Power real-time, intelligent SaaS
Deliver responsive, personalized, and always-available applications with unified data, accelerated performance, and native AI integration.
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
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
Trusted by global enterprises
Power real-time SaaS at scale
Guides and examples to learn more about querying data, building apps, and integrating AI with Spice.
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.
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
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
Common questions about using Spice.ai in SaaS applications
How do SaaS companies use Spice.ai?
SaaS companies use Spice.ai as a data and AI runtime that serves live, governed data to their applications. It federates queries across warehouses, databases, and object storage using SQL federation and acceleration, and materializes active datasets near the application for millisecond reads. This replaces brittle ETL pipelines and separate caching layers with a single runtime.
How does Spice.ai support multi-tenant SaaS architectures?
Spice.ai supports multi-tenancy through sharded deployments, where teams run multiple Spice Runtime instances split by customer, region, or workload. Each tenant gets dedicated resources and a tailored configuration for performance, security, and resiliency. The runtime runs as a sidecar, microservice, or cluster, or on the managed Spice Cloud Platform.
How does Spice.ai keep SaaS application data fresh?
Spice.ai keeps application data fresh using real-time change data capture, which streams updates from source systems into locally accelerated tables. Data such as CRM records stays continuously synced without batch jobs. Applications read from the local copy, so freshness does not come at the cost of latency.
What does CDN-like performance mean for SaaS data?
It means Spice.ai co-locates active datasets next to the application, the way a CDN places content near users. Reads are served from local materializations built on DuckDB, SQLite, and Arrow, keeping latency in milliseconds. Because the data lives with the application, it stays available during high traffic or upstream provider downtime.
Can Spice.ai add AI features to an existing SaaS product?
Yes. Spice.ai runs LLMs and hybrid search inside the same runtime that serves application data, so teams can add personalized recommendations, summaries, and automation without deploying a separate AI stack. Queries and inference outputs are traceable for compliance and governance.
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