Constraint resolver · Vector databases
Best vector database
Recommendation
pick redis first (Redis is an in-memory database designed as a versatile cache and data structure store with advanced features such as JSON operations and vector searches, making it suitable for real-time applications.). meilisearch is the next fit when meilisearch is a rust-based, lightning-fast hybrid search engine that integrates easily into web and mobile applications. it supports both full-text and vector searches.. Rankings cite decision_facts and live GitHub stats.
Pick a vector store from your deployment, Postgres, hybrid-search, and scale constraints - ranked with sourced decision_facts, not star leaderboards alone.
GraphCanon updated Oct 4, 2026
21views this month
Markdown twin · Vector databases category · Resolver API
redis/redis
Why this pick
- Redis is an in-memory database designed as a versatile cache and data structure store with advanced features such as JSON operations and vector searches, making it suitable for real-time applications.
- 76k GitHub stars (live sync).
Sources: decision_facts (2026-07-11) · adoption blurb · when_to_use · GitHub stats
When NOT to use redis
- Your project has limited memory resources since Redis relies on in-memory storage, which could lead to high costs or operational challenges with large datasets.
- You prioritize persistence over speed; while Redis offers persistence options, its primary design is for real-time access and not robust disk-based backup solutions like traditional SQL databases.
- Your application workload does not benefit from the fast read/write capabilities and rich data structure support offered by Redis, possibly implying that a less specialized database would suffice.
meilisearch/meilisearch
Why this pick
- Meilisearch is a Rust-based, lightning-fast hybrid search engine that integrates easily into web and mobile applications. It supports both full-text and vector searches.
- 59k GitHub stars (live sync).
Sources: decision_facts (2026-07-11) · adoption blurb · when_to_use · GitHub stats
When NOT to use meilisearch
- - When you specifically need language support for a large number of languages beyond what Meilisearch currently offers, as some specialized multilingual search engines might handle more languages nimb
- - If your application does not require real-time search-as-you-type or typo tolerance features which can add overhead and may slow down performance in less demanding scenarios.
MemPalace/mempalace
Why this pick
- mempalace is an open-source AI memory system that leverages ChromaDB as its default vector-store backend, supporting Python 3.9+ and offering both local and server-based embedding options.
- 59k GitHub stars (live sync).
Sources: decision_facts (2026-09-18) · adoption blurb · when_to_use · GitHub stats
When NOT to use mempalace
- If you are working in an environment where Python 3.9+ is not available, as mempalace requires this version or higher.
- When you need a system that does not require at least 300 MB of disk space for the embedding model, as mempalace requires this minimum space for its recommended multilingual model.
milvus-io/milvus
Why this pick
- Milvus is a high-performance, cloud-native vector database for scalable vector ANN search, supporting various indexing techniques and built for cloud-native environments.
- Hosting: self hosted - Milvus is designed to be self-hosted and can be deployed in a cloud-native environment.
- Pricing: freemium - Milvus is free to use under the Apache-2.0 license, but additional services or support may come at a cost.
- Requirements: Min 4 GB RAM; Requires Docker; Third-party dependencies must be installed before using Milvus.; Milvus is built with Go and requires a Go environment to run.
- License detail: Milvus is licensed under the Apache-2.0 license.
Sources: decision_facts (2026-09-18) · hosting summary · adoption blurb · when_to_use · requirements · GitHub stats
When NOT to use milvus
- If your project does not require vector similarity search or approximate nearest neighbor search capabilities.
- When the project is not cloud-native and does not benefit from cloud-native optimizations.
- If the project's requirements are better met by a non-vector database or a different type of database.
- When the specific indexing techniques and performance characteristics of Milvus do not align with your project's needs.
pingcap/tidb
Why this pick
- TiDB is a scalable, cloud-native database that supports both transactional and analytical processing with ACID guarantees.
- 40k GitHub stars (live sync).
Sources: decision_facts (2026-07-11) · adoption blurb · when_to_use · GitHub stats
When NOT to use tidb
- If your primary focus is on a traditional relational database with limited transactional and minimal analytics needs, TiDB's complexity and overhead may not be justified.
- ，TiDB。
- If you require strong geographic data distribution requirements that exceed the capabilities of a single database system, consider whether TiDB’s distributed setup meets your specific geographical and
qdrant/qdrant
Why this pick
- Qdrant is a high-performance vector database and search engine built for AI applications, offering distributed deployment with sharding and replication.
- Pricing: freemium - Qdrant offers a free open-source version under the Apache License, with additional paid services available in the cloud.
- Requirements: Min 2 GB RAM; Qdrant supports distributed deployment, which may require additional resources depending on the scale of deployment.
- License detail: Qdrant is available under the Apache License, Version 2.0, which is a permissive free software license.
- 35k GitHub stars (live sync).
Sources: decision_facts (2026-09-18) · adoption blurb · when_to_use · requirements · GitHub stats
When NOT to use qdrant
- If your project requires a database that is not built in Rust, and you have constraints against using Rust-based solutions.
- When you need a vector database that does not support distributed deployment, as Qdrant's strength lies in its scalability features.
Explore
Common questions
- What is the best best vector database?
- For your constraints, GraphCanon ranks redis first. Redis is an in-memory database designed as a versatile cache and data structure store with advanced features such as JSON operations and vector searches, making it suitable for real-time applications. Rankings use decision_facts and live GitHub stats - not paid placement.
- How does GraphCanon rank these picks?
- Hard constraints (self-host, Docker) use the constraint resolver API (
graphcanon_resolve_toolsover capability_facts). Soft constraints (Postgres, hybrid search, scale) boost tools whose decision_facts and adoption blurbs match. Stars break ties; they are not the primary signal. - How is this different from a star-sorted category page?
- Category pages sort by GitHub stars. This resolver ranks by your constraints first, then cites decision_facts for why each tool fits. Use /categories/vector-databases for the full list; use this page when you know your deployment and stack constraints.
- When should I not use the top vector databases pick?
- Each recommendation includes a "When NOT to use" block sourced from decision_facts.when_not_to_use, category guidance, and maintenance signals. Read that before adopting redis.
- Is there a machine-readable version of this page?
- Yes. Append
.mdto this URL or fetch `/best/vector-database.md`. The JSON constraint resolver is at `/api/graphcanon/resolve?category=vector-databases`.