pgvector vs Pinecone
pgvector vs Pinecone for RAG and semantic search: costs, scaling limits, filtering, hybrid search and operations, with Pinecone's 2026 plans and pgvector 0.8.
Quick Answer
Use pgvector if you already run Postgres and have up to tens of millions of vectors; you get transactions, joins and SQL filtering for no extra licence cost. Use Pinecone if vector search is core to your product at large scale and you want a managed service that handles sharding, freshness and read throughput for you.
Reviewed by TechLogHub Engineering Team. Last updated October 15, 2026. Reflects pgvector 0.8.x with iterative index scans and Pinecone's current Starter, Builder, Standard and Enterprise plans including Dedicated Read Nodes.
| Feature | pgvector | Pinecone |
|---|---|---|
| Deployment | Self-hosted or any managed Postgres | Managed SaaS (AWS, GCP, Azure) or BYOC |
| Licence | Open source (PostgreSQL License) | Proprietary |
| Pricing Model | Cost of your Postgres instance | Starter free, Builder $20/mo, Standard $50/mo min + usage |
| Free Tier | Free; also on free tiers of hosted Postgres | Starter: 2 GB storage, 2M write units, 1M read units/month |
| Index Types | HNSW, IVFFlat | Managed proprietary ANN |
| Max Indexed Dimensions | 2,000 (vector), 4,000 (halfvec) | High-dimensional dense + sparse vectors |
| Metadata Filtering | Full SQL WHERE + iterative scans | JSON metadata filters |
| Hybrid / Sparse Search | sparsevec + Postgres full-text (manual fusion) | Native sparse-dense |
| ACID Transactions with App Data | ||
| Multi-Tenancy | Row-level security, partitions, schemas | Namespaces |
| Built-in Embedding & Rerank |
pgvector
An open-source PostgreSQL extension that adds vector data types, distance operators and approximate nearest-neighbour indexes to Postgres.
Pros
- No new database: vectors live next to relational data with full SQL, joins and transactions
- Available on nearly every managed Postgres (RDS, Aurora, Cloud SQL, Azure, Supabase, Neon)
- HNSW and IVFFlat indexes plus halfvec, bit and sparsevec types
- Iterative index scans (since 0.8.0) fix the classic filtered-search recall problem
- Zero licence cost; you pay only for the Postgres instance
- Backups, replication, access control and tooling come from Postgres
Cons
- HNSW indexes are memory-hungry and slow to build on large datasets
- Scaling past one node's memory needs sharding (e.g. Citus) or careful partitioning
- Vector workloads compete with OLTP traffic for CPU and RAM
- Indexing limited to 2,000 dimensions for vector type (4,000 with halfvec)
- No built-in embedding generation or reranking
Best For
Teams already on Postgres building RAG or semantic search over up to tens of millions of vectors.
Pinecone
A fully managed vector database with on-demand (serverless) indexes, namespaces, sparse-dense hybrid search and integrated embedding and reranking models.
Pros
- No infrastructure to manage; storage and compute scale independently
- Handles billions of vectors with namespaces for per-tenant isolation
- Dedicated Read Nodes provide reserved query capacity for high-QPS workloads
- Integrated embedding and reranking models remove extra services from RAG pipelines
- Free Starter plan plus a $20/month flat Builder plan for small projects
- Bring-your-own-cloud option for regulated deployments
Cons
- Another datastore to keep in sync with your source-of-truth database
- Standard plan has a $50/month minimum; Enterprise $500/month minimum
- Usage-based read/write units make costs harder to predict at high query volume
- Proprietary and cloud-only (outside BYOC); no local production equivalent
- Metadata filtering is less expressive than SQL and cannot join relational data
Best For
Products where vector search is core, at large scale or high QPS, and the team prefers a managed service over tuning Postgres.
