Guides → Playground & Guide → Vector DB Cost - Pinecone vs Weaviate vs Qdrant vs pgvector

Vector DB Cost - Pinecone vs Weaviate vs Qdrant vs pgvector

🚀 Open the calculator

Meet Vihaan Reddy. Backend Engineer choosing vector storage. "Pinecone is easiest. pgvector is cheapest. What does each actually cost at 5M vectors?"

🔥 Boss wants the cheapest option. Eng team wants Pinecone.

The story

Vector DB pricing has the biggest spread of any RAG component. Same workload (5M vectors, 1M queries/month) costs $1,400/mo on Pinecone Enterprise, $200/mo self-hosted Qdrant on a single VM, $50/mo pgvector on existing Postgres. The 28× spread between Pinecone hosted and pgvector is real - and the 'right' choice depends on operational maturity, not just unit cost.

Vihaan's choice: 5M vectors, ~1M queries/month, expecting 30% growth/year. Pinecone Serverless ($800/mo) is operationally trivial. Self-hosted Qdrant ($200/mo) saves real money but needs SRE attention. pgvector on existing Postgres ($50/mo extra) is cheapest - but locks performance to your Postgres setup and limits scale.

Three real choices, not nine. (1) Hosted SaaS (Pinecone, Weaviate Cloud) for fast-ship + ops-free. (2) Self-hosted dedicated (Qdrant, Milvus, Weaviate self-hosted) for cost-conscious + ops-capable. (3) pgvector for small-scale + Postgres-native. Don't overthink the 9 vendors - the architectural pattern matters more than the brand.

🎮 Playground

Vector DB Cost Playground

Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.

What will a vector database cost?

Storage scales with vectors × dimensions; queries add ongoing cost. Provider fixed at default.

Estimated monthly cost
—

💡Cost = vector storage (count × dimensions) + query volume. Higher dimensions mean more bytes per vector.

Three real scenarios

Same calculator, three team sizes. Click a tab to see how the numbers shift.

$50.00 / month ≈ $600.00 / year

500K vectors, low query volume. Pinecone Serverless ~$25/mo. Setup: 1 hour. Don't optimize for cost yet - ship the feature.

Healthy range: <$30/mo - Pinecone wins on speed

See inputs used
vectorCount
500,000
vectorDimensions
1,536
queriesPerMonth
200,000
storageProvider
pinecone-serverless

Use cases

Same calculator, different applications. Sizes above, workloads here. Pick the one that looks like yours.

Pre-loaded scenarios for the most common applications. Click a tab to see realistic numbers, then hit "Try this scenario" to load it into the calculator above.

$20.00 / month ≈ $240.00 / year

100K vectors. pgvector on your existing Postgres. Marginal cost. Ship in an afternoon.

Healthy range: $15-30/mo on existing Postgres

See inputs used
vectorCount
100,000
vectorDimensions
768
queriesPerMonth
50,000
storageProvider
pgvector

Ready to run the numbers?

Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

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Top vector DB providers

Verified 11 hours ago
  1. 1
    GPT-5 Mini
    $0.250 in · $2.00 out ·
  2. 2
    gpt-5.1-codex-mini
    $0.250 in · $2.00 out ·
  3. 3
    Command
    $1.00 in · $2.00 out ·

About this calculator: Vector DB Cost - Pinecone vs Weaviate vs Qdrant vs pgvector

Vector DB pricing varies 10× between hosted SaaS and self-hosted. Storage cost + query cost + ops overhead. Real math for RAG production.

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.

▸ Total vectors: Total vectors (chunks/embeddings) stored in the index.
How to choose: Docs times chunks per doc; e.g. 10K docs x 3 chunks = 30K vectors.
▸ Vector dimensions: Embedding dimensionality, which drives storage and RAM.
How to choose: Match your embedding model: 1024 Cohere/Voyage, 1536 OpenAI 3-small, 3072 3-large.
▸ Queries per second (peak): Peak queries per second the index must serve.
How to choose: Use peak, not average; 5 QPS is about 13M queries per month.
▸ Vector writes per month: New vectors added per month via incremental indexing.
How to choose: Estimate ongoing ingestion; large spikes push managed-tier pricing up.
▸ Deployment preference: Filter results to managed, self-hosted, or all options.
How to choose: Managed means no ops; self-hosted is lowest dollar cost if you have the team.
📋 Typical values & starting points Don’t know a value yet? Start with these broad, sourced ballparks — the calculator’s ▾ Typical menus pre-load the same options.
Input Default Typical ballparks
vectorCount moves the needle 5,000,000 —
vectorDimensions 1,536 —
queriesPerMonth moves the needle 1,000,000 —
storageProvider pinecone-serverless —

Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers. Try them live in the calculator; API & agent users get the same data from the MCP resource aicost://input-reference/vector-db-cost.

