📄 Docs | ⚡️ Quickstart | 🧑‍🍳 Cookbook

Spice is a SQL query, search, and LLM-inference engine, written in Rust, for data apps and agents.

Spice provides four industry standard APIs in a lightweight, portable runtime (single binary/container):

1. SQL Query & Search: HTTP, Arrow Flight, Arrow Flight SQL, ODBC, JDBC, and ADBC APIs; vector_search and text_search UDTFs.
2. OpenAI-Compatible APIs: HTTP APIs for OpenAI SDK compatibility, local model serving (CUDA/Metal accelerated), and hosted model gateway.
3. Iceberg Catalog REST APIs: A unified Iceberg REST Catalog API.
4. MCP HTTP+SSE APIs: Integration with external tools via Model Context Protocol (MCP) using HTTP and Server-Sent Events (SSE).

🎯 Goal: Developers can focus on building data apps and AI agents confidently, knowing they are grounded in data.

Spice's primary features include:

• Data Federation: SQL query across any database, data warehouse, or data lake. Scale from single-node to distributed multi-node query execution. Learn More.
• Data Materialization and Acceleration: Materialize, accelerate, and cache database queries with Arrow, DuckDB, SQLite, PostgreSQL, or Spice Cayenne (Vortex). Read the MaterializedView interview - Building a CDN for Databases
• Enterprise Search: Keyword, vector, and full-text search with Tantivy-powered BM25 and petabyte-scale vector similarity search via Amazon S3 Vectors or pgvector for structured and unstructured data.
• AI apps and agents: An AI-database powering retrieval-augmented generation (RAG) and intelligent agents with OpenAI-compatible APIs and MCP integration. Learn More.

If you want to build with DataFusion, DuckDB, or Vortex, Spice provides a simple, flexible, and production-ready engine you can just use.

📣 Read the Spice.ai 1.0-stable announcement.

Spice is built-on industry leading technologies including Apache DataFusion, Apache Arrow, Arrow Flight, SQLite, and DuckDB.

🎥 Watch the CMU Databases Accelerating Data and AI with Spice.ai Open-Source

🎥 Watch How to Query Data using Spice, OpenAI, and MCP

🎥 Watch How to search with Amazon S3 Vectors

Spice simplifies building data-driven AI applications and agents by making it fast and easy to query, federate, and accelerate data from one or more sources using SQL, while grounding AI in real-time, reliable data. Co-locate datasets with apps and AI models to power AI feedback loops, enable RAG and search, and deliver fast, low-latency data-query and AI-inference with full control over cost and performance.

• Spice Cayenne Data Accelerator: Simplified multi-file acceleration using the Vortex columnar format + SQLite metadata. Delivers DuckDB-comparable performance without single-file scaling limitations.
• Multi-Node Distributed Query: Scale query execution across multiple nodes with Apache Ballista integration for improved performance on large datasets.
• Acceleration Snapshots: Bootstrap accelerations from S3 for fast cold starts (seconds vs. minutes). Supports ephemeral storage with persistent recovery.
• Iceberg Table Writes: Write to Iceberg tables using standard SQL INSERT INTO for data ingestion and transformation—no Spark required.
• Petabyte-Scale Vector Search: Native Amazon S3 Vectors integration manages the full vector lifecycle from ingestion to embedding to querying. SQL-integrated hybrid search with RRF.

1. AI-Native Runtime: Spice combines data query and AI inference in a single engine, for data-grounded AI and accurate AI.

2. Application-Focused: Designed to run distributed at the application and agent level, often as a 1:1 or 1:N mapping between app and Spice instance, unlike traditional data systems built for many apps on one centralized database. It’s common to spin up multiple Spice instances—even one per tenant or customer.

3. Dual-Engine Acceleration: Supports both OLAP (Arrow/DuckDB) and OLTP (SQLite/PostgreSQL) engines at the dataset level, providing flexible performance across analytical and transactional workloads.

4. Disaggregated Storage: Separation of compute from disaggregated storage, co-locating local, materialized working sets of data with applications, dashboards, or ML pipelines while accessing source data in its original storage.

