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descriptionWelcome 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 CaseDescription
Agentic AI AppsBuild AI agent backends with unified data and model access
Database CDNCache and accelerate hot data for low-latency applications
Data LakehouseFederated queries across warehouses, lakes, and databases
Enterprise SearchSemantic search across enterprise data sources
Enterprise RAGRetrieval-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 startedAPI reference {% endcolumn %}

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

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

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

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