Spice is a SQL query and AI compute engine, written in Rust, for data-driven apps and agents.

Spice provides three industry standard APIs in a lightweight, portable runtime (single ~140 MB binary):

1. SQL Query APIs: Arrow Flight, Arrow Flight SQL, ODBC, JDBC, and ADBC.
2. OpenAI-Compatible APIs: HTTP APIs compatible the OpenAI SDK, AI SDK with local model serving (CUDA/Metal accelerated) and gateway to hosted models.
3. Iceberg Catalog REST APIs: A unified Iceberg Catalog API.

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

Spice is primarily used for:

• Data Federation: SQL query across any database, data warehouse, or data lake. Learn More.
• Data Materialization and Acceleration: Materialize, accelerate, and cache database queries. Read the MaterializedView interview - Building a CDN for Databases
• AI apps and agents: An AI-database powering retrieval-augmented generation (RAG) and intelligent agents. Learn More.

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

📣 Read the Spice.ai OSS announcement blog post.

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

Why Spice?

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.

How is Spice different?

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.

How does Spice compare?

Data Query and Analytics

Feature	Spice	Trino / Presto	Dremio	ClickHouse	Materialize
Primary Use-Case	Data & AI apps/agents	Big data analytics	Interactive analytics	Real-time analytics	Real-time analytics
Primary deployment model	Sidecar	Cluster	Cluster	Cluster	Cluster
Federated Query Support	✅	✅	✅	―	―
Acceleration/Materialization	✅ (Arrow, SQLite, DuckDB, PostgreSQL)	Intermediate storage	Reflections (Iceberg)	Materialized views	✅ (Real-time views)
Catalog Support	✅ (Iceberg, Unity Catalog)	✅	✅	―	―
Query Result Caching	✅	✅	✅	✅	Limited
Multi-Modal Acceleration	✅ (OLAP + OLTP)	―	―	―	―
Change Data Capture (CDC)	✅ (Debezium)	―	―	―	✅ (Debezium)

AI Apps and Agents

Feature	Spice	LangChain	LlamaIndex	AgentOps.ai	Ollama
Primary Use-Case	Data & AI apps	Agentic workflows	RAG apps	Agent operations	LLM apps
Programming Language	Any language (HTTP interface)	JavaScript, Python	Python	Python	Any language (HTTP interface)
Unified Data + AI Runtime	✅	―	―	―	―
Federated Data Query	✅	―	―	―	―
Accelerated Data Access	✅	―	―	―	―
Tools/Functions	✅	✅	✅	Limited	Limited
LLM Memory	✅	✅	―	✅	―
Evaluations (Evals)	✅	Limited	―	Limited	―
Search	✅ (VSS)	✅	✅	Limited	Limited
Caching	✅ (Query and results caching)	Limited	―	―	―
Embeddings	✅ (Built-in & pluggable models/DBs)	✅	✅	Limited	―

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

Example Use-Cases

Data-grounded Agentic AI Applications

• OpenAI-compatible API: Connect to hosted models (OpenAI, Anthropic, xAI) or deploy locally (Llama, NVIDIA NIM). 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 across disparate data sources. Federated SQL Query Recipe
• Search and RAG: Search and retrieve context with accelerated embeddings for retrieval-augmented generation (RAG) workflows. Vector Search over GitHub Files
• 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 | Monitoring Features Documentation

Database CDN and Query Mesh

• Data Acceleration: Co-locate materialized datasets in Arrow, SQLite, and DuckDB with applications for sub-second query. DuckDB Data Accelerator Recipe
• Resiliency and Local Dataset Replication: Maintain application availability with local replicas of critical datasets. Local Dataset Replication Recipe
• Responsive Dashboards: Enable fast, real-time analytics by accelerating data for frontends and BI tools. 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

Retrieval-Augmented Generation (RAG)

• Unified Search with Vector Similarity: Perform efficient vector similarity search across structured and unstructured data sources. Vector Search over GitHub Files
• 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
• Model and Data Evaluations: Assess model performance and data quality with integrated evaluation tools. Language Model Evaluations Recipe

FAQ

• 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.

Watch a 30-sec BI dashboard acceleration demo

BI.dashboard.acceleration.with.Spice.mp4

See more demos on YouTube.

