Bring data close to your applications and users

Build stateful applications with low-latency data access using distributed databases and storage—without managing infrastructure.

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Store structured relational data, application state, and files where your workloads run

Structured data with SQL

Query relational data with SQLite syntax. Create tables, indexes, and joins. Store vectors for AI embeddings alongside application data.

Fast key-value access

Store and retrieve state with sub-10ms latency. Use KV Store for session data, feature flags, and real-time counters.

Object storage at scale

Upload and serve large files with S3-compatible API. Store images, videos, and static assets. Class C operations are FREE.

  • Run SQL queries across distributed infrastructure with low latency and strong consistency. Store application data, vectors for AI, and structured data with vector search capabilities.

  • Store and retrieve state with millisecond response times. Use for session management, feature flags, rate limiting counters, and real-time application state.

  • Store images, videos, documents, and static assets. Process objects with Functions on upload or read. Serve content through Cache with global distribution.

Globally distributed by default

Data stored and served across 100+ locations. Queries and reads run close to users without manual region selection.

Integrated storage services

SQL Database, KV Store, and Object Storage work together in the same architecture. Query from Functions, cache objects, store vectors alongside relational data.

Developer-first APIs

SQLite syntax for SQL, simple key-value APIs for KV, REST API for Object Storage. No new query languages, no complex configuration.

Low-latency operations

Queries respond in milliseconds. Key-value reads under 10ms. Object uploads and downloads optimized for global distribution.

Built-in observability

Query metrics via GraphQL. Storage usage in real-time. Monitor latency, throughput, and errors across all services.

Scale without limits

Auto-scaling storage and compute. Handle traffic spikes and growing datasets without provisioning or capacity planning.

AI Applications: combine vectors, structured data, and Functions

Build AI-powered applications with SQL Database for vectors and relational data, KV Store for session state, and Functions for inference logic. Run RAG pipelines and AI agents close to users.

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AI application architecture with SQL Database vectors

Modern storage patterns built on distributed infrastructure

RAG pipelines

Store embeddings and run vector search for AI responses in one database

Session management

Store sessions with automatic expiration and low-latency reads from any location

Object processing

Trigger Functions on upload to transform files and store results in Object Storage or SQL Database

Feature flags

Toggle features and roll out changes progressively without redeploying applications

Frequently Asked Questions
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Get faster access, simpler scaling, and less storage overhead as data grows.

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