Redis vs. OpenSearch: What’s faster for GenAI & vector search?

Why Redis stands out for GenAI & vector search

GenAI is more powerful than ever, but most apps still feel slow. Users expect instant responses, but latency and inefficiencies get in the way. Redis makes GenAI faster. With up to 52x higher QPS and 100x lower query latency than OpenSearch, Redis delivers the real-time speed that AI apps need.

But speed alone isn’t enough. AI apps also require some combination of caching, semantic routing, short-term memory, session storage, and distributed state management. They need a platform that is easy to scale and operate, without the complexity of manual sharding, constant index tuning, reindexing overhead, or backup and scaling challenges. OpenSearch was not built for this. Redis was.

That is why companies building retrieval-augmented generation (RAG), AI-powered recommendations, and conversational memory choose Redis. Redis doesn’t just store and search. It provides the essential building blocks for GenAI, delivering speed, scalability, and automated management, combining speed, scalability, and automation to reduce operational overhead and deliver real-time performance at scale.

Performance: Redis delivers 50x+ better throughput and 100x lower latency

Benchmarks comparing Redis and OpenSearch highlight a massive performance gap. Redis isn’t just a little faster. It’s more than an order of magnitude faster:

• Single-client benchmarks show Redis performing up to 18x faster than OpenSearch in vector search queries.
• Multi-client benchmarks show Redis outperforming OpenSearch by up to 52x in queries per second (QPS).
• Query latency is up to 106x lower with Redis, enabling real-time AI responses where OpenSearch struggles with delays.

For high-scale AI apps, this means faster responses, reduced infrastructure costs, and a better user experience.

Cloud flexibility: Avoid lock-in with Redis Cloud

Another key advantage of Redis is cloud choice. While Amazon OpenSearch locks users into AWS, Redis provides multi-cloud, hybrid, and on-prem deployment options. This allows enterprises to:

• Run Redis across AWS, GCP, and Azure.
• Deploy Redis on-premises or in hybrid environments for more control over AI infrastructure.
• Ensure data sovereignty and compliance by keeping sensitive data within specific geographic regions or private environments.
• Leverage Redis’ built-in high availability and Active-Active capabilities, which OpenSearch lacks.

For organizations operating in regulated industries or regions with strict data residency requirements, Redis offers the flexibility to meet compliance needs without sacrificing performance. Beyond compliance, multi-cloud and hybrid deployment options help enterprises avoid vendor lock-in, optimize for cost and performance across cloud providers, and support diverse deployment needs as infrastructure strategies evolve.

Ease of management: Simplicity at scale

Managing AI infrastructure can be complex, but Redis Cloud eliminates much of the operational overhead that comes with scaling and maintaining Amazon OpenSearch.

• Automated scaling: Redis Cloud automatically scales workloads by adding or removing capacity as needed, without requiring manual intervention. OpenSearch users must plan and adjust cluster size manually.
• Automatic shard balancing: Unlike OpenSearch, which requires manual shard allocation and rebalancing, Redis dynamically moves key slots across shards to optimize performance.
  • No shard planning required: Redis automatically distributes data, eliminating the need for predefined shard configurations.
  • Seamless rebalancing on scaling events: When adding or removing nodes, Redis shifts key slots between shards without downtime or the need for reindexing.
  • Consistent performance: Redis avoids the bottlenecks and performance degradation that can occur when OpenSearch shards become unbalanced.
• Lower operational overhead: Redis Cloud reduces the need for manual index management and frequent reindexing, making it far easier to operate than OpenSearch.
• Built-in multi-tenancy: Redis Cloud provides efficient multi-tenant capabilities without requiring complex index storage and resource allocation management.
• Automated backup & disaster recovery: Automated backups and high availability are built-into Redis Cloud, whereas OpenSearch users must configure and maintain snapshot-based recovery manually.
• Native TTL support: Redis allows automatic expiration of data, simplifying real-time session management and caching. OpenSearch lacks built-in TTLs, requiring manual deletion scripts or index lifecycle policies to remove old data.

For teams looking to reduce complexity and focus on AI development rather than database management, Redis Cloud offers a streamlined, worry-free approach that scales effortlessly. 

Flexibility: Support for vector search plus full-text search

Many AI-driven apps, such as retrieval-augmented generation (RAG) and personalized recommendations, require a combination of vector search with exact match filtering. Some vector databases rely on external full-text search (FTS) solutions or require text to be vectorized before searching, adding complexity.

Both Redis and OpenSearch include built-in FTS capabilities for:

• Lexical searches with BM25/TF-IDF scoring.
• Metadata filtering on tags, text, numerics, and geo-coordinates.
• Exact match filtering with vector search that delivers fast, relevant results.

Hybrid search capabilities differ between the two. OpenSearch supports blended ranking hybrid search, where lexical and vector search results are combined and ranked together. Redis supports filtered hybrid search, where vector search results are refined using metadata filtering, exact match lookups, or full-text search constraints.

While Redis does not offer blended ranking today (it’s coming soon), it provides real-time indexing, low-latency query performance, and efficient exact match filtering, making it a strong choice for GenAI apps that demand speed and scalability.

Real-time updates: The advantage of instant adaptation

For AI apps that demand immediate updates to vector data and metadata, Redis excels. Unlike OpenSearch, Redis does not require reindexing for updates. This makes it well-suited for use cases that rely on real-time modifications to keep AI-driven experiences dynamic and responsive such as:

• Personalization and recommendations: Streaming services and e-commerce platforms update user preferences in real time to serve more relevant content.
• AI chatbots and LLM memory: Customer support bots and AI chatbots need to retain conversational context dynamically without indexing delays.
• Fraud detection and security: Financial institutions and cybersecurity apps continuously refine risk models and behavioral analysis to detect anomalies instantly.
• Gaming and AI-driven NPCs: Games with adaptive AI require real-time behavioral updates to create responsive and engaging experiences.
• Search and semantic caching: AI-powered search platforms refine vector embeddings based on real-time interactions without the cost of reindexing.

Redis keeps updates instant, reducing latency and improving responsiveness in these apps.

Redis is the best choice for GenAI apps

For enterprises building high-performance, scalable AI apps, Redis is the superior choice over OpenSearch. It delivers:

• 50x+ higher QPS and 100x lower query latency for performance sensitive GenAI apps
• Automated scaling, high availability, and seamless backups for easier management.
• Full-text search and exact match filtering alongside vector search.
• Instant updates with no costly reindexing for dynamic data.
• Multi-cloud, hybrid, and on-prem flexibility to fit any infrastructure strategy.

If your AI apps demand real-time performance, search flexibility, and cloud freedom, Redis is the smarter choice. Get started for free with Redis Community Edition or Redis Cloud.