RadarTrek Intel — monthly score updates
We track 40+ tools so you don't have to. Score changes, new tools, and new guides — once a month, no spam.
Vector Databases comparison · 2026
Pinecone edges out pgvector in this Vector Databases comparison, scoring 89 against 77 across our seven scored dimensions. pgvector leads on Hybrid Search (88 vs 75), while Pinecone has the edge on Ecosystem (95 vs 80). The two are closest on Hybrid Search, where the gap is just 13 points. Both offer a free tier, making either a low-risk starting point. Use the radar chart and dimension table below to find which fits your specific priorities best.
pgvector
Vector search as a Postgres extension — no new database needed
77/100
Pinecone
The most widely adopted managed vector database
89/100
Radar comparison
pgvector
77
Pinecone
89
Developer UX
SDK quality, indexing API, and setup speed.
Query Performance
ANN search speed and recall accuracy at scale.
Scalability
Index size limits and horizontal scaling for billions of vectors.
Price / Value
Cost per million vectors and free tier generosity.
Hybrid Search
Combining vector similarity with keyword/metadata filtering.
Ecosystem
LangChain/LlamaIndex integrations and framework support.
Overall Score
Based on our independent scoring across 6 dimensions, Pinecone scores 89/100 overall versus pgvector's 77/100 — a 12-point margin. Pinecone leads on Price / Value in particular. That said, pgvector may still be the right choice if the dimensions where it scores higher match your specific priorities — the radar chart above shows the full profile side by side.
Both pgvector and Pinecone offer a free tier, so entry-level cost is not a differentiating factor. Compare the feature and usage limits of each free plan to see which gives you more headroom before a paid upgrade is needed.
pgvector scores higher on Price / Value — 96/100 versus 65/100 for Pinecone. If price / value is your primary decision criterion, pgvector is the stronger choice in this head-to-head.
Switching between vector databases tools is generally possible but involves migration effort: exporting your data or configuration from pgvector, re-importing or reconfiguring in Pinecone, and updating any API integrations or environment variables in your codebase. The effort scales with how deeply embedded the tool is in your stack. Test Pinecone on a non-production project first before migrating.
pgvector (77/100) is the better fit for teams who prioritise price / value — its strongest dimension — and who want a free entry point. Pinecone (89/100) is the better fit for teams who prioritise scalability and want a free entry point. If both dimensions matter equally, the overall score winner (Pinecone) is the safer default choice.
Want this built for your business?
We design and build digital products — web apps, AI tools, SaaS platforms.