Build secure, governed AI agents
Provision short-lived, least-privilege sandboxes for every agent. Federate the right context and enforce policy at query time.
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
Agents need context - without risking your data
Generic agent frameworks over-permission access, leading to sensitive data exposure and limited audit trails. Security teams need least-privilege context, inline policy enforcement, and full traces.
Security is the foundation, not an afterthought
Spice sandboxes, governs, and accelerates agent context in one runtime.
Data-centric isolation
Provision short-lived, scoped datasets per task or session. Apply least-privilege access to only the data an agent needs.
Federated context with security controls
Spice presents a single SQL layer over databases, object storage, APIs, and catalogs so applications operate on federated context rather than isolated pipelines.
Inline policy and guardrails
Apply role-based access controls, redact sensitive fields, and restrict tool access by role, dataset, or environment before the prompt ever reaches a model.
LLM gateway with distributed tracing
Route to hosted or local models through the AI Gateway. Capture prompts, retrieve context, tool calls, and outputs for evaluation, red-teaming, and compliance audits.
Why teams trust Spice for secure, production-grade AI agents
Spice unifies federated context, least-privilege sandboxes, policy enforcement, and LLM governance so agents are both useful and secure.
Native Sandboxing
Scope datasets per agent/task and auto-expire to limit blast radius.
Policy Enforcement
Apply guardrails at query time. Restrict tools and enforce roles dynamically.
End-to-End Audit
Trace all prompt inputs, context, and model invocations for every agent.
Federated and Governed Context
Retrieve only permitted structured and unstructured data.
Operational Performance
Local acceleration delivers ms latency context for agents, even with full controls.
Deployment Flexibility
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 all our data. This brings accuracy to probabilistic AI systems, which are very prone to hallucinations.”
Rachel Wong
CTO, Basis Set
Trusted by global enterprises
Build more performant and secure AI agents
Guides and examples to learn more about deploying secure, high-performance AI agents with Spice.
AI Agent Docs
Explore the docs for examples and guidance on building AI agents with Spice.
Intelligent Security Copilot Cookbook
Follow this step-by-step recipe to stream query logs into Spice, apply AI-driven pattern analysis, and surface alerts for suspicious database activity.
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 […]
See Spice in action
Get a guided walkthrough of how development teams use Spice to query, accelerate, and integrate AI for mission-critical workloads.
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