Magic Data
Where Magic Data
fits the AI-Native Cloud
Five layers. One integrated stack. Magic Data closes the comprehension gap between data and agent-ready intelligence.
DigitalOcean's AI-Native Cloud is built end-to-end for the inference and agentic era. It has five integrated layers from silicon to agent, with no cross-vendor hops and no egress between them.
But today's DO "Data & Learning" layer — PostgreSQL with pgvector, Valkey, Knowledge Bases, and real-time data capabilities — solves retrieval. It doesn't yet solve understanding.
As VAST Data's leadership demonstrated at Deploy 2026, the next frontier isn't storing data or even querying it — it's building continuous AI systems where data is processed where it lives.
DigitalOcean customers' data architectures are sprawling, multi-engine, and opaque. Before any agent can reason over them, someone has to map the topology and make it legible to inference. That last mile is exactly what Magic Data was built to close.
Magic Data can automatically traverse and map the topology of a customer's data architecture across their Managed Databases, Spaces buckets, and connected sources. It produces a structured, machine-readable understanding of how that data is organized, related, and queryable. This pairs directly with DigitalOcean's DBaaS offerings and becomes the connective tissue between raw storage and agent-ready retrieval.
Rather than requiring customers to hand-craft their Knowledge Bases from scratch, Magic Data could generate a set of pre-vectorized embeddings and deposit them directly into DigitalOcean Managed PostgreSQL with pgvector or Managed OpenSearch. The output would be agent-ready out of the box: structured, embedded, and aligned to how Plano and the Managed Agents layer expect to consume context.
This directly extends the value of DigitalOcean's Memory Layer — bridging the gap between episodic memory (what the agent just did) and semantic memory (what the customer's data environment actually means).
Magic Data currently ships production-grade, always-on data mining agents with native visualizations. This is already fully containerized and deployable on DigitalOcean Droplets today. They run continuously, surface insights from live data, and render findings in visual dashboards. This is exactly the kind of real-traffic, real-cost-constraint workload that Deploy 2026 was built around.
They can be replatformed to run inside DigitalOcean's Managed Agents harness with secure sandboxes, durable state, and orchestration via Plano — turning the standalone product into a native offering inside the five-layer stack.