📊 CALCULATOR AT A GLANCE
🚀 Open the full calculator ✉️ Email [email protected]
Vector DB Cost - Pinecone vs Weaviate vs Qdrant vs pgvector full size

Reading your result

Pinecone Serverless ≈ $0.80-1.20/M queries + $0.30/GB-month storage. 5M vectors at 1536 dims = ~30GB = $9 storage. 1M queries = $1. Plus baseline = $40 minimum. Total ~$50-100/mo at this scale.

Self-hosted Qdrant ≈ $150-300/mo on a $200 VM. Add maintenance overhead (SRE time). Operationally meaningful at scale.

pgvector ≈ marginal cost on existing Postgres. Adds ~30GB to Postgres + a few CPU cycles per query. Often $20-100/mo extra on existing setup. Best for <10M vectors and simple use cases.

Weaviate Cloud is similar to Pinecone - premium hosted, slightly different pricing model (per-pod vs per-query). Comparable total at this scale.

What "good" looks like:
  • Hosted SaaS: $50-2K/mo at 5M vectors. Fast ship, ops-free, expensive at scale.
  • Self-hosted dedicated: $100-400/mo. SRE time required.
  • pgvector: $20-100/mo. Best for <10M, simple cases.
  • Switching cost: 2-4 weeks engineering, painful at scale.

What this calculator can't tell you

Honest limitations. Every model is wrong; some are useful. Where this one falls short:

For these, use: Embedding Cost for upstream. RAG Pipeline for downstream.

Trade-offs

Cost isn't the only dimension. Click any constraint to see how recommendations change.

What matters most to you? Click any dimension — recommendations update.

Best fit for "cost":

  1. pgvector Cheapest for <10M vectors
  2. Qdrant self-hosted Cheapest for >10M vectors
  3. Pinecone Serverless Cheapest hosted

Cost ranking depends on scale. <10M vectors: pgvector wins. 10M-1B: Qdrant or Milvus self-hosted. Hosted SaaS only justifiable for fast-ship or sub-10M with no Postgres.

Most popular

Everything above is the 80% case. The last 20% is where the money is.

The gaps we just listed are real, and they are the expensive ones: your actual prompts, your switching cost, your MLOps overhead. An AICost expert spends the hour on your AI and cloud costs, not a generic playbook. You leave with a written report: the way forward, in 30 days of concrete steps.

  • An hour with the people who built the engines
  • Report the same day
  • The fee credits toward any AICost plan
  • Two slots a week
Book a Solution Session: $299 → or $99 for small business →

Not sure yet? The $39 AICost Blueprint credits toward a Session, and the Session fee credits toward any plan. You never pay twice for the same ground. See all pricing →

Ready to run your own numbers?

You have seen the shape of it. Open the calculator with your model, your tokens, your volume.

🚀 Open the full calculator →

Where to go next

Cost the embedding side →

Indexing + query embedding costs.

 Full RAG architecture cost →

Embedding + vector DB + retrieval + LLM.

 Self-host break-even math →

Same logic applies to vector DB self-host vs SaaS.

Methodology

Source
https://docs.pinecone.io/docs/pricing
Extraction
Vendor pricing pages monitored quarterly. Self-hosted infra cost from cloud calculators.
Editorial gate
8-layer defense, see aicost.ai/ai-cost-economics
Last verified
7/27/2026, 8:00:00 PM

Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.

3 years of pricing history

Why this matters: pricing for major vendors has dropped 40-90% in the last 24 months. A budget set 12 months ago is probably wrong by 30%+.

View 3-year history for →
📖 Data sources & methodology 163 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-28

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-28.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-07-28
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Anthropic Docs
2026-07-28
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 631 days captured
OpenAI
2026-07-28
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 632 days captured
Google AI
2026-07-28
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 607 days captured
Google Vertex
2026-07-28
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 607 days captured
DeepSeek
2026-07-28
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 546 days captured
xAI
2026-07-28
https://x.ai/api
Daily snapshot since Nov 2024 · 464 days captured
Mistral
2026-07-28
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 605 days captured
Cohere
2026-07-28
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 631 days captured
Voyage AI
2026-07-28
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →