Pinecone vs pgvector: which vector database is right for your AI stack?

When to choose Pinecone vs pgvector

That's the generic picture. Your vector count, team, and infrastructure will tip this one way or the other. ↓

Pinecone vs pgvector: performance at different scales

pgvector's HNSW index delivers strong performance for most real-world applications, but understanding where it excels and where it struggles is essential for choosing the right tool. At under 1M vectors on Supabase Pro or a comparable managed Postgres instance, HNSW achieves p50 latencies around 5ms and p99 under 20ms for typical 1536-dimension embeddings. Build time is the main trade-off: HNSW index construction on 1M vectors can take 10–30 minutes depending on m and ef_construction parameters, and requires significant memory, plan for roughly 1–2 GB of RAM per 1M vectors in the index.

As your dataset grows from 1M to 10M vectors, pgvector performance remains acceptable on properly sized instances (RDS r7g.large or Supabase dedicated). p50 latency at 1M vectors is approximately 5ms; at 10M vectors on the same hardware, expect p50 of 15–30ms with HNSW. The key question is query volume: a low-traffic application at 10M vectors is very different from 1,000 queries per second at 10M vectors. For high-concurrency scenarios above 5M vectors, pgvector's single-node architecture starts to show limits that Pinecone's distributed design handles more gracefully.

Pinecone is purpose-built for vector search at scale. Its internal indexing (based on proprietary ANN algorithms with horizontal sharding) maintains sub-10ms p50 query latency across hundreds of millions of vectors, a benchmark pgvector cannot match on standard hardware. At 100M vectors, Pinecone achieves <10ms p50 consistently. This performance advantage matters most for high-concurrency, large-dataset use cases: real-time recommendation engines, large-scale semantic search APIs, and applications where query latency directly affects user experience.

For most startups and under 5M vectors at moderate query volume, the latency difference is imperceptible to end users. The choice often comes down to operational simplicity and infrastructure fit, not raw performance. Answer the questions below to get a recommendation that accounts for your actual scale.

Pinecone vs pgvector: the hybrid search advantage

Pure semantic vector search fails in predictable ways: brand names, product identifiers, proper nouns, recent events not in the model's training data, and exact-match queries where the spelling matters more than the meaning. A user searching for "GPT-4o" in your knowledge base should get documents that contain "GPT-4o", not documents about language models in general. Semantic search alone misses this. The solution is hybrid search: combining dense embedding vectors (semantic similarity) with sparse BM25 or SPLADE vectors (keyword frequency/relevance), then merging the results with reciprocal rank fusion or a learned ranker.

Pinecone has native sparse-dense hybrid search built into its API. You store a sparse vector alongside the dense embedding, query with both simultaneously, and Pinecone handles the fusion internally with configurable alpha weighting between sparse and dense scores. This is production-ready, well-documented, and requires no additional infrastructure. The sparse vectors can be generated with BM25 (built-in to Pinecone's tooling) or SPLADE encoders for learned sparse representations.

With pgvector, hybrid search is possible but requires manual implementation. The standard approach: run a full-text search query (PostgreSQL's tsvector/tsquery system) and a vector similarity query separately, then combine results using reciprocal rank fusion in application code or a SQL CTE. This works but adds complexity, you maintain two ranking signals, tune fusion weights manually, and the approach is slower than native hybrid because it requires merging two separate result sets. There is no built-in BM25 in PostgreSQL; tsvector is a simpler TF-IDF approximation.

Production recommendation: if keyword + semantic hybrid search is a core product feature, not a nice-to-have, Pinecone's native support is significantly cleaner than pgvector's manual approximation. If hybrid search is secondary or occasional, the pgvector approach is workable and avoids the overhead of a separate vector database.

Pinecone vs pgvector: cost comparison

pgvector's cost model is simple: you pay for the PostgreSQL instance, and vector storage is just more rows in a table. If you're already running Supabase Pro ($25/month), adding a vector column and HNSW index to your existing database is essentially free, you're using compute you're already paying for. At larger scales, a Supabase Pro plan comfortably supports 1M+ vectors; Supabase Team or a dedicated instance handles 5–10M vectors. Neon's usage-based pricing (starting from $0 for the free tier, scaling with compute and storage) offers another cost-efficient path to pgvector, particularly for applications with sporadic query patterns where serverless pause-and-resume reduces idle costs.

Pinecone's pricing has two tiers: serverless and pod-based. The serverless tier charges approximately $0.33 per 1M queries and $0.04 per 1M vectors per month for storage, making it very economical for low-query-volume applications (a prototype querying 10,000 times per month costs under $1). Pod-based pricing is for high-throughput workloads: an s1.x1 pod at roughly $0.096/hour ($69/month) supports ~5M vectors with fast query performance. For large-scale deployments, pod costs compound: a p2.x2 for high-throughput search costs ~$0.38/hour.

The crossover calculation favors pgvector at low-to-moderate scale, and Pinecone's serverless tier at very low query volume (its per-query pricing is cheaper than paying for idle Postgres compute). For production workloads above 5M vectors with high query concurrency, dedicated Pinecone pods often become cost-competitive with the PostgreSQL instance sizing required to maintain good performance, especially when factoring in the engineering cost of tuning pgvector at scale.

Practical advice: most startups should start with pgvector on Supabase or Neon, it's nearly free if you're already on Postgres, and migration to a dedicated vector database is straightforward if you hit real performance limits. Don't pay for Pinecone's performance until you need it.

Common questions about Pinecone vs pgvector