FAQ

Frequently asked questions

What's the difference between the Spice.ai Cloud Platform and Spice.ai OSS?

Spice.ai OSS is an open-source project created by the Spice AI team that provides a unified SQL query interface to locally materialize, accelerate, and query data tables sourced from any database, data warehouse, or data lake.

The Spice.ai OSS runtime

The Spice.ai Cloud Platform is a data and AI application platform that provides a set of building-blocks to create AI and agentic applications. Building blocks include a cloud-data-warehouse, ML model training and inference, and a cloud-scale, managed Spice.ai OSS cloud-hosted service.

The Spice.ai Cloud Platform

How much does Spice.ai Cloud cost?

It's free to get an API key to use the Community Edition.

Customers who need resource limits, service-level guarantees, or priority support we offer high-value paid tiers based on usage.

What level of support do you offer?

We offer enterprise-grade support with an SLA for Enterprise Plans.

For standard plans we offer best-effort community support in Discord.

What's your approach to security and compliance?

See Security. The Spice.ai Cloud Platform is SOC 2 Type II compliant.

What SQL query engine/dialect do you support?

Spice.ai OSS is built on Apache DataFusion as its primary query execution engine, providing vectorized, multi-threaded query processing. It uses the PostgreSQL SQL dialect. Spice also supports DuckDB, SQLite, and PostgreSQL as acceleration engines at the dataset level.

What AI capabilities does Spice provide?

Spice provides unified APIs for data and AI workflows, including model inference, embeddings, and an AI gateway supporting OpenAI, Anthropic, Amazon Bedrock, and xAI. Spice also includes advanced tools such as vector and hybrid search, text-to-SQL, and data sampling.

What AI model providers does Spice support?

Spice supports local model serving (e.g. Llama) and gateways to hosted AI platforms including OpenAI, Anthropic, xAI, and Amazon Bedrock. See Model Providers for details.

Can Spice handle federated queries?

Yes. Spice natively supports federated SQL queries across disparate data sources with advanced query push-down capabilities, executing portions of queries directly on source databases to reduce data transfer and improve performance.

Can Spice integrate with existing BI tools?

Yes. Spice integrates with BI tools through standard SQL interfaces (ODBC, JDBC, ADBC, Arrow Flight SQL), enabling accelerated, real-time analytics for dashboards and reporting.

Does Spice support Change Data Capture (CDC)?

Yes. Spice supports CDC via Debezium, enabling real-time data ingestion and materialization from databases such as PostgreSQL and MySQL.

Does Spice support schema evolution?

Spice infers the schema for datasets and views at startup and does not apply runtime schema changes by default. If the source schema changes while the runtime is running (e.g. columns are added, removed, or their types change), data refreshes will fail with a schema mismatch error rather than silently applying the new schema.

To pick up a new source schema, restart the Spice runtime. On startup, Spice re-infers the schema from the source and the accelerated table is re-initialized with the updated schema.

What is Data-grounded AI?

Data-grounded AI anchors models in accurate, current, domain-specific data rather than relying solely on pre-trained knowledge. Spice unifies enterprise data across databases, data lakes, and APIs, dynamically incorporating real-world context at inference time. This helps minimize hallucinations, reduce operational risk, and build trust in AI by delivering reliable, relevant outputs.

Where can I find examples and recipes?

The Spice.ai Cookbook provides quickstarts and examples demonstrating Spice capabilities, including federated queries, RAG, text-to-SQL, and more.