Vector DB Cost · for RAG builders & CTOs
Pinecone vs pgvector vs Qdrant vs 5 more
Managed vs self-hosted. Serverless vs pod-based. Price differences up to 20x for the same workload.
Pricing verified: 2026-06-05
8 vendors compared
What this calculator does
Compare 8 vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma, and more) at your exact workload.
Why use it
- Managed vs self-hosted can differ 10-20x for the same workload
- Pricing models vary wildly — per-vector, per-pod, per-dimension, per-cluster
- Preset workload sizes (100K, 1M, 50M, 1B vectors) show you where cost scales break
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Enter key values
Total vectors (chunks × docs), dimensions, QPS peak, writes/month.
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Or load a preset
Early-stage (100K), Mid-stage (1M), Enterprise (50M), Billion-scale — each loads typical numbers.
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Read the winner
Cheapest option at your workload is highlighted. Managed vs self-host delta shown separately.
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Act on it
At small scale, managed wins on operational cost. At billion-scale, self-host becomes unbeatable.
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Enter workload
Total vectors = (corpus docs × chunks per doc). E.g., 10K docs × 3 chunks = 30K vectors. Dimensions — 1024 typical for Voyage/Cohere, 1536 for OpenAI 3-small, 3072 for OpenAI 3-large. Higher dimensions cost more on some DBs (Weaviate is per-dimension). QPS peak = queries per second during peak traffic. Writes/month = new vectors added each month.
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Pick deployment preference
Managed (Pinecone, Weaviate Cloud, Qdrant Cloud) = no ops burden but higher cost. Self-hosted (pgvector, Qdrant self-host, Milvus) = lower cost but you operate it. "Any" shows all 8 side-by-side. At early scale, the operational time to self-host eats more than the managed premium. At billion-scale, self-host becomes unavoidable on cost.
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Compare all 8
Results table shows monthly + annual cost across all 8 vendors sorted cheapest first. Gold highlight = your current selection. Green = cheapest. Managed vs self-hosted delta is shown — useful for the "is the ops burden worth it" decision. Each vendor's pricing model (per-vector, per-pod, per-dimension, etc.) is shown in the pricing model column.
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Next actions
This covers the DB layer only. For full RAG cost: RAG Pipeline Cost. For embedding model choice: Embedding Cost. For self-host break-even at LLM layer: Self-Host Break-even. If your corpus is growing, re-run this quarterly — you often cross scale boundaries and should switch DBs.
📊 Calculator at a glance
🎛 CALCULATOR
📊 Your vector workload
We'll compute monthly cost across all 8 options.
Number of chunks/embeddings you'll store. 10K docs × 3 chunks = 30K vectors typical.
Higher dimensions = more storage + RAM. Some DBs price per-dimension (Weaviate).
Average QPS during peak usage. 5 QPS = 13M queries/month.
New vectors added each month (incremental indexing).
Load preset
📈 RESULTS
Cheapest option at your workload
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Cheapest managed
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Cheapest self-hosted
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Managed vs self-host
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💡 Recommendations
📋 All 8 vector DB options ranked
Same workload, different vendors. Green = cheapest, gold highlight = your current selection.
| Vector DB | Pricing model | Monthly cost | Annual |
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🎯 Use this result to
- 🗂️ Pick the right vector DB — Pinecone wins under 1M vectors, others at scale. Get a concrete answer for your numbers.
- 📊 See full TCO not sticker — Storage + queries + write costs + managed-service premiums all included.
- ⛰️ Spot scale cliffs — Pricing tiers flip at 5M, 10M, 50M vectors. Calc surfaces them before you commit.
- 🔌 Integrate with your AI agents — MCP available for agentic workflow integration. Cost-aware vector retrieval.
📋 What now?
- Managed vs self-hosted is the big lever — the delta card shows it; below ~1–2M vectors managed usually wins on total cost once you price your ops time.
- Dimensions drive storage — if recall holds, a 1024-dim model over 3072 can cut index size (and some per-dimension DB bills) by roughly two-thirds.
- Size on peak QPS, not average — managed tiers step up on peak throughput; confirm your real peak before committing to a tier.