5. Edge to Cloud Native: Deploy as a standalone instance, Kubernetes sidecar, microservice, or cluster—across edge/POP, on-prem, and public clouds. Chain multiple Spice instances for tier-optimized, distributed deployments.

✅ = Fully supported ❌ = Not supported Limited = Partial or restricted support

• OpenAI-compatible API: Connect to hosted models (OpenAI, Anthropic, xAI, Amazon Bedrock) or deploy locally (Llama, NVIDIA NIM) with OpenAI Responses API support for advanced interactions. AI Gateway Recipe
• Federated Data Access: Query using SQL and NSQL (text-to-SQL) across databases, data warehouses, and data lakes with advanced query push-down for fast retrieval. Scale to distributed multi-node query execution with Apache Ballista. Federated SQL Query Recipe
• Search and RAG: Search and retrieve context with accelerated embeddings for retrieval-augmented generation (RAG) workflows. Native Amazon S3 Vectors integration for petabyte-scale vector search. Full-text search (FTS) via Tantivy-powered BM25 and vector similarity search (VSS) integrated into SQL via text_search and vector_search UDTFs. Reciprocal rank fusion (RRF) for hybrid search. Amazon S3 Vectors Cookbook Recipe
• LLM Memory and Observability: Store and retrieve history and context for AI agents while gaining deep visibility into data flows, model performance, and traces. LLM Memory Recipe | Observability & Monitoring Features Documentation

• Data Acceleration: Co-locate materialized datasets in Arrow, SQLite, DuckDB, PostgreSQL, or Cayenne (Vortex+SQLite) with applications for sub-second query. Bootstrap from snapshots stored in S3 for fast cold starts. Write to Iceberg tables with standard SQL INSERT INTO. DuckDB Data Accelerator Recipe
• Resiliency and Local Dataset Replication: Maintain application availability with local replicas of critical datasets. Recover from federated source outages using acceleration snapshots. Local Dataset Replication Recipe
• Responsive Dashboards: Enable fast, real-time analytics by accelerating data for frontends and BI tools with configurable refresh schedules. Sales BI Dashboard Demo
• Simplified Legacy Migration: Use a single endpoint to unify legacy systems with modern infrastructure, including federated SQL querying across multiple sources. Federated SQL Query Recipe

• Unified Search with Vector Similarity: Perform efficient vector similarity search across structured and unstructured data sources with native Amazon S3 Vectors integration for petabyte-scale vector storage and querying. The Spice runtime manages the vector lifecycle: ingesting data, embedding it using AWS Bedrock (Amazon Titan, Cohere), HuggingFace models, or Model2Vec (500x faster static embeddings), and storing in S3 Vector buckets or pgvector. Supports cosine similarity, Euclidean distance, or dot product. SQL-integrated search via vector_search and text_search UDTFs with hybrid search using reciprocal rank fusion (RRF). Example: SELECT * FROM vector_search(my_table, 'search query', 10) WHERE condition ORDER BY _score;. Amazon S3 Vectors Cookbook Recipe
• Semantic Knowledge Layer: Define a semantic context model to enrich data for AI. Semantic Model Feature Documentation
• Text-to-SQL: Convert natural language queries into SQL using built-in NSQL and sampling tools for accurate query. Text-to-SQL Recipe

• Is Spice a cache? No specifically; you can think of Spice data acceleration as an active cache, materialization, or data prefetcher. A cache would fetch data on a cache-miss while Spice prefetches and materializes filtered data on an interval, trigger, or as data changes using CDC. In addition to acceleration Spice supports results caching.

• Is Spice a CDN for databases? Yes, a common use-case for Spice is as a CDN for different data sources. Using CDN concepts, Spice enables you to ship (load) a working set of your database (or data lake, or data warehouse) where it's most frequently accessed, like from a data-intensive application or for AI context.

➡️ Docs FAQ

See more demos on YouTube.

Catalog Connectors connect to external catalog providers and make their tables available for federated SQL query in Spice. Configuring accelerations for tables in external catalogs is not supported. The schema hierarchy of the external catalog is preserved in Spice.

