Power real-time, intelligent SaaS

Deliver responsive, personalized, and always-available applications with unified data, accelerated performance, and native AI integration.

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Do more with your data

• 100x – up to 100x faster queries
• 80% – up to 80% cost savings on data lakehouse spend
• 2x – increase in data reliability for critical workloads

Software development teams face a data bottleneck

Modern SaaS apps depend on enterprise data to power dynamic user experiences. But brittle ETL, disconnected analytics stacks, and latency from remote databases slow down feature delivery and responsiveness. Teams need governed, low-latency access to all their data-without adding new systems or operational drag.

Deliver faster and more resilient software

Unify data, accelerate performance, and embed AI directly into your app workflows.

Unified query and search layer

Access data across your warehouse, database, and storage systems from a single runtime. Simplify application logic and remove fragile integration layers.

Sharded deployment for secure multi-tenancy

Run multiple Spice Runtime instances, sharded by customer, region, or workload. Provide each tenant with dedicated resources and tailored configurations for performance, security, and resiliency.

CDN-like performance

Co-locate active datasets near your application for instant reads and resilience during high traffic or provider downtime.

Built-in AI and automation

Add LLM-powered features and intelligent agents directly within the Spice runtime. Deliver personalized recommendations, summaries, and automation with full governance.

Purpose-built for SaaS speed and scale

Spice eliminates data friction. Federate, accelerate, and serve AI from one governed runtime designed for always-on, high-traffic SaaS environments.

Accelerated Performance

Local materialization via DuckDB, SQLite, and Arrow for millisecond latency

Real-Time Data Sync

CDC-based updates keep CRMs and app data continuously fresh.

Embedded AI

Run LLMs and hybrid search inside the runtime for intelligent workflows.

Observability & Governance

Trace data, queries, and inference outputs for compliance and trust.

Deployment Flexibility

Run Spice anywhere: as a sidecar, microservice, cluster, or on the managed Spice Cloud Platform.

Developer-First Design

Deploy and query with SQL or REST. No orchestration tools or data ops needed.

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

Power real-time SaaS at scale

Guides and examples to learn more about querying data, building apps, and integrating AI with Spice.

[Recipe
Data Acceleration with DuckDB Cookbook
This recipe will walkthrough how to accelerate a local copy of the taxi trips dataset stored in S3 using DuckDB as the data accelerator engine.

[Docs
Spice Federation and Acceleration Docs
Learn how to get started with Spice federation and acceleration. Query, join, and accelerate data using SQL from multiple sources, including databases, data warehouses, and data lakes

[Blog
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 …