Vector databases changed a lot between 2025 and 2026: hybrid search became standard, pgvector caught up, and managed services beat self-hosting. Here is how Milvus, Qdrant, Weaviate, and pgvector compare, plus migration costs.

💡 What You Will Learn

Vector databases changed a lot between 2025 and 2026: hybrid search became standard, pgvector caught up, and managed services beat self-hosting. Here is how Milvus, Qdrant, Weaviate, and pgvector comp

📜 Table of Contents

• The four
• Five changes from 2025
• Choosing
• Before you migrate
• FAQ

Vector Database Comparison 2025 to 2026: Milvus vs Qdrant vs Weaviate vs pgvector

Four shifts define the last eighteen months: hybrid search went from nice-to-have to required, pgvector caught up for most mid-size workloads, managed services beat self-hosting, and agent memory layers latched onto vector stores. Stars from our tracker snapshot (late July 2026).

The four

Chroma remains fine for local development; Typesense is worth a look for typo-tolerant hybrid search.

Five changes from 2025

1. Hybrid search is table stakes - keyword plus vector in one query.
2. pgvector matured enough that most apps no longer need a second database.
3. Disk-based indexes matured, cutting the cost of tens of millions of vectors.
4. Managed cloud won: self-hosting an index, scaling, and backups is real work.
5. Agent memory projects like mem0 now treat vector stores as their substrate.

Choosing

• Already on Postgres under ~10M vectors: pgvector
• Shipping a product, want less ops: managed Qdrant or Zilliz
• Need hybrid search with typo tolerance: Weaviate or Typesense
• Benchmarking and evaluation: Milvus tooling

Before you migrate

Rebuilds are unavoidable - decide early whether you re-embed or only re-index, and make sure both sides use the same distance metric, since cosine and dot product silently reorder results. Retest metadata filters (date ranges and array contains differ most), remember that dedicated stores are usually eventually consistent, and expect snapshot-based backups rather than incremental ones.

FAQ

Is Chroma still relevant? Yes for prototypes; migrate when you hit scale, and export is straightforward.

When to leave Postgres? No hard threshold - judge by latency targets and concurrency. A single Postgres node usually handles millions of vectors at modest concurrency.

Is self-hosting ever right? With mature container and monitoring tooling, yes; otherwise index tuning and scaling consume ongoing effort.

Swapping embedding models? Re-embed everything - different models mean different vector spaces.

Capabilities and cloud pricing change - check official docs.

❓ FAQ

Did anything get worse?

Self-managing a vector DB is harder in 2026 - indexes and scaling grew complex; managed services are the sane default.

Is Chroma still relevant?

Yes, for local dev and prototypes; not for production scale.