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Vector databases · October 2026 Edition

Chroma vs MongoDB Atlas Vector Search

Zero of fourteen models named Chroma first on the direct prompt; zero named MongoDB Atlas Vector Search. Chroma was named by fourteen of the fourteen models and MongoDB Atlas Vector Search by eight and Chroma carries 36 labels and MongoDB Atlas Vector Search 12, so the shares are not directly comparable.

Chroma

criticized challenger

Named in one category this edition.

MongoDB Atlas Vector Search

accepted challenger

Named in one category this edition.

First-choice share6%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate31%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 10; printed, not drawn.
Labels3612A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Chroma reading right to left. Rank and label count are printed, not drawn.Weaviate was named alongside these two in eleven of the fourteen direct answers. pgvector vs Chroma · pgvector vs MongoDB Atlas Vector Search · Pinecone vs Chroma

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; every quote names the model and the prompt it came from. Both figures come from the vector databases page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
ChromaFirst choices, of fourteen modelsMongoDB Atlas Vector Search
Direct00
Paraphrase011 against Chroma
Comparative003 against Chroma
Budget-constrained40
Scale-constrained00
Negative007 against Chroma · 1 against MongoDB Atlas Vector Search
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, Chroma and MongoDB Atlas Vector Search were named in the same answer nine times, of the 101 answers naming Chroma and the 20 naming MongoDB Atlas Vector Search. In those answers MongoDB Atlas Vector Search took the first choice zero times and Chroma one.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Chroma and MongoDB Atlas Vector Search stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30B
Chroma MongoDB Atlas Vector Search first choice named as an alternative argued againstblank: not namedEach cell is one answer, Chroma on the left and MongoDB Atlas Vector Search on the right.

The direct prompt

The plain question, one answer per model, grouped by where Chroma and MongoDB Atlas Vector Search stood in it.

Neither was the first choice, one was named

1 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Qwen 3.7 FlashPinecone alternatives: MongoDB Atlas Vector Search, Weaviate

Neither was named

13 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Pinecone, Weaviate
GPT-5.4 miniPinecone alternatives: Qdrant, Weaviate
Gemini 3.5 Flashpgvector alternatives: Pinecone, Qdrant, Weaviate
Perplexity SonarQdrant, pgvector alternatives: Pinecone, Weaviate
Grok 4.1 Fastpgvector alternatives: Pinecone, Qdrant, Weaviate
Mistral Smallpgvector alternatives: pgvectorscale
DeepSeek V4 FlashQdrant, pgvector alternatives: Pinecone, Weaviate
Llama 4 Maverickpgvector
Kimi K2pgvector alternatives: Pinecone, Qdrant, Weaviate
GLM 4.7 FlashXQdrant, Weaviate alternatives: pgvector
MiniMax M2.5Pinecone alternatives: Elasticsearch, Milvus, Weaviate
GPT-6 LunaQdrant alternatives: Pinecone, pgvector
Muse Glimmer 30BPinecone, pgvector alternatives: Qdrant, Weaviate

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Chroma leads by five points.
Chroma5%#5 of 7
MongoDB Atlas Vector Search0%#– of 7
The full small business standing →
Mid-marketThe figures above
Chroma leads by five points.
Chroma6%#4 of 10
MongoDB Atlas Vector Search2%#7 of 10
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Chroma0%#10 of 10
MongoDB Atlas Vector Search0%#– of 10
The full enterprise standing →

What the models said about Chroma

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.

“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

What the models said about MongoDB Atlas Vector Search

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.

“MongoDB Atlas gets pricing gripes in broader DB contexts” Grok 4.1 Fast · negative prompt · soft negative
“the sweet spot today is MongoDB Atlas Vector Search (if your data already lives in MongoDB)” GLM 4.7 FlashX · paraphrase prompt · first choice
“Stick with MongoDB Atlas if your existing data pipeline is already built around MongoDB documents.” Qwen 3.7 Flash · direct prompt · alternative
“Use pgvector or Atlas Vector Search” Kimi K2 · scale prompt · alternative
Also compared
pgvector vs Chromapgvector vs MongoDB Atlas Vector SearchPinecone vs ChromaPinecone vs MongoDB Atlas Vector SearchQdrant vs ChromaQdrant vs MongoDB Atlas Vector SearchChroma vs WeaviateChroma vs MilvusWeaviate vs MongoDB Atlas Vector SearchMilvus vs MongoDB Atlas Vector SearchAll twenty-one head-to-head pages and the full vector databases standing

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.