When 'just use Postgres' is the right call
For most RAG prototypes and many production apps, pgvector is the pragmatic default. Your documents, users and permissions already live in Postgres, so storing embeddings alongside them means one backup strategy, one access-control model and one query language. You can filter by tenant, date or status with ordinary SQL, join to relational tables, and update a row and its embedding in the same transaction, so search results are never stale relative to your data. The extension is mature. Version 0.8 added iterative index scans, which keep scanning the HNSW or IVFFlat index until enough rows pass your WHERE clause, solving the long-standing problem of filtered queries returning too few results. Half-precision halfvec and binary bit types cut memory use, and sparsevec supports sparse embeddings. Because pgvector ships on AWS RDS and Aurora, Google Cloud SQL, Azure Database for PostgreSQL, Supabase, Neon and most other hosts, adopting it rarely requires procurement or new infrastructure. If your vector count is in the low millions, start here and measure before adding another database.
Scaling past tens of millions of vectors
pgvector's limits are Postgres's limits. HNSW delivers good recall and latency, but the index performs best when it fits in memory, and build times on tens of millions of high-dimensional vectors can stretch to hours. Indexed columns are capped at 2,000 dimensions for the standard vector type and 4,000 for halfvec, which matters for some large embedding models unless you reduce dimensions. Heavy vector queries also compete with your transactional workload, so many teams end up running a dedicated Postgres instance or read replica for search anyway. Scaling horizontally means partitioning tables, sharding with an extension such as Citus, or using a managed Postgres that separates storage and compute. All are workable but add engineering effort. Pinecone is designed for this regime. Its on-demand indexes separate storage from compute, scale to billions of vectors, and keep new writes searchable quickly. Namespaces isolate tenants within an index, and Dedicated Read Nodes, added to Standard and Enterprise plans in late 2025, reserve query capacity so high-QPS workloads avoid shared queues and read rate limits. If you are heading past roughly 50 to 100 million vectors or need consistent latency under heavy load, Pinecone removes a lot of tuning work.
Cost comparison
pgvector itself is free; you pay for the Postgres instance. Adding embeddings to an existing database may cost nothing until memory pressure forces a larger instance. A dedicated mid-size managed Postgres for vector search typically runs tens to a few hundred dollars a month depending on RAM, and costs are predictable because they do not vary with query volume. Pinecone's Starter plan is free and includes 2 GB of storage, 2 million write units and 1 million read units per month across up to five indexes, along with free embedding and reranking quotas. The Builder plan costs a flat $20 per month. Standard has a $50 monthly minimum and Enterprise a $500 minimum, with usage above that billed for storage, reads and writes; Standard includes a three-week trial with $300 in credits. The crossover depends on query volume more than vector count. Low-traffic apps with millions of vectors are often cheaper on Pinecone's usage pricing than on an always-on Postgres instance sized for HNSW. High-QPS apps can flip the other way, which is where Dedicated Read Nodes or a self-managed pgvector cluster become worth pricing out. Use Pinecone's estimator with real traffic numbers.
Filtering, hybrid search and data freshness
Real search rarely uses vectors alone. With pgvector, filtering is plain SQL, so you can combine similarity with permissions, joins and complex predicates, and iterative scans keep recall high when filters are selective. Hybrid search is possible by combining Postgres full-text search or sparsevec with dense similarity, but you write the fusion logic, such as reciprocal rank fusion, yourself. Pinecone supports JSON metadata filters with common comparison and set operators, which cover most RAG needs but cannot join to other tables. You typically duplicate the fields you need to filter on into each vector's metadata. Native sparse-dense vectors and hosted sparse embedding and reranking models make hybrid retrieval easier to stand up than in pgvector. Freshness and consistency are where pgvector has a structural advantage. An insert, update or delete is visible to search as soon as the transaction commits, and there is no sync pipeline to fail. With Pinecone, your application or a change-data-capture job must keep vectors aligned with the source database, and deletions in particular need care. For permission-sensitive search where stale results are unacceptable, that consistency often decides the choice.
Verdict
Choose pgvector if you already run Postgres, your corpus is in the millions to low tens of millions of vectors, and you value SQL filtering, joins and transactional consistency with your app data. Choose Pinecone if vector search is a core product feature at hundreds of millions of vectors or high QPS, you need per-tenant namespaces at scale, or you simply do not want to tune HNSW and Postgres memory. Most teams should start with pgvector and move to Pinecone only when measured latency, recall or operational load says so.