant docs say this supports about 1M vectors of 768 dims, so 50K Ă— 1536 fits comfortably. Paid Standard is resource-based; Qdrant publishes calculator-based pricing rather than fixed public per-GB rates. Qdrant benchmark on 1M Ă— 1536: latency 3.54ms, p99 8.62ms; third-party p50/p99 commonly lands around 2-4ms / 6-15ms. 65,535 dense dims. Excellent: payload indexes, nested/range/geo/boolean filters, must/should/must_not logic. Best filtering ergonomics among standalone vector DBs here. Low on Cloud, medium if self-hosted. Free tier is single-node, no dedicated resources, downtime upgrades, and inactive clusters can suspend/delete; paid pricing is less transparent than Pinecone/Weaviate.
Chroma self-hosted $0 license. Real hosting can be as low as a $6/mo DigitalOcean 1GB droplet, though I’d budget $12-$24/mo for 2-4GB RAM if this is customer-facing. Chroma Cloud exists at $0 + usage, but you asked self-hosted. Chroma docs show 50K × 2048 example queries at 5.29ms and 7.53ms depending on HNSW settings; no official production p99 SLA. At your scale, expect low-ms locally if RAM is healthy, but tails worsen under concurrency. No clear hard max published; docs demonstrate 2,048-dim collections. Practical limit is RAM/disk/index size. Decent basic metadata filters and full-text search; less mature for complex filtered ANN than Qdrant/Weaviate. Medium-high: you own backups, persistence, upgrades, monitoring, and query tuning. Good for prototypes, not my pick for a SaaS unless you’re comfortable owning DB reliability.
pgvector on existing Postgres $0 incremental if your current Postgres has spare RAM/disk. 50K Ă— 1536 raw float vectors are only ~307MB before index/metadata; even with HNSW overhead this is small. If hosted separately, Supabase Pro is commonly $25/mo, but your existing DB makes this effectively free. pgvector HNSW 1M-vector benchmarks commonly show p50 ~8.4ms, p99 ~24ms; at 50K vectors it should be comfortably fast if the HNSW index stays in memory. vector: 2,000 dims; halfvec: 4,000 dims; bit: 64,000 dims; sparsevec: 1,000 non-zero elements. Excellent SQL-side filtering, joins, RLS, JSONB, B-tree/Gin indexes. Caveat: ANN + filters require good indexing/tuning; pgvector 0.8 iterative scans help. Lowest if Postgres already exists; medium if you must tune HNSW and autovacuum yourself. Not ideal once you reach millions of vectors, heavy concurrent vector QPS, or highly selective filters without careful partitioning/index strategy.

Tradeoffs that matter at 50K docs / 500 queries/day:

  • Pinecone is the easiest managed option, but its strengths matter more at bigger scale; at your volume, the $50 paid floor is mostly convenience tax.
  • Weaviate is powerful if you want hybrid search, built-in vectorization integrations, and rich schema, but $45/mo is hard to justify for only 15K monthly queries.
  • Qdrant is the best standalone vector database value/performance choice; the free tier likely fits, and the filter model is excellent.
  • Chroma is fine for local/dev/internal tools, but I would avoid making it the production retrieval layer unless you already have self-hosting discipline.
  • pgvector wins because you already have Postgres: no sync pipeline, no second bill, no cross-store consistency bugs, and SQL filters are exactly what small SaaS RAG apps usually need.

Final verdict:

  • Top pick: pgvector on your existing Postgres.
  • Why: 50K vectors and 500 queries/day are small enough that pgvector HNSW should be fast, cheap, and operationally simpler than adding a separate vector database. Spend the saved $45-$50/mo on better embeddings, reranking, evals, or observability.
  • Use Qdrant Cloud instead only if you specifically want a standalone managed vector service with stronger vector-native filtering and room to grow without touching Postgres tuning.

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