Researched and Edited by Rajat Gupta
Last updated: · How we review
Editor's Summary · Vector Database Software
Start with where your data already lives, because that decides more than benchmark numbers do. If you run Postgres, pgvector keeps embeddings beside the rows they describe and removes an entire system from the stack — for most applications that is the right answer and the cheapest one. If you run MongoDB, Atlas Vector Search does the same thing. Elasticsearch, Redis, ClickHouse and SingleStore all now do vectors too.
Reach for a dedicated engine when scale or recall genuinely demands it. Pinecone is the managed default, Milvus and Qdrant are the open-source options at serious scale, and Weaviate and Chroma sit closer to the developer-experience end.
The cost model deserves more attention than it usually gets. Keeping a large index in memory is expensive, which is why turbopuffer and Upstash Vector serve from object storage and bill per request — a much better fit for multi-tenant products where most indexes are idle.
And check filtering carefully. Nearly every engine claims metadata filtering; the difference is whether it filters before or after the nearest-neighbour search, which changes both recall and latency.
Quick picks for Vector Database Software
- Best if you already run Postgres — pgvector
- Best managed dedicated engine — Pinecone
- Best open-source at scale — Milvus
Who gets the most from Vector Database Software
- 1Engineers building retrieval-augmented generation over a document corpus
- 2Platform teams choosing between extending Postgres and running a dedicated engine
- 3Teams running multi-tenant search where most indexes are idle most of the time
How to choose Vector Database Software
Check whether your current database already supports vectors before adding a system to operate — for most applications pgvector or Atlas Vector Search is sufficient and removes a synchronization problem. If you do need a dedicated engine, test with your real corpus and your real filters, since pre-filter versus post-filter behavior changes results in ways a generic benchmark will not show. Weigh the cost model against your access pattern: an index that is queried rarely does not justify provisioned memory.
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What is Supabase?
Supabase is an open-source and user-friendly way to quickly create an efficient backend. It allows businesses to build secure database applications with authentication, Postgres, real-time subscriptions, and instant APIs - no need to employ database specialists. Plus, storing, sharing, and ...
Read more about Supabase9.8
SpotScore
Elasticsearch
Empower your website with intelligent search capabilities.
Best for: SMB teams · Mid-market · Enterprise
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What is Elasticsearch?
Elasticsearch brings the power of full-text search to the website, turning it into an information hub that enables intelligent navigation of content. This takes site to another level, providing additional functionality as well as improving usability and conversion rates. Elasticsearch is a ...
Read more about ElasticsearchElasticsearch offers custom pricing plan
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See the companies researching Vector Database software right now — while they're still comparing options.
9.0
SpotScore
Weaviate
Open-source vector database for semantic search, RAG, and AI-native applications
Best for: SMB teams
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What is Weaviate?
Weaviate is an open-source vector database designed for AI-native applications. It stores, indexes, and searches high-dimensional vector embeddings alongside structured data, enabling semantic search, RAG (retrieval-augmented generation), and recommendation systems. Weaviate natively integrates ...
Read more about WeaviateStarts from Freefree, also offers free forever plan
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Learn More About Vector Database Software
Compare 24 vector databases on index type, filtering, scale, hosting model and cost. Covers dedicated engines, Postgres extensions and cloud services.
Vector database software stores and searches high-dimensional embeddings, powering the semantic search, retrieval-augmented generation (RAG), and long-term memory that AI applications depend on. Unlike traditional databases built around exact-match queries, these systems are optimized for approximate nearest-neighbor search at scale, letting an application find the most relevant piece of context out of millions of documents in milliseconds.
- Whether your existing database can do this, before adding a dedicated engine?
- Whether metadata filtering happens before or after the vector search, which changes recall and latency?
- Cost model: provisioned memory versus object storage and per-request billing?
What is vector database software?
Vector database software stores and searches high-dimensional embeddings, powering the semantic search, retrieval-augmented generation (RAG), and long-term memory that AI applications depend on. Unlike traditional databases built around exact-match queries, these systems are optimized for approximate nearest-neighbor search at scale, letting an application find the most relevant piece of context out of millions of documents in milliseconds.
They form a core layer of the modern AI stack alongside the LLM itself and the agent framework calling it.
