pgvector vs TiDB
Side-by-side comparison of two AI agent tools
pgvectorfree
Open-source vector similarity search for Postgres
T
TiDBopen-source
TiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data sil
Metrics
| pgvector | TiDB | |
|---|---|---|
| Stars | 23.2k | 40.6k |
| Star velocity /mo | 437.9679144385027 | 3.4k |
| Commits (90d) | 126 | 388 |
| Releases (6m) | 0 | 4 |
| Overall score | 0.5755572807642764 | 0.8111691514841008 |
Pros
- +Native PostgreSQL integration preserves ACID compliance, transactions, and allows complex JOINs between vector and relational data
- +Supports multiple vector types (single/half-precision, binary, sparse) and distance metrics (L2, cosine, inner product, Hamming, Jaccard)
- +Wide ecosystem compatibility with any language that has a Postgres client and available through multiple installation methods
Cons
- -Requires PostgreSQL expertise and may have steeper learning curve compared to dedicated vector databases
- -Installation complexity varies by platform, especially on Windows systems
- -Performance may not match specialized vector databases for very large-scale vector workloads
Use Cases
- •RAG (Retrieval Augmented Generation) applications where embeddings need to be stored alongside document metadata and user data
- •E-commerce recommendation systems that combine vector similarity with product catalog data and user preferences
- •Semantic search applications where vector queries need to be combined with traditional filters and business logic
FAQ
- Which is more popular, pgvector or TiDB?
- TiDB has more GitHub stars (40,615 vs 23,202).
- Which is more actively developed, pgvector or TiDB?
- TiDB had more commits in the last 90 days (388 vs 126).
- Should I use pgvector or TiDB?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.