Who AI recommends, and when it changes.
Data as of Apr 21, 2026 · Based on 24 AI answers · A buyer need in Developer Backend & Vector Search Services. · See how Parse measures this
dominates the local development prototyping need with 41.1% of AI recommendations between March and April 2026, driven by its zero-config setup and Python native workflows. is second at 23.2%, consistently surfaced for high-performance local RAG and on-prem deployment. The remaining share is split among , , , and for specific sub-conditions.
Where a different pick wins:
QdrantAI sends users seeking fast, low-complexity on-prem vector search to Qdrant over Chroma. · 3 sources
pgvectorWhen the buyer already uses PostgreSQL, pgvector is the consistent recommendation for staying within a familiar stack. · 1 source
MilvusMilvus is highlighted for data privacy and scalability, despite higher deployment complexity. · 1 source
LanceDB OSS is surfaced for in-process embedded vector search with multimodal support. · 1 source
Recommendation share
Qdrant leads at 38% of AI recommendations; Chroma follows at 33%.
By platform
Platforms disagree: Chroma leads on Google AI Overviews, Qdrant on ChatGPT.
Representative prompts behind this market ranking, and how AI tends to answer.
Buyer needs that sit next to this one in the same market.
Why here: Recommended for high-performance local hosting and on-prem deployments with low complexity. · 3 sources
Why here: Surfaced for flexibility and hybrid search, typically alongside Qdrant for on-prem AI applications. · 2 sources
“I want to run a vector database on-premise for data privacy. What is the best open-source vector database that is easy to deploy and scale?”
AI highlights
Chroma