Vector Database Software compared
Spotsaas lists 24 vector database products. Entries below are ordered by how many verified reviews each carries; where a vendor publishes pricing openly it is shown.
| # | Product | SpotScore | Rating | Reviews | Starting price |
|---|---|---|---|---|---|
| 1 | ElasticsearchTop rated | 9.8 | ★★★★★4.50 | 204 | — |
| 2 | 9 | ★★★★★4.50 | 195 | — | |
| 3 | — | ★★★★★4.30 | 1 | — | |
| 4 | — | — | — | — | |
| 5 | — | — | — | — | |
| 6 | — | — | — | — | |
| 7 | — | — | — | — | |
| 8 | — | — | — | — | |
| 9 | — | — | — | — | |
| 10 | — | — | — | — |
3 of the 24 listings carry verified Spotsaas reviews. Products without reviews are listed but not ranked.
What to check before you buy
Whether your existing database can do this, before adding a dedicated engine
Essential questions to ask the vendor:
- Whether your existing database can do this, before adding a dedicated engine?
How to overcome it: Check whether your current database already supports vectors before adding a system to operate — for most applications pgvector or Atlas Vector Search is sufficient and removes a synchronization problem.
Whether metadata filtering happens before or after the vector search, which changes recall and latency
Essential questions to ask the vendor:
- Whether metadata filtering happens before or after the vector search, which changes recall and latency?
How to overcome it: If you do need a dedicated engine, test with your real corpus and your real filters, since pre-filter versus post-filter behavior changes results in ways a generic benchmark will not show.
Cost model: provisioned memory versus object storage and per-request billing
Essential questions to ask the vendor:
- Cost model: provisioned memory versus object storage and per-request billing?
How to overcome it: Weigh the cost model against your access pattern: an index that is queried rarely does not justify provisioned memory.
Who uses Vector Database Software
Typical roles include Engineers building retrieval-augmented generation over a document corpus, Platform teams choosing between extending Postgres and running a dedicated engine, and Teams running multi-tenant search where most indexes are idle most of the time.
Frequently asked questions
Basics FAQs
What is vector database software?
Vector database software stores and searches high-dimensional embeddings, powering the semantic search, retrieval-augmented generation (RAG), and long-term memory that AI applications depend on. Unlike traditional databases built around exact-match queries, these systems are optimized for approximate nearest-neighbor search at scale, letting an application find the most relevant piece of context out of millions of documents in milliseconds.
Zilliz Cloud · Chroma · Milvus
What does ANN stand for?
ANN stands for approximate nearest neighbor. Vector database software stores and searches high-dimensional embeddings, powering the semantic search, retrieval-augmented generation (RAG), and long-term memory that AI applications depend on. Unlike traditional databases built around exact-match queries, these systems are optimized for approximate nearest-neighbor search at scale, letting an application find the most relevant piece of context out of millions of documents in milliseconds.
Zilliz Cloud · Chroma · Milvus
Pricing FAQs
Is there free vector database software?
Yes. 6 of the 24 products listed offer a genuinely free or freemium tier: Zilliz Cloud, Chroma, Pinecone, Supabase, Rockset, Weaviate. A free trial is not the same thing, and is noted separately on each listing.
Choosing FAQs
How do I choose vector database software?
Check whether your current database already supports vectors before adding a system to operate — for most applications pgvector or Atlas Vector Search is sufficient and removes a synchronization problem. If you do need a dedicated engine, test with your real corpus and your real filters, since pre-filter versus post-filter behavior changes results in ways a generic benchmark will not show. Weigh the cost model against your access pattern: an index that is queried rarely does not justify provisioned memory.
Buyers FAQs
Who uses vector database software?
Typically engineers building retrieval-augmented generation over a document corpus; platform teams choosing between extending Postgres and running a dedicated engine; teams running multi-tenant search where most indexes are idle most of the time.
Coverage FAQs
How many vector database products does Spotsaas track?
Spotsaas currently lists 24 products in this category, 3 of them with verified reviews. Listings are researched from vendor documentation and updated as the market changes.
Disclaimer: This research has been collated from a variety of authoritative sources. We welcome your feedback at [email protected].