Fast, secure data
access for agents

Give agents high-concurrency, high-throughput access to operational databases, warehouses, and APIs. Analytical queries run on an isolated replica of your operational data, kept current in real time through CDC.

Get a demo (opens in a new tab)

View the cookbook (opens in a new tab)

Diagram of three AI agents, each connected through its own Spice.ai instance to a shared data store that spans Databricks, PostgreSQL, Snowflake, and S3

Fast, current data at agent scale

31x

faster than querying object storage directly (TPC-H SF-100, one node)

566K

rows per second of CDC ingest, so agent data stays current (CH-benCHmark SF-1000)

2.9x

faster on three nodes than one as agent workloads grow (TPC-H SF-100)

Built for how agents use data

Agents query often, in bursts, and across many sources. Spice gives each agent fast, scoped, and audited access to the data it needs. Spice stays simple to operate from one deployment to thousands.

Real-time analytics

Analytical query on operational data

Spice replicates the operational data an agent needs into an isolated analytics replica, kept current in real time through high-throughput CDC.

Read the CDC docs (opens in a new tab)

Real-time analytics on operational data: an app writes to MySQL, an isolated Spice analytics replica is added beside it, CDC streams each change into the replica, and agents' heavy analytical queries run on Spice without touching MySQLisolatedCDCMySQLanalytics replicaAppAgent 1Agent 2Real-time analytics on operational data
Query without ETL

Data access without ETL

Spice federates databases, warehouses, object storage, and APIs into one SQL layer. Agents call SQL, hybrid search, schema discovery, and your MCP servers through one authenticated MCP endpoint.

Read the federation docs (opens in a new tab)

Isolated agent data

Physical isolation

Each agent queries a Spice runtime that holds only the datasets its Spicepod declares. Datasets are read-only by default. The agent holds a Spice API key, not database credentials.

Read the authentication docs (opens in a new tab)

Per-tenant lakehouses

One standard lakehouse for every tenant

Spice standardizes lakehouse deployments for every tenant, so adding a customer is a configuration change instead of managing additional data infrastructure.

Read the Management API docs

Tool access and audit

Controls and audit on every call

A model calls only the tools you list, within the rate limits you set. Task history records each SQL query, model call, and tool call.

Read the tools docs (opens in a new tab)

Connect an agent to your data

MCP example

An example of connecting an agent to Spice through MCP.

# spicepod.yamlruntime:  auth:    api_key:      enabled: true  # /v1/mcp requires auth      keys:        - ${secrets:SPICE_API_KEY}datasets:  - from: postgres:public.orders    name: orders    params:      pg_host: db.example.com      pg_pass: ${secrets:PG_PASS}    acceleration:      enabled: true  # A local, accelerated copy for agents  - from: snowflake:DB.SALES.DEALS    name: deals  # A warehouse table, credentials omitted for simplicity$ claude mcp add --transport http spice \    http://localhost:8090/v1/mcp \    --header "X-API-KEY: <key>"sql> -- the agent calls the sql tool over MCP     SELECT o.status, COUNT(*) AS orders,       d.stage AS deal_stage     FROM orders o            -- Postgres     JOIN deals d             -- Snowflake       ON d.customer_id = o.customer_id     WHERE o.customer_id = 42     GROUP BY o.status, d.stage;# example outputshipped    18  negotiationpending     3  negotiation

  1. Connect and accelerate your data

    Define each dataset in spicepod.yaml and set acceleration to enabled.

  2. Run Spice next to your agents

    Start Spice with spice run, or deploy it as a sidecar, in your cluster, or on Spice Cloud.

  3. Query from any client

    Agents call Spice over MCP, SQL over HTTP, Arrow Flight, JDBC, ODBC, or the OpenAI-compatible API.

  1. /plugin marketplace add spiceai/skills
  2. /plugin install spiceai@spiceai

Proven in production

Teams run Spice to serve fast data to apps and agents at scale.

Peter Janovsky

“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

Darin Douglass

100x

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

Learn how Barracuda gained 100x faster query responses and reduced costs 50% with Spice