Skip to content

faq.md

Latest commit

 

History

History
94 lines (53 loc) · 6.98 KB

faq.md

File metadata and controls

94 lines (53 loc) · 6.98 KB
Raw
description Frequently asked questions
icon circle-question

FAQ

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

  • Spice.ai OSS — the open-source Spice runtime. Self-hosted, free, Apache 2.0.
  • Spice.ai Cloud — a managed, multi-tenant hosted service running Spice as a platform with additional building blocks (cloud data warehouse, model training/inference, AI gateway).
  • Spice.ai Enterprise — a self-hosted enterprise distribution of Spice with advanced features (HA, RBAC, SSO, governance, premium connectors), enterprise support, and an SLA.

See Distributions for a detailed comparison.

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 streaming ingestion from several sources:

  • Native PostgreSQL logical replication (recommended for PostgreSQL sources). Spice connects directly to the source using Postgres' wal_level=logical and streams INSERT/UPDATE/DELETE events into the accelerator. See PostgreSQL Logical Replication in the OSS documentation.
  • DynamoDB Streams for Amazon DynamoDB sources — Spice consumes the table's change stream and applies INSERT/UPDATE/DELETE events to the accelerator with refresh_mode: changes.
  • Apache Kafka for event-streaming topics — Spice consumes records directly with refresh_mode: append for real-time, append-only acceleration.
  • Debezium (over Kafka) for sources where Debezium is already deployed, or for databases without a native Spice CDC path (MySQL, SQL Server, etc.).

How do I keep an accelerated dataset incrementally up-to-date?

For sources with a monotonically-increasing version column (e.g. updated_at), Spice incrementally ingests new and modified records using time_column + refresh_mode: append, with refresh_append_overlap to tolerate clock skew and retention_period to evict old or soft-deleted records. See Incremental Ingestion for configuration details and examples.

Does Spice support schema evolution?

Spice infers the schema for datasets and views at startup. How the runtime responds to a source schema change while running (e.g. columns added, removed, or retyped) is controlled per dataset by the on_schema_change setting.

By default (on_schema_change: block), Spice does not apply source schema changes automatically: the dataset stays healthy and continues serving queries using the schema registered at startup. To evolve the schema without restarting, set on_schema_change to one of:

  • append_new_columns — add newly-appeared source columns; reject removals and incompatible changes.
  • sync_all_columns — keep the registered schema synchronized with the source schema.
  • drop_and_recreate — apply widening changes in place; otherwise drop and recreate the accelerated table (destructive, and only with refresh_mode: full).
  • fail — fail the refresh when the source schema diverges.

Restarting the runtime also re-infers the schema from the source and re-initializes the accelerated table.

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.