Install the Spice CLI:

On macOS, Linux, and WSL:

curl https://install.spiceai.org | /bin/bash

Or using brew:

brew install spiceai/spiceai/spice

On Windows using PowerShell:

iex ((New-Object System.Net.WebClient).DownloadString("https://install.spiceai.org/Install.ps1"))

Step 1. Initialize a new Spice app with the spice init command:

spice init spice_qs

A spicepod.yaml file is created in the spice_qs directory. Change to that directory:

cd spice_qs

Step 2. Start the Spice runtime:

spice run

Example output will be shown as follows:

2025/01/20 11:26:10 INFO Spice.ai runtime starting...
2025-01-20T19:26:10.679068Z  INFO runtime::init::dataset: No datasets were configured. If this is unexpected, check the Spicepod configuration.
2025-01-20T19:26:10.679716Z  INFO runtime::flight: Spice Runtime Flight listening on 127.0.0.1:50051
2025-01-20T19:26:10.679786Z  INFO runtime::metrics_server: Spice Runtime Metrics listening on 127.0.0.1:9090
2025-01-20T19:26:10.680140Z  INFO runtime::http: Spice Runtime HTTP listening on 127.0.0.1:8090
2025-01-20T19:26:10.879126Z  INFO runtime::init::results_cache: Initialized sql results cache; max size: 128.00 MiB, item ttl: 1s

The runtime is now started and ready for queries.

Step 3. In a new terminal window, add the spiceai/quickstart Spicepod. A Spicepod is a package of configuration defining datasets and ML models.

spice add spiceai/quickstart

The spicepod.yaml file will be updated with the spiceai/quickstart dependency.

version: v1
kind: Spicepod
name: spice_qs
dependencies:
  - spiceai/quickstart

The spiceai/quickstart Spicepod will add a taxi_trips data table to the runtime which is now available to query by SQL.

2025-01-20T19:26:30.011633Z  INFO runtime::init::dataset: Dataset taxi_trips registered (s3://spiceai-demo-datasets/taxi_trips/2024/), acceleration (arrow), results cache enabled.
2025-01-20T19:26:30.013002Z  INFO runtime::accelerated_table::refresh_task: Loading data for dataset taxi_trips
2025-01-20T19:26:40.312839Z  INFO runtime::accelerated_table::refresh_task: Loaded 2,964,624 rows (399.41 MiB) for dataset taxi_trips in 10s 299ms

Step 4. Start the Spice SQL REPL:

spice sql

The SQL REPL inferface will be shown:

Welcome to the Spice.ai SQL REPL! Type 'help' for help.

show tables; -- list available tables
sql>

Enter show tables; to display the available tables for query:

sql> show tables;
+---------------+--------------+---------------+------------+
| table_catalog | table_schema | table_name    | table_type |
+---------------+--------------+---------------+------------+
| spice         | public       | taxi_trips    | BASE TABLE |
| spice         | runtime      | query_history | BASE TABLE |
| spice         | runtime      | metrics       | BASE TABLE |
+---------------+--------------+---------------+------------+

Time: 0.022671708 seconds. 3 rows.

Enter a query to display the longest taxi trips:

SELECT trip_distance, total_amount FROM taxi_trips ORDER BY trip_distance DESC LIMIT 10;

Output:

+---------------+--------------+
| trip_distance | total_amount |
+---------------+--------------+
| 312722.3      | 22.15        |
| 97793.92      | 36.31        |
| 82015.45      | 21.56        |
| 72975.97      | 20.04        |
| 71752.26      | 49.57        |
| 59282.45      | 33.52        |
| 59076.43      | 23.17        |
| 58298.51      | 18.63        |
| 51619.36      | 24.2         |
| 44018.64      | 52.43        |
+---------------+--------------+

Time: 0.045150667 seconds. 10 rows.

Using the Docker image locally:

docker pull spiceai/spiceai

In a Dockerfile:

from spiceai/spiceai:latest

Using Helm:

helm repo add spiceai https://helm.spiceai.org
helm install spiceai spiceai/spiceai

The Spice.ai Cookbook is a collection of recipes and examples for using Spice. Find it at https://github.com/spiceai/cookbook.

