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Spice.ai Runtime Components

Spice runtime components are the building blocks for configuring data access, acceleration, AI models, embeddings, and secrets. Each component is defined in the spicepod.yaml manifest.

Data Connectors connect to databases, data warehouses, data lakes, and file systems for federated SQL queries. Spice supports over 30 connectors including PostgreSQL, MySQL, S3, Snowflake, Databricks, and DuckDB.

Data Accelerators materialize datasets locally in memory or on disk for faster query performance. Choose from Arrow (in-memory), DuckDB, SQLite, PostgreSQL, or Cayenne depending on workload characteristics.

Catalog Connectors integrate with data catalogs like Apache Iceberg, Unity Catalog, AWS Glue, and DuckLake to discover and register datasets from existing catalog infrastructure.

Models configure LLM providers for AI inference through an OpenAI-compatible API. Connect to hosted models (OpenAI, Anthropic, xAI) or serve models locally with CUDA/Metal acceleration.

Embeddings generate vector representations of text for semantic search and RAG workflows, using built-in models or external providers.

Tools define callable functions that LLMs can invoke during inference, including MCP (Model Context Protocol) integrations for connecting to external services.

Views create virtual tables from SQL queries over other datasets, similar to database views.

Secret Stores manage credentials and sensitive configuration values using environment variables, files, or external secret managers like AWS Secrets Manager and Azure Key Vault.

Workers coordinate interactions between models and tools, supporting load balancing strategies like round-robin and fallback across multiple LLM providers.