Application search that scales
Deliver relevant and fast results by combining vector, full-text, and keyword search in one runtime.
Do more with your data
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up to 100x faster queries
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up to 80% cost savings on data lakehouse spend
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increase in data reliability for critical workloads
Modern application search mandates a hybrid approach
Building great application search demands a blend of search methods: keyword for precision, full-text for context, and vector for semantic meaning. Most teams struggle to combine them effectively without standing up separate engines or ETL pipelines. The result is fragmented search logic, inconsistent results, and slower performance.
Relevant and fast application search
Ship application search that combines multiple methods, predictable performance, and full data governance.
Hybrid search out-of-the-box
Blend results for keyword, full-text, and vector similarity using Reciprocal Rank Fusion (RRF). Add per-query weights, filters, and recency boosts so the most relevant and newest results appear first.
Explore hybrid SQL search
Natively integrated with AI
Add categorization, classification, and enrichment directly into your search results using Spice's SQL AI function. Leverage built-in LLM functions for labeling, summarizing, sentiment, and custom tasks.
Explore SQL AI functions
Simple to build, easy to scale
Spice integrates with serverless S3 Vectors and scales with your data and traffic
Get started with Amazon S3 Vectors and Spice
Accelerate locally
Materialize hot data with DuckDB, SQLite or Spice Cayenne acceleration and offload vector loads to scalable object store indexes with Amazon S3 Vectors. Get predictable performance for common queries with index-only reads and filter pushdown.
Explore Spice Cayenne Data Accelerator
Unified search for real-time, data-driven apps
Spice provides SQL-first, unified hybrid search. No extra pipelines or duplicate storage.
SQL-First Hybrid Search
Express all search functions in familiar SQL syntax.
Low-Latency Runtime
Accelerate queries for sub-second responses under high concurrency.
Built-in Re-Ranking
Blend multiple result sets with Reciprocal Rank Fusion for per-query weighting and tunable relevance.
Centralized Policy and Governance
Apply enterprise-grade security, access controls, and audit capabilities to every query.
Single Data and AI Runtime
Combine hybrid search, federated data access, and AI workflows to deliver an enterprise-grade search experience.
Deploy Anywhere
Run Spice anywhere: as a sidecar, microservice, cluster, or on the managed Spice Cloud Platform.
Deployed in production
Run data-intensive workloads on a high-performance engine trusted by teams building real-time systems at scale.
“Spice opened the door to take these critical control-plane datasets and move them next to our services in the runtime path.”
Peter Janovsky
Software Architect, Twilio
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Faster queries
“It just spins up and works, which is really nice. The responsiveness is amazing, which is a huge gain for the customer.”
Darin Douglass
Principal Software Engineer, Barracuda
“Partnering with Spice AI has transformed how NRC Health delivers AI-driven insights. By unifying siloed data across systems, we accelerated AI feature development, reducing time-to-market from months to weeks - and sometimes days. With predictable costs and faster innovation, Spice isn't just solving some of our data and AI challenges - it's helping us redefine personalized healthcare.”
Tim Ottersburg
VP of Technology, NRC Health
“Spice AI grounds AI in our actual data, using SQL queries across many data sources. This brings accuracy to probabilistic AI systems, which are very prone to hallucinations.”
Rachel Wong
CTO, Basis Set
Trusted by global enterprises
Build an application search engine that works
Guides and examples to learn more about introducing application search with Spice.
True Hybrid Search: Vector, Full-Text, and SQL in One Runtime
TL;DR Show Me the (Data)! It’s well established (and maybe even trite) to say that enterprises are going all-in on artificial intelligence, with more than $40 billion directed toward generative AI projects in recent years. The initial results have been underwhelming. A recent study from the Massachusetts Institute of Technology's NANDA initiative concluded that despite the enormous allocation of […]
Real-Time Hybrid Search Using RRF: A Hands-On Guide with Spice
Surfacing relevant answers to searches across datasets has historically meant navigating significant tradeoffs. Keyword (or lexical) search is fast, cheap, and commoditized, but limited by the constraints of exact matching. Vector (or semantic) search captures nuance and intent, but can be slower, harder to debug, and expensive to run at scale. Combining both usually entails standing up multiple engines […]
Making Object Storage Operational for Real-Time and AI Workloads
TLDR Introduction Although legacy systems and workflows remain common, many enterprises are re-evaluating their architectures to meet new demands – driven in part, but not exclusively, by AI – that require support for more data-intensive and real-time applications. The underlying storage needs for these novel workloads are generally outside the bounds of a traditional operational […]
FAQs
Answers to common questions about building application search with Spice
What is hybrid search?
Hybrid search combines keyword, full-text, and vector similarity methods in a single query so results capture both exact matches and semantic meaning. Spice runs these methods in one runtime and merges the results into a single ranked list using Reciprocal Rank Fusion (RRF). Explore hybrid vector and full-text search for how this works in the Spice platform.
Do I need a separate vector database for application search?
No. Spice unifies vector, full-text, and keyword search in one SQL runtime, so you can add semantic search without standing up a separate engine or ETL pipeline. Search results behave like SQL tables, which means you can filter, join, and aggregate them alongside your application data.
How do I tune search relevance for different queries?
Spice blends result sets with Reciprocal Rank Fusion and supports per-query weights, filters, and recency boosts. This lets you weight keyword precision for exact lookups, semantic matches for natural-language queries, and newer content where freshness matters, all expressed in SQL.
Can I enrich search results with AI?
Yes. Spice includes SQL AI functions that apply categorization, classification, labeling, summarization, and sentiment analysis directly to search results within a query. The models run through the LLM inference and AI model serving layer in the same runtime, so no separate enrichment pipeline is required.
How does application search stay fast as data and traffic grow?
Spice materializes hot data locally using DuckDB, SQLite, or Spice Cayenne acceleration and offloads large vector workloads to object store indexes such as Amazon S3 Vectors. Index-only reads and filter pushdown keep performance predictable for common queries under high concurrency.
See Spice in action
Walk through your use case with an engineer and see how Spice handles federation, acceleration, and AI integration for production workloads.
Talk to an engineer