Published 2026-04-10 · Not yet reviewed · 11 services compared · Figures compiled 2026-04-10, not re-checked since · 3 pricing changes tracked
The vector database landscape in April 2026: 11 services across five categories — dedicated cloud vector DBs, open-source self-hosted, PostgreSQL + pgvector, embedded/local, and serverless. Vector databases are the fastest-growing infrastructure category in developer tooling, driven by RAG pipelines and AI applications.
Key trends: pgvector has democratized vector search — Supabase, Neon, and any PostgreSQL instance now support similarity search with zero extra infrastructure. Dedicated vector DBs (Pinecone, Qdrant, Weaviate) differentiate on scale, performance, and advanced features like hybrid search and reranking. The open-source ecosystem is strong: Qdrant, Milvus, Chroma, and LanceDB are all fully free to self-host. Serverless options (Upstash Vector, Turbopuffer) offer pay-per-use with no infrastructure management.
This guide covers: pricing tables, category breakdowns, vector storage and dimension analysis, self-hosted vs managed cost comparison, cost analysis for solo developers and teams, hidden costs, and best-for-use-case recommendations — compiled by hand from vendor pricing pages.
All prices verified as of April 2026. Hover rows to highlight. Click service names for full vendor profiles with free tier details.
| Service | Type | Free Vectors | Free Storage | Dimensions | Paid From | Model |
|---|---|---|---|---|---|---|
| Pinecone | Managed (Serverless) | ~1M (2 GB) | 2 GB | Up to 20,000 | $0.33/M reads (Standard) | Usage-based (read/write units) |
| Qdrant Cloud | Managed (Dedicated) | ~1M (1 GB) | 1 GB | Unlimited | ~$10/mo | Per-cluster (RAM/disk) |
| Weaviate Cloud | Managed (Multi-model) | Sandbox only | Sandbox (14-day) | Unlimited | $25/mo (Shared) | Per-cluster |
| Zilliz Cloud | Managed Milvus | ~5M (5 GB) | 5 GB | Up to 32,768 | $65/mo (Standard) | Compute units (CU) |
| Chroma | Embedded / Cloud | Unlimited (self-hosted) | Unlimited (self-hosted) | Unlimited | Cloud pricing TBA | Self-hosted: free; Cloud: usage-based |
| LanceDB | Embedded (Lance format) | Unlimited (OSS) | Unlimited (OSS) | Unlimited | Cloud: usage-based after $100 credits | Self-hosted: free; Cloud: usage-based |
| Upstash Vector | Serverless (HTTP) | 10K | ~10K vectors | 1,536 | $0.4/100K queries (Pay-as-you-go) | Per-query + per-vector |
| Turbopuffer | Serverless (S3-backed) | None (pay-per-use) | None | Unlimited | $0.30/M vectors/mo stored | Pay-per-use (storage + queries) |
| Supabase pgvector | PostgreSQL + pgvector | ~100K (within 500 MB) | 500 MB (shared with all data) | Up to 2,000 | $25/mo (Pro) | Per-project |
| Neon pgvector | PostgreSQL + pgvector (Serverless) | ~50K (within 512 MB) | 512 MB (shared with all data) | Up to 2,000 | $19/mo (Launch) | Usage-based |
| MongoDB Atlas Vector Search | Document DB + Vector Search | ~100K (within 512 MB) | 512 MB (M0 cluster) | Up to 4,096 | $57/mo (M10 Dedicated) | Per-cluster |
Vector databases fall into five categories, each optimized for different scale, deployment model, and integration needs.
Self-hosting can dramatically reduce costs but requires ops expertise. Here is the honest comparison.
| Approach | Cost (1M vectors) | Cost (10M vectors) | Ops Burden | Best For |
|---|---|---|---|---|
| Qdrant self-hosted | $5–10/mo (VPS) | $20–50/mo | Medium (Docker) | Cost-sensitive teams with ops skills |
| Milvus self-hosted | $15–30/mo (VPS) | $50–100/mo | High (etcd, MinIO, Pulsar) | Large-scale with dedicated infra team |
| Chroma self-hosted | $5–10/mo (VPS) | $20–40/mo | Low (single binary) | Prototypes, small-to-medium RAG |
| pgvector (self-hosted PG) | $5–15/mo | $30–80/mo | Medium (PG admin) | Teams already running PostgreSQL |
| Pinecone (managed) | $0 (free tier) | $70–200/mo | None | Zero-ops requirement |
| Qdrant Cloud (managed) | $0 (free tier) | $30–100/mo | None | Easy start with OSS escape hatch |
| Zilliz Cloud (managed) | $0 (free tier) | $65–200/mo | None | Milvus users wanting managed service |
What does each vector database cost for a solo developer versus a team building production RAG?
