Safely connect AI to your enterprise data
Provision temporary, scoped AI sandboxes and ensure AI agents or models receive only the minimum data required, governed by detailed policies with full observability.
Do more with your data
Spice enforces fine-grained access controls, encryption, and compliance standards to protect your data from ingestion to inference.
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Up to 100x faster queries
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up to 80x cost savings on data lakehouse spend
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increase in data reliability for critical workloads
Data-centric isolation
Spice provisions temporary, scoped datasets tailored to each model or agent. Access is short-lived, governed by policy, and auditable across the runtime, ensuring AI agents never touch production data directly.
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Principle of least privilege
Every query, embedding, or completion call is executed with only the minimum data required.
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Governed runtime access
All AI interactions run inside a governed runtime. Policies, roles, and encryption are enforced automatically, and every action is logged for compliance and traceability.
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Secure by default
By deploying Spice alongside your agents, sensitive tables remain behind your firewall while apps interact only with safe, pre-materialized datasets. The result is a smaller attack surface and sub-second access to frequently queried data.
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Integrations across all of your data sources
Accelerate your data and AI stack with a library of 40+ prebuilt connectors for the most common databases, warehouses, and file stores- - from Databricks and S3 to MySQL and PostgreSQL. Govern AI sandboxes with the same policies as your production data environments.
Deployed in production
Spice enforces access controls, encryption, and compliance standards to protect your data from ingestion to inference.
“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
FAQs
Answers to common questions about secure AI sandboxing in Spice
What is an AI sandbox in Spice?
An AI sandbox in Spice is a temporary, scoped dataset provisioned for a specific model or agent. Access is short-lived, governed by policy, and auditable across the runtime, so AI agents never touch production data directly.
How does sandboxing protect production databases from AI agents?
Spice deploys alongside your agents, so sensitive tables remain behind your firewall while applications interact only with safe, pre-materialized datasets. This reduces the attack surface, and agents that access data through tools can be governed with the MCP server gateway, which routes tools to models with fine-grained access controls.
Are AI interactions with data logged and auditable?
Yes. All AI interactions run inside a governed runtime where policies, roles, and encryption are enforced automatically, and every action is logged for compliance and traceability. Each query, embedding, or completion call executes with only the minimum data required.
How fast is data access from a sandbox?
Sandboxed applications interact with pre-materialized datasets, which provide sub-second access to frequently queried data. Serving LLM inference in Spice from the same runtime keeps models close to the data they are permitted to use.
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.
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