Onyx Database AI-Controlled Database Cloud Services
Structured data, made flexible and intelligent.
An API-driven database management system with a built-in for designing, generating, visualizing, and managing repeatable workflows across relational data.
const widget = {
name: "M2-55",
category: "Electronics",
description: "A powerful gadget..."
}
await db.save(tables.Product, widget);
const results = await db.from(tables.Product)
.search("gadget AND powerful")
.and("category", "Electronics")
.list();const answer = await db.chat("what is a M2-55?");What Is Onyx?
A faster approach at developing web applications
Onyx Cloud is an API-driven database management system and with built-in RAG (Retrieval Augmented Generation) capabilities for querying data across structured tables (using REST API and client SDK builder-pattern queries) and unstructured documents (inverted index searching).
It automatically generates optimized queries for structured data and enables natural-language access across your entire data model. Onyx can be used to atomically save data with extremely fast read/write performance, as well as resolve flexible relationships to other graphs.
Built for Evolving Teams
Onyx is designed for systems where data is:
- Highly connected
- Constantly evolving
- Analyzed continuously
Why Traditional Databases Slow you down
The Old Way
- • Fixed schemas and rigid relationship constraints
- • Manually written static queries
- • Analysis performed outside the DB
The Pitfalls
Approaches like this don't evolve and have limited AI capabilities; the result is less data-driven AI insights.
How Onyx Is Different
Onyx models data as entities in collections, but relationships are not stored as rigid edges.
- Relationships are still defined at a declarative schema level
- Resolver queries script retreive connected nodes
- Graphs attach at the model level
- Resolution happens inside the DB
Backed by indexed based joining and optimized traversal structures on solid state drives to mitigate N+1 performance issues.
AI Control Plane
Rather than writing one-off SQL, users work with workflow trees that analyze intent, generate queries, and route to dynamic workflows
First-Class Outputs
- statistical summaries
- derived datasets
- narrative explanations
- visualization-ready results
How Teams Use Onyx
- Graph-shaped domain models
- Analytics over evolving schemas
- AI and RAG backends
- Authorization and policy systems
- Internal data platforms
Why It Matters
Stop inheriting database tradeoffs
SQL forces rigid schemas and join complexity. MongoDB pushes integrity and relationships into application code. Elasticsearch adds ETL pipelines and search results can lag behind live data. BI tools sit on top and require manual grids and charts.
Onyx replaces all of these bolt-on compromises with one platform, one data model, one API.
Structured, but rigid. Migrations, schema friction, and relationship-heavy queries devolve into join complexity.
Keep structure while letting relationships evolve without redesigning your schema surface area.
Easy to start, then inconsistent documents and duplication push integrity into app logic.
Structured data stays first-class and flexible—without pushing complexity into application code.
Requires ETL, denormalized indexes, and eventual consistency just to search.
Built-in full-text search on live transactional data—no ETL and search stays consistent.
Dashboards sit on extracts, so you rebuild joins/filters per report and insights lag behind live data.
AI assistants generate queries, visuals, and insights so dashboards come from reusable workflow outputs.
Rethink how database work gets done
Learn how Onyx works, explore the architecture, or dive into the technical model behind resolver-defined relationships and AI-controlled workflows.