# pgvector vs Chroma: which do AI models recommend for vector databases, October 2026

IT AI Recommendation Index, October 2026 Edition, Vector databases. Eight of fourteen models named pgvector first on the direct prompt; zero named Chroma. Page: https://it-ai-index.com/it-data/vector-databases/pgvector-vs-chroma/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| pgvector | 35% | #1 of 10 | 6% | 66 | 14 of 14 |
| Chroma | 6% | #4 of 10 | 31% | 36 | 14 of 14 |

## The direct prompt, model by model

- Gemini 3.5 Flash: pgvector first (first choices: pgvector) (alternatives: Pinecone, Qdrant, Weaviate)
- Perplexity Sonar: pgvector first (first choices: Qdrant, pgvector) (alternatives: Pinecone, Weaviate)
- Grok 4.1 Fast: pgvector first (first choices: pgvector) (alternatives: Pinecone, Qdrant, Weaviate)
- Mistral Small: pgvector first (first choices: pgvector) (alternatives: pgvectorscale)
- DeepSeek V4 Flash: pgvector first (first choices: Qdrant, pgvector) (alternatives: Pinecone, Weaviate)
- Llama 4 Maverick: pgvector first (first choices: pgvector)
- Kimi K2: pgvector first (first choices: pgvector) (alternatives: Pinecone, Qdrant, Weaviate)
- Muse Glimmer 30B: pgvector first (first choices: Pinecone, pgvector) (alternatives: Qdrant, Weaviate)
- GLM 4.7 FlashX: neither first, one named (first choices: Qdrant, Weaviate) (alternatives: pgvector)
- GPT-6 Luna: neither first, one named (first choices: Qdrant) (alternatives: Pinecone, pgvector)
- Claude Haiku 4.5: neither named (first choices: Pinecone, Weaviate)
- GPT-5.4 mini: neither named (first choices: Pinecone) (alternatives: Qdrant, Weaviate)
- Qwen 3.7 Flash: neither named (first choices: Pinecone) (alternatives: MongoDB Atlas Vector Search, Weaviate)
- MiniMax M2.5: neither named (first choices: Pinecone) (alternatives: Elasticsearch, Milvus, Weaviate)

## What the models said about pgvector

- "| Poor filtered search | pgvector, basic setups | ... | Reindexing locks | pgvector | Large HNSW rebuilds block reads/writes" (Grok 4.1 Fast, negative prompt, soft negative)
- "pgvector when you expect very high vector-search throughput or huge scale without testing" (GPT-6 Luna, negative prompt, soft negative)
- "Be cautious at massive scale ... you must be cautious of its ceilings" (Gemini 3.5 Flash, negative prompt, soft negative)
- "Unless you have 2-3 people who can own this infrastructure, lean managed or existing DB extension first.... Already run Postgres heavily, <10M vectors | pgvector" (Kimi K2, scale prompt, first choice)
- "The best vector database for a mid-market B2B company is pgvector, which is a PostgreSQL extension. It is the strongest default for mid-market enterprises." (Llama 4 Maverick, direct prompt, first choice)
- "evaluating pgvector might save you the overhead of learning a new technology, as it handles vector search well within a relational environment" (Qwen 3.7 Flash, scale prompt, first choice)

## What the models said about Chroma

- "Chroma has experienced significant production issues, including a critical memory leak... A severe cache invalidation bug in version 0.5 caused p99 latency to spike from 42ms to 542ms" (MiniMax M2.5, negative prompt, hard negative)
- "Only use Chroma behind a private network with auth enabled and patched, never internet exposed with defaults." (Muse Glimmer 30B, negative prompt, hard negative)
- "Avoid Chroma for production — it's a prototype tool, not a production database" (Kimi K2, negative prompt, hard negative)
- "Start with Chroma or Qdrant (self‑hosted) or pgvector if you already have PostgreSQL." (GLM 4.7 FlashX, budget prompt, first choice)
- "Best for small budgets: Chroma (free, self-hosted) or pgvector (free, if using PostgreSQL)." (Mistral Small, budget prompt, first choice)
- "Chroma: Offers a free plan and is considered one of the cheapest options." (Llama 4 Maverick, budget prompt, first choice)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.