Spice makes it easy for developers to create production-ready
AI-native data warehouses using Apache Arrow, DuckDB, and SQLite.
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Query structured and unstructed data using SQL across databases, data warehouses, and data lakes.
DocsSelf-hostable binary or Docker image, platform-agnostic, and Apache 2.0 licensed. Built on industry standard technologies including Apache DataFusion and Apache Arrow.
Materialize data and content in DuckDB, SQLite, and PostgreSQL; in-memory or on disk. Results caching included.
DocsLoad-balance across local models and OpenAI compatible endpoints with full observability, metrics, telemetry, query/task history, data flows, and tracing.
See how Spice has been deployed in production architectures.
Slow 15 sec queries across 100B+ rows.
Poor user experience with slow page loads.
Unnecessary Databricks workspace expense.
Unified SQL Interface for effortlessly materializing, accelerating, and querying data from any database, data warehouse, or data lake.
Accelerate and co-locate datasets with applications and frontends, to serve more concurrent queries and users with faster page loads and data updates
Faster, more responsive dashboards without massive compute costs.
Co-locate datasets in pipelines where the data is needed to minimize data-movement and improve query performance.
Federated SQL query across databases, data warehouses, and data lakes using Data Connectors.
Try materializing and querying Parquet files from S3, on your laptop, in under 60 seconds.
Announcing the release of Spice v0.20-beta 🧩 Spice v0.20.0-beta improves federated query performance with column pruning and adds support for Metal (Apple Silicon) and CUDA (NVidia) accelerators. The S3, PostgreSQL, MySQL, and GitHub Data Connectors have graduated from Beta to Release Candidates. The Arrow, DuckDB, and SQLite Data Accelerators have graduated from Alpha to Beta.
Announcing the release of Spice v0.19.4-beta ☘️ Spice v0.19.4-beta introduces a new `localpod` Data Connector, improvements to accelerator resiliency and control, and a new configuration to control when accelerated datasets are considered ready.
Announcing the release of Spice v0.19.3-beta 📈 Spice v0.19.3-beta improves the performance and stability of data connectors and accelerators, including faster queries across multiple federated sources by optimizing how filters are applied. Anthropic has also been added as a LLM model provider.
“We have been looking for a way to accelerate queries from our Databricks workspaces. Spice was the perfect solution, as it was super simple to setup and it was easy to define and query accelerated datasets without a lot of overhead.”
Chief Data Officer at Barracuda
