Vector Search 2026: Azure AI Search vs pgvector vs Pinecone

The Decision Framework

Three questions decide the choice, in order:

1. Do you already run Azure or Postgres for the rest of the application?
2. What is the corpus size, and what is the expected query rate?
3. Do you need hybrid search (vector + keyword), filtered search, or pure semantic search?

These three questions handle the majority of decisions. Specialised workloads (multi-tenant SaaS with hard isolation, multi-modal embeddings, very low latency at very high QPS) bring additional constraints, but they are exceptions, not the default.

Azure AI Search: When It Wins

Azure AI Search (formerly Azure Cognitive Search) is the default for teams already committed to Azure. The platform fit matters more than per-feature comparison: Entra-backed access, Sweden Central or other EU regions for data residency, OpenAI integration for the indexer, and a managed search service that does not require you to operate a separate database.

Specific strengths that make it the right choice:

• Hybrid search out of the box. BM25 + vector + semantic ranker as a single query. The ranker (Microsoft's cross-encoder) materially improves relevance on long-tail queries with no extra code.
• Indexer-managed ingestion. Connectors to Blob Storage, Cosmos DB, SQL, SharePoint. The indexer handles chunking and embedding generation through skillsets. Less glue code.
• Security and tenant isolation. Per-document permissions with security trimming. The capability that makes Azure AI Search a fit for multi-tenant SaaS without separate indexes per tenant.
• Predictable EU residency. Sweden Central, West Europe, North Europe as deployable regions, with all data confined to the chosen region.

The trade is cost. Azure AI Search pricing per replica per partition is meaningful, and small corpora end up overpaying for the management layer. For a 10,000-document RAG corpus on a small B2B app, pgvector is usually cheaper. For a 10-million-document enterprise corpus with hybrid search and security trimming, Azure AI Search is almost always the right answer.

pgvector with Postgres: When It Wins

The pgvector extension reached production maturity in 2024 and gained HNSW indexing that closes most of the relevance-vs-latency gap with specialised stores. Azure Database for PostgreSQL, AWS RDS, and Supabase all ship it. The 2026 case for pgvector is the same as the 2024 case, sharpened by HNSW.

When pgvector wins:

• The application already runs on Postgres. One database, one backup, one access pattern. No data movement.
• The corpus is small to medium (under ~5 million vectors). HNSW on managed Postgres handles this range comfortably with single-digit-millisecond p99 latency.
• The queries are filter-heavy. WHERE tenant_id = $1 AND created_at > $2 AND embedding <=> $3 LIMIT 10 is a single SQL query; the planner can use both the regular and the vector index efficiently.
• You need transactional consistency. Insert a document and its chunks atomically; query immediately. No eventual-consistency window between OLTP and vector store.

-- pgvector with HNSW and a filter
CREATE INDEX ON documents USING hnsw (embedding vector_cosine_ops)
  WITH (m = 16, ef_construction = 64);

SELECT id, content, embedding <=> $1 AS distance
FROM documents
WHERE tenant_id = $2 AND deleted_at IS NULL
ORDER BY embedding <=> $1
LIMIT 10;

Where pgvector struggles: very large corpora where HNSW build time and memory footprint become operationally painful, and workloads that need first-class hybrid search. Postgres full-text search exists, but the combined ranking with vector results is something the application has to wire, not something the database produces.

Dedicated Managed Vector Platforms: When They Win

Pinecone, Weaviate Cloud, Qdrant Cloud, and the others compete in the high-end of the market. The case for them in 2026:

• Very large corpora. Tens of millions to billions of vectors with consistent single-digit-millisecond p99 at high QPS.
• Specialised retrieval features. Sparse + dense hybrid, learned sparse models, native multi-vector representations, ColBERT-style late interaction.
• Operational simplicity at scale. If the alternative is operating a self-hosted vector store at meaningful scale, the managed offering is cheaper and more reliable.

The constraints that often push teams away:

• EU residency commitments. Some platforms still have limited EU presence or unclear sub-processor chains. Check the legal disclosures, not the marketing page.
• Procurement. Another vendor relationship for a team already inside the Microsoft, AWS, or Google enterprise agreement.
• Egress and integration cost. Pulling documents from Azure Blob to Pinecone, embedding them, indexing, then querying back to the application: data movement that does not exist with the in-platform options.

Hybrid Search Reality

Pure vector search underperforms hybrid search on most enterprise corpora. Acronyms, product codes, specific names: BM25 finds them; pure semantic search drifts. The teams that ship great RAG run hybrid by default.

Hybrid implementation differs by platform:

• Azure AI Search. Single query with BM25 + vector + semantic ranker. Tune weights at query time.
• pgvector. Two queries, one against tsvector full-text, one against vector index, combined in SQL or in application code. The combination is not built-in but is straightforward.
• Dedicated platforms. Most offer native hybrid with their own ranking. Pinecone, Weaviate, and Qdrant all do.

Cost Comparison Structure

Specific pricing changes; the structure does not. The cost components to compare honestly:

• Storage. Per-GB cost of storing vectors plus payload.
• Compute. Replicas, partitions, pod sizes, dedicated capacity. The variable cost driver.
• Indexing operations. Some platforms charge per indexed document; some include it.
• Query cost. Per-query pricing or amortised in compute pricing.
• Egress and integration. The cost of moving documents and embeddings into and out of the vector store.

A common pattern: the marketing-page comparison shows the dedicated platform as cheap. The reality at scale, including egress and the embedding generation, often closes the gap with Azure AI Search or pgvector. Run the comparison with your real corpus size and query rate, not with the synthetic benchmarks.

What to Test Before Committing

Three benchmarks decide the choice for most workloads:

1. Relevance on your queries. A small labelled set of 50 to 100 real user queries with their expected results. Run hybrid and pure-vector on each candidate and grade with the team.
2. p95 and p99 latency at expected load. Synthetic latency tests at half and twice expected QPS, including the filter and security predicates that production will carry.
3. Operational cost on a representative dataset. Real corpus size, real document size, real query rate. Two weeks of running each option in parallel produces real numbers.

Benchmarks against public datasets (MS MARCO, BEIR) inform the decision but do not replace running on the team's own corpus. The corpus is the asset; the platform is the operational choice.

A Default Recommendation

For a B2B team on Azure with a corpus under five million documents and a need for hybrid search with security trimming: Azure AI Search. The platform fit, the hybrid relevance, and the Entra-backed security are worth the cost premium over pgvector for any non-trivial enterprise workload.

For a team that already runs Postgres, has a small to medium corpus, and does not need a separate search-ranking engine: pgvector. One database, one backup story, one query language.

For very large corpora, very high QPS, or specific retrieval features the other two cannot deliver: a dedicated platform, chosen on EU residency, procurement fit, and the specific retrieval feature that justifies the move.

The choice is less interesting in 2026 than it was in 2023. The market has settled. The right answer is usually the platform you are already on, run well.