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description Welcome to the Spice.ai Cloud Platform!
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Welcome to Spice.ai

The Spice.ai Cloud Platform is an AI application and agent cloud — an AI-backend-as-a-service with composable, ready-to-use building blocks including high-speed SQL query, LLM inference, vector search, and RAG, built on cloud-scale, managed Spice.ai OSS.

{% hint style="info" %} This documentation covers the Spice.ai Cloud Platform.

For the self-hostable Spice.ai OSS runtime, visit docs.spiceai.org. {% endhint %}

🚀Get StartedSign up and run your first query in minutesget-started
⚡Federated SQL QueryQuery across any data source with one SQL interfacefederated-sql-query.md
🤖AI GatewayOpenAI-compatible API for LLM inferenceai-gateway.md
🔍Search & RetrievalVector and hybrid search for RAG workflowssearch-and-retrieval.md
🔌Data ConnectorsConnect to 30+ databases, warehouses, and lakesdata-connectors
📊MonitoringObserve performance with Grafana, Datadog, and moremonitoring

What You Can Do

With the Spice.ai Cloud Platform you can:

  • Query and accelerate data — Run high-performance SQL queries across multiple data sources with results optimized for AI applications and agents.
  • Use AI models — Perform LLM inference with OpenAI, Anthropic, xAI, and more for chat, completion, and generative AI workflows.
  • Build agentic AI apps — Combine data, models, search, and tools into production-grade AI agent backends.
  • Collaborate on Spicepods — Share, fork, and manage datasets, models, embeddings, evals, and tools in a collaborative hub indexed by spicerack.org.

Use Cases

Use Case Description
Agentic AI Apps Build AI agent backends with unified data and model access
Analytics Replica Run analytics on operational data without ETL or migration
Database CDN Cache and accelerate hot data for low-latency applications
Data Lakehouse Federated queries across warehouses, lakes, and databases
Enterprise Search Semantic search across enterprise data sources
Enterprise RAG Retrieval-augmented generation with your own data

{% columns %} {% column %} Quick Start

Get up and running in minutes:

  1. Sign in with GitHub
  2. Create a Spice app
  3. Add a dataset and query data
  4. Add an AI model and chat

Get started API reference {% endcolumn %}

{% column %} {% code title="query.py" overflow="wrap" %}

from spicepy import Client

client = Client("YOUR_API_KEY")
reader = client.query(
    "SELECT * FROM my_table LIMIT 10"
)
df = reader.read_pandas()
print(df)

{% endcode %} {% endcolumn %} {% endcolumns %}

Community & Support