Redis Vector vs Turbopuffer: Vector Databases Comparison
Compare Redis Vector and Turbopuffer side by side. Both are tools in the Vector Databases category.
The short answer
Choose Redis Vector if multi-modal capabilities: vector search, caching, sessions, and messaging in one platform.
Choose Turbopuffer if up to 100x cost reduction compared to traditional vector databases.
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Quick Comparison
| Category | Vector Databases | Vector Databases |
| Website | redis.io | turbopuffer.com |
Pros & Cons: Redis Vector vs Turbopuffer
- +Multi-modal capabilities: vector search, caching, sessions, and messaging in one platform
- +Sub-millisecond latency with in-memory data storage
- +Multiple deployment options: fully managed cloud, self-managed, or open source
- +HNSW and Flat indexing support for flexible search requirements
- +Eliminates need for multiple tools, reducing architectural complexity
- −In-memory storage can be expensive at large scale
- −Not purpose-built solely for vector search like dedicated vector databases
- −May require additional configuration for optimal vector search performance
- −Pricing can become complex with usage-based model
- +Up to 100x cost reduction compared to traditional vector databases
- +Sub-10ms p50 latency with serverless auto-scaling
- +No namespace limits unlike competitors
- +Enterprise compliance (HIPAA, SOC 2, CMEK) on standard plan
- +Trusted by major companies: Notion, Cursor, Linear, PlayerZero
- −$64/month minimum spend makes it less suitable for small experiments
- −Relatively new platform with smaller community
- −Limited documentation compared to more established solutions
- −Object storage architecture may have latency trade-offs for some use cases
How to Choose a Vector Databases Tool
Key criteria to evaluate when comparing Vector Databases solutions:
About Redis Vector
Redis offers vector search capabilities through Redis Cloud (fully managed), Redis Software (self-managed), and Redis Open Source, with the Redis Vector Library (RedisVL) simplifying working with vectors in Redis. Unlike dedicated vector databases, Redis offers multi-modal capabilities—handling vector search, real-time caching, feature storage, and pub/sub messaging in a single system, eliminating the need for multiple tools and reducing complexity and cost. Redis supports HNSW (Hierarchical Navigable Small World) for fast approximate nearest neighbor (ANN) search and Flat indexing for exact search. Vector search lives alongside caching, sessions, and messaging in one platform, with data staying in memory with no network hops between systems, enabling core operations to run at sub-millisecond latency. Founded in 2011 and headquartered in San Francisco (relocated from Mountain View in 2024), Redis serves enterprises across multiple industries with proven performance at scale.
About Turbopuffer
Turbopuffer is a serverless vector and full-text search database trusted by leading companies including Notion, Cursor, Linear, and PlayerZero. Founded in 2023 by ex-Shopify engineers Simon Eskildsen and team, Turbopuffer reached $1 million in ARR within a year of launch and now operates profitably with only 22 employees while powering billions of vectors. The platform features serverless architecture with automatic scaling, sub-10ms p50 latency, support for billions of vectors, full-text search, hybrid search, and metadata filtering. TurboPuffer achieves up to 100x cost reduction compared to traditional vector databases by storing data on object storage like S3 at $0.02 per GB instead of in-memory at $2+ per GB. Turbopuffer has no enforced namespace limits and includes enterprise-grade compliance features like HIPAA BAA, SOC 2, and CMEK even on the non-enterprise plan. Query prices have been reduced by up to 94%, making it 10x-100x cheaper than alternatives with usage-based pricing.
What is Vector Databases?
Purpose-built databases for storing, indexing, and querying high-dimensional vector embeddings used in semantic search, RAG, and recommendation systems.
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