Supported Data Connectors

Name	Description	Status	Protocol/Format
databricks (mode: delta_lake)	Databricks	Stable	S3/Delta Lake
duckdb	DuckDB	Stable	Embedded
file	File	Stable	Parquet, CSV
github	GitHub	Stable	GitHub API
dremio	Dremio	Release Candidate	Arrow Flight
graphql	GraphQL	Release Candidate	JSON
mysql	MySQL	Release Candidate
postgres	PostgreSQL	Release Candidate
s3	S3	Release Candidate	Parquet, CSV
databricks (mode: spark_connect)	Databricks	Beta	Spark Connect
delta_lake	Delta Lake	Beta	Delta Lake
flightsql	FlightSQL	Beta	Arrow Flight SQL
mssql	Microsoft SQL Server	Beta	Tabular Data Stream (TDS)
odbc	ODBC	Beta	ODBC
snowflake	Snowflake	Beta	Arrow
spark	Spark	Beta	Spark Connect
spice.ai	Spice.ai	Beta	Arrow Flight
abfs	Azure BlobFS	Alpha	Parquet, CSV
clickhouse	Clickhouse	Alpha
debezium	Debezium CDC	Alpha	Kafka + JSON
dynamodb	Amazon DynamoDB	Alpha
ftp, sftp	FTP/SFTP	Alpha	Parquet, CSV
http, https	HTTP(s)	Alpha	Parquet, CSV
iceberg	Apache Iceberg	Alpha	Parquet
localpod	Local dataset replication	Alpha
sharepoint	Microsoft SharePoint	Alpha	Unstructured UTF-8 documents
mongodb	MongoDB	Coming Soon
elasticsearch	ElasticSearch	Roadmap

Supported Data Accelerators

Name	Description	Status	Engine Modes
arrow	In-Memory Arrow Records	Release Candidate	memory
duckdb	Embedded DuckDB	Release Candidate	memory, file
postgres	Attached PostgreSQL	Release Candidate	N/A
sqlite	Embedded SQLite	Release Candidate	memory, file

Supported Model Providers

Name	Description	ML Format(s)	LLM Format(s)
file	Local filesystem	ONNX	GGUF, GGML, SafeTensor
huggingface	Models hosted on HuggingFace	ONNX	GGUF, GGML, SafeTensor
spice.ai	Models hosted on the Spice.ai Cloud Platform	ONNX	OpenAI-compatible HTTP endpoint
openai	OpenAI (or compatible) LLM endpoint	-	OpenAI-compatible HTTP endpoint
azure	Azure OpenAI	-	OpenAI-compatible HTTP endpoint
anthropic	Models hosted on Anthropic	-	OpenAI-compatible HTTP endpoint
xai	Models hosted on xAI	-	OpenAI-compatible HTTP endpoint

Supported Catalogs

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.

Name	Description	Status	Protocol/Format
databricks	Databricks	Alpha	Spark Connect, S3/Delta Lake
unity_catalog	Unity Catalog	Alpha	Delta Lake
spice.ai	Spice.ai Cloud Platform	Alpha	Arrow Flight
iceberg	Apache Iceberg	Alpha	Parquet
glue	AWS Glue	Coming Soon	JSON, Parquet, Iceberg

⚡️ Quickstart (Local Machine)

quickstart.mp4

Step 1. 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:

curl -L "https://install.spiceai.org/Install.ps1" -o Install.ps1 && PowerShell -ExecutionPolicy Bypass -File ./Install.ps1

Step 2. 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 3. Start the Spice runtime:

spice run

Example output will be shown as follows:

Spice.ai runtime starting...
2024-08-05T13:02:40.247484Z  INFO runtime::flight: Spice Runtime Flight listening on 127.0.0.1:50051
2024-08-05T13:02:40.247490Z  INFO runtime::metrics_server: Spice Runtime Metrics listening on 127.0.0.1:9090
2024-08-05T13:02:40.247949Z  INFO runtime: Initialized results cache; max size: 128.00 MiB, item ttl: 1s
2024-08-05T13:02:40.248611Z  INFO runtime::http: Spice Runtime HTTP listening on 127.0.0.1:8090
2024-08-05T13:02:40.252356Z  INFO runtime::opentelemetry: Spice Runtime OpenTelemetry listening on 127.0.0.1:50052

The runtime is now started and ready for queries.

Step 4. 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.

2024-08-05T13:04:56.742779Z  INFO runtime: Dataset taxi_trips registered (s3://spiceai-demo-datasets/taxi_trips/2024/), acceleration (arrow, 10s refresh), results cache enabled.
2024-08-05T13:04:56.744062Z  INFO runtime::accelerated_table::refresh_task: Loading data for dataset taxi_trips
2024-08-05T13:05:03.556169Z  INFO runtime::accelerated_table::refresh_task: Loaded 2,964,624 rows (421.71 MiB) for dataset taxi_trips in 6s 812ms.

Step 5. 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.

⚙️ Runtime Container Deployment

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

🏎️ Next Steps

Explore the Spice.ai Cookbook

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

Using Spice.ai Cloud Platform

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.

📄 Documentation

Comprehensive documentation is available at docs.spiceai.org.

Over 45 quickstarts and samples available in the Spice Cookbook.

🔌 Extensibility

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.

🔨 Upcoming Features

🚀 See the Roadmap to v1.0-stable for upcoming features.

🤝 Connect with us

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 blog.spiceai.org

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