Do coding agents recommend Pinecone?
Pinecone was chosen in 12% of 158 judged vector search sessions, ranking fifth. Measured with Claude Code, Codex and Cursor.
Pinecone was chosen in 12% of 158 judged vector search sessions, ranking fifth. It was also raised as a candidate in 91 further sessions without being chosen.
This page reports what happened when Claude Code, Codex and Cursor had to solve a problem in vector search inside a realistic codebase. Not what a chat assistant says about Pinecone. What an agent actually installed.
The numbers
| Category | Vector search |
| Sessions in the category | 158 |
| Sessions where Pinecone was chosen | 19 |
| Install share | 12% |
| Rank in category | 5 of 15 |
| Codebases it won in | 5 |
| Raised as a candidate, not chosen | 91 |
| Chosen when considered | 17% |
| Site | pinecone.io |
By agent
With 19 wins spread across three agents, the rates below are small numbers and a difference between them is not yet a finding. They are here because the direction is worth knowing, not because the gap is established.
| Agent | Sessions | Chose Pinecone | Share |
|---|---|---|---|
| Claude Code | 58 | 5 | 9% |
| Codex | 66 | 12 | 18% |
| Cursor | 34 | 2 | 6% |
By who was asking
Pinecone does much better with one kind of buyer than another. It won 22% of sessions asked as enterprise team and 4% of those asked as senior engineer.
| Who is asking | Sessions | Chose Pinecone | Share |
|---|---|---|---|
| Vibe coder | 45 | 9 | 20% |
| Junior developer | 60 | 3 | 5% |
| Senior engineer | 26 | 1 | 4% |
| Enterprise team | 27 | 6 | 22% |
What Pinecone was up against
The full ranking in vector search, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Neon | 54 | 34% |
| 2 | OpenAI Vector Stores | 24 | 15% |
| 3 | Qdrant | 23 | 15% |
| 4 | Typesense | 20 | 13% |
| 5 | Pinecone (this page) | 19 | 12% |
| 6 | Meilisearch | 7 | 4% |
| 7 | Elasticsearch | 2 | 1% |
| 8 | turbopuffer | 2 | 1% |
What this means
This is an integration problem, not a presence problem. Agents raised Pinecone in 91 sessions and chose it in 19, so it reaches the shortlist and then loses. Something at the last step is costing the session, and in our data that is usually a quickstart that does not run when pasted, documentation describing an interface that changed, or a package name that does not match the product name.
That is the cheaper of the two problems to have. The reason is written down in each losing transcript.
Where these numbers come from
The 158 sessions in vector search are part of a published set of 5,915, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every vector search run can be replayed on the board.
If you work on Pinecone: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
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Common questions
Do coding agents recommend Pinecone?
Yes. Pinecone was chosen in 19 of the 158 judged sessions in vector search, a 12% install share, ranking fifth in its category.
Does Claude Code recommend Pinecone?
In 5 of the 58 sessions in vector search run with Claude Code, which is 9%.
Do different coding agents treat Pinecone differently?
Yes, and by a wide margin. Codex chose it in 18% of its runs and Cursor in 6%.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 56 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.
How often is Pinecone considered but not chosen?
It was raised as a candidate in 91 sessions without being chosen, and chosen in 19. That is a 17% conversion from considered to chosen.
Where this comes from
Armature ran 5,915 judged sessions with Claude Code, Codex and Cursor inside 56 realistic codebases, and published every run. The numbers on this page come from that work.