# Vector search: which search services coding agents choose > Neon and OpenAI benefit from existing integrations. Source: https://armature.tech/leaderboards/vector-search (Armature agent leaderboards). 158 runs, 6 apps, 3 agents, 4 personas, updated 2026-09-08. Interactive board with every run: https://armature.tech/leaderboards#app/vector-search ## Key learnings ### The existing database blocks a new service Neon wins all 30 donation app runs and 18 of 26 repair app runs. Agents cite PostgreSQL reuse and less synchronization work when rejecting a separate vector service. ### OpenAI wins through the installed SDK The report app selects OpenAI Vector Stores in 24 of 30 runs. Agents reuse its OpenAI SDK and avoid a separate embedding and database pipeline. ### Qdrant wins the tenant document app Qdrant wins 17 of 27 document app runs. Agents favor its metadata filters and Python client while keeping document permissions in the application database. ### Typesense leads the hybrid search requests Typesense wins 9 of 19 reviewed runs that request vector plus full-text search. Pinecone wins 19 runs overall, but none of these hybrid search runs. ## The ranking | # | Product | Wins | Share | |---|---|---:|---:| | 1 | Neon (neon.tech) | 54 | 34% | | 2 | OpenAI Vector Stores (openai.com) | 24 | 15% | | 3 | Qdrant (qdrant.tech) | 23 | 15% | | 4 | Typesense (typesense.org) | 20 | 13% | | 5 | Pinecone (pinecone.io) | 19 | 12% | | 6 | Meilisearch (meilisearch.com) | 7 | 4% | | 7 | Elasticsearch (elastic.co) | 2 | 1% | | 8 | turbopuffer (turbopuffer.com) | 2 | 1% | | 9 | Supabase Vector (supabase.com) | 1 | 1% | | 10 | Chroma (trychroma.com) | 1 | 1% | | 11 | Amazon Bedrock Knowledge Bases (aws.amazon.com) | 1 | 1% | | 12 | Render Postgres (render.com) | 1 | 1% | | 13 | Weaviate (weaviate.io) | 1 | 1% | | 14 | Amazon RDS for PostgreSQL (aws.amazon.com) | 1 | 1% | | 15 | Amazon Aurora PostgreSQL (aws.amazon.com) | 1 | 1% | ## By agent - Codex: 66 runs, first Neon (18), then Pinecone (12) - Claude Code: 58 runs, first Neon (26), then Qdrant (7) - Cursor (Grok 4.6): 34 runs, first Neon (10), then Qdrant (7) ## By persona - Junior developer: 60 runs, first OpenAI Vector Stores (24), then Typesense (14) - Vibe coder: 45 runs, first Neon (30), then Pinecone (9) - Enterprise team: 27 runs, first Qdrant (17), then Pinecone (6) - Senior engineer: 26 runs, first Neon (18), then Typesense (5) A case is one codebase with one agent, asked several times in different words and as different people. 12 of 18 cases did not hold to a single choice. ## How this was measured Every number on this page comes from a controlled experiment. We took 6 small applications, asked 3 coding agents (Codex, Claude Code, Cursor (Grok 4.6)) to add vector search to each of them, in several wordings and as a junior developer and vibe coder and enterprise team and senior engineer, and let the agent choose the product. Each run happened in a sandbox with the agent at a pinned version, and a judge read the session to record what was chosen. That is 158 runs. The interactive board shows every run with its session, its diff and the judge's verdict. A simulated user stood in for the owner of the codebase: it read the agent's plan and had to approve it before any code was written; it sent the agent back at least once in 70 runs. Methodology and publications: https://armature.tech/publications If you sell in this sector, what these numbers mean for a vendor: https://armature.tech/library/vector-search-coding-agents-playbook (Markdown: https://armature.tech/library/vector-search-coding-agents-playbook.md) ## Other sectors - [Agent sandboxes](https://armature.tech/leaderboards/sandboxes) (https://armature.tech/leaderboards/sandboxes.md) - [Observability](https://armature.tech/leaderboards/observability) (https://armature.tech/leaderboards/observability.md) - [Payments](https://armature.tech/leaderboards/payments) (https://armature.tech/leaderboards/payments.md) - [Deploy](https://armature.tech/leaderboards/deploy) (https://armature.tech/leaderboards/deploy.md) - [Auth](https://armature.tech/leaderboards/auth) (https://armature.tech/leaderboards/auth.md) - [Email providers](https://armature.tech/leaderboards/mail) (https://armature.tech/leaderboards/mail.md) - [Product analytics](https://armature.tech/leaderboards/product-analytics) (https://armature.tech/leaderboards/product-analytics.md) - [Databases](https://armature.tech/leaderboards/databases) (https://armature.tech/leaderboards/databases.md) - [File storage](https://armature.tech/leaderboards/storage) (https://armature.tech/leaderboards/storage.md) - [LLM evals & observability](https://armature.tech/leaderboards/evals) (https://armature.tech/leaderboards/evals.md) - [Voice Agents](https://armature.tech/leaderboards/voice-agents) (https://armature.tech/leaderboards/voice-agents.md) - [Serverless functions](https://armature.tech/leaderboards/serverless) (https://armature.tech/leaderboards/serverless.md) - [Cloud](https://armature.tech/leaderboards/cloud) (https://armature.tech/leaderboards/cloud.md) - [AI gateway](https://armature.tech/leaderboards/ai-gateway) (https://armature.tech/leaderboards/ai-gateway.md) - [Bot protection](https://armature.tech/leaderboards/bot-protection) (https://armature.tech/leaderboards/bot-protection.md) - [Search](https://armature.tech/leaderboards/search) (https://armature.tech/leaderboards/search.md) - [Agent frameworks](https://armature.tech/leaderboards/agent-frameworks) (https://armature.tech/leaderboards/agent-frameworks.md) - [Performance in CI](https://armature.tech/leaderboards/perf-ci) (https://armature.tech/leaderboards/perf-ci.md) - [Code review](https://armature.tech/leaderboards/code-review) (https://armature.tech/leaderboards/code-review.md) - [In-app chat & calls](https://armature.tech/leaderboards/in-app-communication) (https://armature.tech/leaderboards/in-app-communication.md)