Access ready-to-use Spicepods and datasets hosted on the Spice.ai Cloud Platform using the Spice runtime. A list of public Spicepods is available on Spicerack: https://spicerack.org/.

To use public datasets, create a free account on Spice.ai:

1. Visit spice.ai and click Try for Free.

2. After creating an account, create an app to generate an API key.

Once set up, you can access ready-to-use Spicepods including datasets. For this demonstration, use the taxi_trips dataset from the Spice.ai Quickstart.

Step 1. Initialize a new project.

# Initialize a new Spice app
spice init spice_app

# Change to app directory
cd spice_app

Step 2. Log in and authenticate from the command line using the spice login command. A pop up browser window will prompt you to authenticate:

spice login

Step 3. Start the runtime:

# Start the runtime
spice run

Step 4. Configure the dataset:

In a new terminal window, configure a new dataset using the spice dataset configure command:

spice dataset configure

Enter a dataset name that will be used to reference the dataset in queries. This name does not need to match the name in the dataset source.

dataset name: (spice_app) taxi_trips

Enter the description of the dataset:

description: Taxi trips dataset

Enter the location of the dataset:

from: spice.ai/spiceai/quickstart/datasets/taxi_trips

Select y when prompted whether to accelerate the data:

Locally accelerate (y/n)? y

You should see the following output from your runtime terminal:

2024-12-16T05:12:45.803694Z  INFO runtime::init::dataset: Dataset taxi_trips registered (spice.ai/spiceai/quickstart/datasets/taxi_trips), acceleration (arrow, 10s refresh), results cache enabled.
2024-12-16T05:12:45.805494Z  INFO runtime::accelerated_table::refresh_task: Loading data for dataset taxi_trips
2024-12-16T05:13:24.218345Z  INFO runtime::accelerated_table::refresh_task: Loaded 2,964,624 rows (8.41 GiB) for dataset taxi_trips in 38s 412ms.

Step 5. In a new terminal window, use the Spice SQL REPL to query the dataset

spice sql

SELECT tpep_pickup_datetime, passenger_count, trip_distance from taxi_trips LIMIT 10;

The output displays the results of the query along with the query execution time:

+----------------------+-----------------+---------------+
| tpep_pickup_datetime | passenger_count | trip_distance |
+----------------------+-----------------+---------------+
| 2024-01-11T12:55:12  | 1               | 0.0           |
| 2024-01-11T12:55:12  | 1               | 0.0           |
| 2024-01-11T12:04:56  | 1               | 0.63          |
| 2024-01-11T12:18:31  | 1               | 1.38          |
| 2024-01-11T12:39:26  | 1               | 1.01          |
| 2024-01-11T12:18:58  | 1               | 5.13          |
| 2024-01-11T12:43:13  | 1               | 2.9           |
| 2024-01-11T12:05:41  | 1               | 1.36          |
| 2024-01-11T12:20:41  | 1               | 1.11          |
| 2024-01-11T12:37:25  | 1               | 2.04          |
+----------------------+-----------------+---------------+

Time: 0.00538925 seconds. 10 rows.

You can experiment with the time it takes to generate queries when using non-accelerated datasets. You can change the acceleration setting from true to false in the datasets.yaml file.

Comprehensive documentation is available at spiceai.org/docs.

Over 45 quickstarts and samples available in the Spice Cookbook.

Spice.ai is designed to be extensible with extension points documented at EXTENSIBILITY.md. Build custom Data Connectors, Data Accelerators, Catalog Connectors, Secret Stores, Models, or Embeddings.

🚀 See the Roadmap for upcoming features.

We greatly appreciate and value your support! You can help Spice in a number of ways:

• Build an app with Spice.ai and send us feedback and suggestions at hey@spice.ai or on Discord, X, or LinkedIn.
• File an issue if you see something not quite working correctly.
• Join our team (We’re hiring!)
• Contribute code or documentation to the project (see CONTRIBUTING.md).
• Follow our blog at spiceai.org/blog

⭐️ star this repo! Thank you for your support! 🙏