| Service | Solo / Prototype /mo | Team / Production /mo | Key Cost Factor |
|---|---|---|---|
| Pinecone | $0 | $70+ (Standard) | Usage-based (read/write units) |
| Qdrant Cloud | $0 | $10–50 | Per-cluster (RAM/disk) |
| Weaviate Cloud | $25 | $45+ (Flex) | Per-cluster |
| Zilliz Cloud | $0 | $65+ | Compute units (CU) |
| Chroma | $0 (self-hosted) | $0–50 (self-hosted + cloud TBA) | Self-hosted: free; Cloud: usage-based |
| LanceDB | $0 (self-hosted) | $0–100+ (cloud) | Self-hosted: free; Cloud: usage-based |
| Upstash Vector | $0–5 | $10–30 | Per-query + per-vector |
| Turbopuffer | $0.30–5 | $5–50 | Pay-per-use (storage + queries) |
| Supabase pgvector | $0 | $25/project | Per-project |
| Neon pgvector | $0 | $19+ | Usage-based |
| MongoDB Atlas Vector Search | $0 | $57+ | Per-cluster |
Vector database pricing has unique gotchas beyond the sticker price.
The vector database is only half the cost. You also pay to generate embeddings. OpenAI text-embedding-3-small: $0.02/M tokens (~$0.01 per 10K documents). Cohere embed-v3: $0.10/M tokens. For 1M documents, embedding costs can exceed vector storage costs. Consider open-source embedding models (Sentence Transformers, Nomic) to eliminate this recurring cost.
Higher-dimension embeddings (3,072 for OpenAI text-embedding-3-large) use ~4x more storage than lower-dimension ones (768 for many open-source models). This directly multiplies your storage costs. For most RAG applications, 768–1,536 dimensions provide sufficient quality at 2–4x lower cost. Use Matryoshka dimensionality reduction (supported by modern embedding models) to tune this trade-off.
HNSW indexes (used by most vector DBs) consume significant CPU and memory during construction. On free tiers with limited compute, indexing 100K+ vectors can take hours. Some services (Pinecone, Turbopuffer) handle this transparently; self-hosted solutions require you to provision sufficient RAM (typically 2–4x the vector data size for HNSW).
Free-tier performance varies dramatically. Pinecone Starter and Qdrant free clusters share resources — p99 latency can spike during peak hours. Serverless options (Turbopuffer, Upstash) have cold-start latency. pgvector on shared Postgres (Supabase free, Neon free) competes with other queries for CPU. Budget for dedicated compute if sub-100ms p99 latency matters.
Proprietary vector databases (Pinecone, Turbopuffer) have no standard export format — migrating requires re-indexing all vectors. Open-source solutions (Qdrant, Milvus, Chroma) let you export and move freely. pgvector stores vectors in standard PostgreSQL — pg_dump includes your vector data. If portability matters, weight open-source or pgvector-based solutions.
Vector database pricing is evolving rapidly as the market matures. See full change timeline for all tracked changes.
| Date | Vendor | Change | Impact |
|---|---|---|---|
| 2026-08-28 | Weaviate | pricing_restructured | The free tier now has specific limits: 100,000 objects, 1 GB memory, 10 GB disk, 1 collection, up to 3 tenants, 2,000 embedding requests/day, and 1,000 Query Agent requests/month. The cloud offering is now structured into 'Free', 'Flex', and 'Premium' tiers. |
| 2026-02-01 | Supabase | limits_reduced | Project pause policy tightened — inactive projects now pause after 1 week |
| 2026-01-15 | Neon | pricing_restructured | Moved to fully usage-based pricing model post-Databricks acquisition — free tier storage now per-project, projects increased 10→100, branches capped at 10 per project, Neon Auth added |
Supabase pgvector or Neon pgvector — zero extra infrastructure, SQL queries, vectors live alongside your relational data. Go from zero to working semantic search in under an hour.
Pinecone (2 GB, ~1M vectors) or Zilliz Cloud (5 GB, ~5M vectors). Both offer generous free tiers with zero ops. Zilliz has more free storage; Pinecone has a larger ecosystem and simpler API.
Qdrant (self-hosted or cloud) or Pinecone (fully managed). Qdrant offers the best price-to-performance for self-hosted; Pinecone for zero-ops. Both support hybrid search, metadata filtering, and handle 10M+ vectors reliably.
Qdrant (Rust, high performance, Docker-friendly) or Chroma (Python-native, simplest setup). For enterprise scale: Milvus (Zilliz-backed, GPU-accelerated). All three are fully open-source with no feature gating.
Upstash Vector (REST API, edge-native, free tier) or Turbopuffer (S3-backed, pay-per-use, no minimum). Both work in serverless environments where TCP connections are unavailable.
Weaviate — built-in vectorization modules for text (OpenAI, Cohere, Hugging Face) and images (CLIP). Generative search combines retrieval with LLM generation in a single query. Multi-tenancy built-in for SaaS.
MongoDB Atlas Vector Search — add vector search to your existing MongoDB cluster with no extra infrastructure. Combine $vectorSearch with aggregation pipeline for hybrid queries. Free on M0 clusters.
Compare vector database pricing, search free tiers, and track pricing changes — all from your AI coding assistant.
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