SurrealDB

SurrealDB emerges as a revolutionary multi-model database platform, engineered to simplify and accelerate the development of modern, data-intensive applications. It fundamentally changes how developers interact with data by consolidating the capabilities of multiple database systems—such as document, relational, graph, and time-series databases—into a single, cohesive, and powerful engine. Written in Rust, SurrealDB offers exceptional performance, memory safety, and reliability. It is designed to be the ultimate backend data layer, eliminating the need for complex technology stacks and allowing teams to focus on building innovative features rather than managing infrastructure.

The platform's core philosophy is to provide a seamless developer experience. It achieves this through SurrealQL, an expressive and intuitive SQL-like query language that extends traditional SQL with advanced features for handling nested data, graph relationships, and real-time updates. With native support for vector embeddings and in-database machine learning inference, SurrealDB is purpose-built for the new era of AI-native applications, making it an ideal choice for building everything from sophisticated RAG (Retrieval-Augmented Generation) systems to real-time collaborative platforms.

How to use SurrealDB

Getting started with SurrealDB is designed to be straightforward, catering to various development needs and environments.

1. Deployment: You can choose from multiple deployment options. The easiest way is to use Surreal Cloud, a fully managed service that handles all infrastructure operations. Alternatively, for full control, you can self-host SurrealDB using Docker, pre-compiled binaries, or by building from source. It can even run directly in the browser via WebAssembly, using IndexedDB for persistence.
2. Connection: Connect to your database instance using the extensive range of official SDKs, including JavaScript/TypeScript, Python, Rust, Go, Java, .NET, and PHP. The SurrealDB command-line interface (CLI) is another powerful tool for managing databases, importing/exporting data, and running queries directly.
3. Data Modeling: SurrealDB offers the flexibility to start with a schemaless model for rapid prototyping. As your application matures, you can enforce data integrity by defining schemas using `DEFINE TABLE`, `DEFINE FIELD`, `DEFINE INDEX`, and `DEFINE EVENT` statements in SurrealQL.
4. Querying and Manipulation: Interact with your data using SurrealQL. Use familiar statements like `CREATE`, `SELECT`, `UPDATE`, and `DELETE`. For graph data, use the intuitive `RELATE` statement to create connections between records (e.g., `RELATE user:tobie->writes->article:surrealdb`).
5. Building AI and Real-time Features: Leverage `LIVE SELECT` to subscribe to data changes in real-time. For AI applications, store your vector embeddings and perform similarity searches. Use SurrealML to import pre-trained models (PyTorch, Tensorflow) and run `ML::INFER` queries directly in the database.

Core Features of SurrealDB

• Multi-Model Database: Natively supports document, relational, graph, and time-series data models, allowing you to model complex domains without multiple databases.
• SurrealQL: An advanced, SQL-like query language with built-in support for graph traversals, geospatial queries, JSON patching, and real-time notifications.
• Vector Search & Embeddings: First-class support for storing, indexing (with HNSW, IVF), and querying high-dimensional vector embeddings for AI applications like semantic search and recommendation engines.
• In-Database Machine Learning (SurrealML): Import and run inference on machine learning models (PyTorch, Tensorflow, Sklearn) directly within the database, bringing computation closer to the data.
• Real-time Capabilities: Live Queries allow clients to subscribe to query results, receiving updates automatically as the underlying data changes.
• Advanced Security Model: Granular, policy-based access control for tables, rows, and fields. Supports JWT-based authentication and third-party providers.
• Scalable Architecture: Engineered to scale from a single-node in-memory instance to a globally distributed, fault-tolerant cluster.
• Extensive Connectivity & SDKs: Comprehensive support via REST, WebSocket APIs, and a wide array of official SDKs for popular programming languages and frameworks.

Use Cases for SurrealDB

SurrealDB's versatile nature makes it suitable for a wide range of applications:

• AI-Native Applications: Ideal for building Retrieval-Augmented Generation (RAG) systems, chatbots, semantic search engines, and personalized recommendation systems by combining its vector search and in-database ML capabilities.
• Real-time Collaborative Platforms: Powering applications like collaborative editors (e.g., Google Docs), whiteboards, project management tools, and live dashboards that require instant data synchronization.
• Modern Web & Mobile Backends: Serves as a complete Backend-as-a-Service (BaaS), simplifying the tech stack for startups and enterprises by providing database, authentication, and real-time APIs in one.
• Graph-based Systems: Building social networks, knowledge graphs, identity and access management systems, and fraud detection engines using its powerful and intuitive graph data model.
• IoT & Time-Series Analysis: Efficiently handling time-series data from IoT devices, with features for aggregation, windowing, and real-time analysis.

Advantages of SurrealDB

Choosing SurrealDB provides several key advantages:

• Radical Simplification: Replaces a complex ecosystem of databases (e.g., PostgreSQL + Neo4j + Elasticsearch) and services with a single, unified platform, reducing operational overhead and development complexity.
• Enhanced Developer Productivity: The intuitive SurrealQL, comprehensive documentation, and extensive SDKs enable developers to build features faster and with less code.
• Future-Proof Architecture: Built from the ground up for modern application requirements, including AI integration, real-time data streaming, and complex data relationships.
• High Performance: Being written in Rust ensures high throughput, low latency, and efficient resource utilization.
• Ultimate Flexibility: The ability to switch between schemaless and schemafull modes, combined with its multi-model design, allows the database to evolve with your application's needs.

Pricing and Plans

SurrealDB offers a flexible pricing structure through its Surreal Cloud platform, designed to scale with your project's needs.

• Free Plan: Perfect for hobbyists, prototypes, and getting started. This plan includes 1 GB of storage, 0.25 vCPU, 1 GB of memory, and community support.
• Start Plan: A pay-as-you-go plan starting from $0.021 per hour, designed for development and staging environments. It offers vertical scalability, allowing you to increase resources as needed, along with daily automated backups.
• Scale Plan (Coming Soon): Aimed at production applications that require high availability and horizontal scalability. This plan will feature fault-tolerant deployments and multi-tenant storage.
• Dedicated Plan: An enterprise-grade solution for mission-critical applications. It provides dedicated fault-tolerant clusters, advanced security features like bring-your-own-key (BYOK), AWS PrivateLink, and custom SLAs. Contact sales for pricing.