A vector database sizing calculator helps AI engineers plan their embedding infrastructure before deployment. Underestimating storage and RAM requirements is one of the most common causes of production failures in RAG systems. This tool calculates storage needs, RAM requirements, QPS capacity, and monthly costs across the leading vector database providers so you can choose the right solution for your scale.
Vector Configuration
Total chunks/documents you plan to index
Source URL, timestamps, tags, IDs (typical: 100–500 bytes)
Infrastructure Requirements
Provider Cost Comparison
Monthly cost estimates at your scale (approximate 2026 pricing)
| Provider | Type | Monthly Cost | Storage Limit | Notes |
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Scaling Notes
Note: Pricing estimates are based on approximate 2026 rates and change frequently. Verify current pricing with each provider before planning your budget. Self-hosted costs assume standard cloud VM pricing and do not include operational overhead.
How to Use the Vector Database Sizing Calculator
Choosing and sizing a vector database is one of the most consequential infrastructure decisions in a RAG system. Undersizing leads to performance degradation or failed deployments; oversizing wastes budget. This vector database sizing calculator gives you concrete storage, RAM, and cost estimates before you commit to a provider or hardware configuration.
Step 1: Enter Your Vector Count
Start with the total number of vectors you plan to index. This is the total number of chunks produced by your document corpus. If you plan to index 1,000 documents with average 50 chunks each, enter 50,000 vectors. Account for growth — if you expect your corpus to double in 12 months, size for the larger number. Adding vectors to most databases is easy; running out of capacity in production is painful.
Step 2: Select Embedding Dimensions
Choose the embedding model you plan to use. OpenAI's text-embedding-3-small generates 1,536-dimension vectors; text-embedding-3-large generates 3,072 dimensions. BGE-large and E5-large use 768 dimensions. Smaller dimension counts dramatically reduce storage and RAM: going from 3,072 to 1,536 dimensions halves the vector storage requirement. OpenAI's Matryoshka embeddings allow you to use truncated dimensions (e.g., 512 dimensions from text-embedding-3-small) with only minor quality loss — worth evaluating for cost-sensitive deployments.
Step 3: Configure Metadata and Index Type
Metadata is stored alongside each vector for filtering and display. Common metadata includes source URL, document title, chunk index, creation timestamp, and custom tags. A typical metadata payload is 100–500 bytes per vector. For index type: HNSW is the best default for most use cases — it provides excellent speed-recall balance and is supported by all major vector databases. Flat (exact) search is only practical for under 100K vectors. IVF can be used for very large datasets where approximate search at slightly lower recall is acceptable.
Understanding Storage Requirements
Raw vector storage is dimensions × 4 bytes × vector_count. For 1M vectors at 1,536 dimensions: 1,536 × 4 × 1,000,000 = 6.14 GB raw. Add metadata (200 bytes × 1M = 200 MB) for about 6.3 GB. A HNSW index adds 30–50% overhead for the graph structure, bringing the total to approximately 9 GB. For in-memory serving, you need RAM at least equal to the total storage. Memory-mapped (mmap) mode keeps only the index graph in RAM (~30% of total), serving the vectors from disk.
Choosing Between Managed and Self-Hosted
Managed services (Pinecone, Weaviate Cloud, Qdrant Cloud) handle infrastructure, scaling, and reliability automatically but cost more per GB. Self-hosted solutions (Qdrant, Chroma, pgvector on your own servers) have near-zero marginal storage costs but require DevOps expertise. At under 10M vectors, managed services often make sense — the engineering time saved exceeds the cost premium. Above 50–100M vectors, self-hosted solutions typically become significantly more economical. pgvector is a unique choice: if you already run PostgreSQL, it adds vector search with zero additional infrastructure and integrates seamlessly with your existing relational data.
QPS and Throughput Planning
QPS (queries per second) capacity depends on RAM, CPU cores, and vector count. A rule of thumb for in-memory HNSW: expect 200–800 QPS per CPU core for 1M vectors. At 10M vectors, expect 50–200 QPS per core. For high-traffic applications (over 1,000 QPS), plan for horizontal scaling with multiple nodes or choose a managed service with auto-scaling. For typical RAG applications where users submit queries through a chat interface, 10–100 QPS is usually sufficient even for mid-size teams.
Frequently Asked Questions
Is this vector database sizing calculator free to use?
Yes, completely free with no signup required. All calculations run locally in your browser — no data is transmitted anywhere. Use it to plan any vector database deployment.
Is my data private when using this tool?
Absolutely. All calculations happen in your browser with no network requests. Your vector count, dimension settings, and infrastructure configurations are never sent anywhere.
How is vector database storage calculated?
Each vector requires (dimensions × 4 bytes) for 32-bit float storage. A 1,536-dimension OpenAI embedding uses 6,144 bytes (6 KB) per vector. Add metadata overhead per vector, then multiply by total vector count. A HNSW index adds approximately 30–50% overhead for the index graph structure on top of the raw vector data.
How much RAM does a vector database need?
For fast in-memory serving, the entire vector index should fit in RAM. This means RAM ≈ total storage (vectors + index overhead). Some databases (Qdrant, Weaviate) support disk-based serving with memory-mapped files, requiring only the HNSW graph in RAM (~30% of total). Cloud providers like Pinecone manage this automatically with their serverless tier.
What is the difference between Pinecone, Qdrant, and pgvector?
Pinecone is a fully managed cloud service — easiest to start, most expensive at scale. Qdrant is open-source and can be self-hosted (free) or used via their cloud; it has excellent performance and Rust-based efficiency. pgvector is a PostgreSQL extension — great if you already run Postgres, zero additional infrastructure. Weaviate is open-source with a managed cloud option, featuring built-in ML model integration. Chroma is optimized for local development and small-scale production.
How many vectors can fit in 1 GB of storage?
For 1,536-dimension OpenAI embeddings (float32), each vector takes 6,144 bytes. With 100 bytes of metadata, that's ~6.1 KB per vector. 1 GB stores roughly 163,000 raw vectors. After adding HNSW index overhead (~40%), 1 GB stores approximately 116,000 searchable vectors. For 768-dimension models (BGE, E5), 1 GB holds approximately 230,000 vectors.
How does the number of dimensions affect performance and cost?
More dimensions = larger storage and RAM footprint, slower query time, and higher cost. OpenAI's text-embedding-3-large uses 3,072 dimensions (2× the storage of 3-small's 1,536). However, higher-dimension embeddings generally have better semantic precision. Many teams use Matryoshka embeddings that allow truncating dimensions without a major quality loss — text-embedding-3 supports this, letting you use 256 or 512 dimensions to cut costs dramatically.
What QPS (queries per second) can I expect from a vector database?
QPS varies enormously with hardware, index type, and vector count. In-memory HNSW on modern hardware typically delivers 100–2,000 QPS for 1M vectors per CPU core. Pinecone's serverless tier handles burst traffic automatically. For self-hosted solutions, a single 8-core server with 32 GB RAM can typically serve 500–1,500 QPS for 10M 1,536-dimension vectors.
When should I choose pgvector vs a dedicated vector database?
Choose pgvector when: you're already running PostgreSQL, your vector count is under 1–5M, and you want to query vectors alongside relational data. Choose a dedicated vector DB (Pinecone, Qdrant, Weaviate) when: you need over 10M vectors, require advanced filtering or multi-tenancy, need high QPS with low latency, or want a fully managed service. At smaller scales, pgvector is often the most cost-effective choice.