What Happened

At its 2025 Annual Conference in Beijing, OceanBase announced and open-sourced seekdb — an AI-native database engineered specifically for hybrid, multimodal AI workloads.

The launch was reported through PR Newswire

This wasn’t a flashy demo.
No viral video.
No chatbot output.

It was something far more foundational: a new kind of database designed for the next generation of AI applications — where vectors, text, events, JSON, embeddings and real-time writes all live together in one system.

Most people will skip this kind of story.
But if you build AI tools, agents, or products that demand retrieval, this is the layer that determines what your application can actually do.

Why This Matters

AI today hits bottlenecks not in model intelligence, but in data fragmentation.

Different formats
Different storage layers
Different indexes
Different access controls
Different retrieval pipelines

Every AI system quickly becomes a patchwork:
vector DB + text search + operational DB + object storage + application cache + RAG middleware.

That patchwork is fragile.
It creates latency.
It creates complexity.
It creates failure modes that models can’t compensate for.

OceanBase suggests a shift:
AI-native data infrastructure — not RAG strapped on top of traditional storage, but storage designed from day one for multimodal AI.

That is a meaningful development.

Inside seekDB

The announcement highlights several capabilities that caught my eye as a builder.

Hybrid Retrieval in One Query

seekdb fuses:

• Vector search

• Full-text search

• Scalar filtering

• JSON/GIS lookups

• Structured queries

…all processed within a single retrieval pipeline.

This matters because hybrid retrieval workflows today require separate systems stitched together through application logic.
seekdb collapses that into one engine.

Millisecond Responses on Billion-scale Data

The system is optimized for billion-scale multimodal datasets while still delivering millisecond-level latency.

For AI agents orchestrating tool calls or knowledge workflows, this is critical.
Latency determines viability.

Transactional and AI-native Together

Unlike many vector DBs that can’t handle operational loads, seekdb sits on OceanBase’s transactional engine with full ACID compliance.
This means:

• Real-time writes

• Indexing as data changes

• Consistent reads

• MySQL compatibility

A rare combination in AI-native contexts.

Lightweight Deployment

The database can run on:

• 1 CPU core

• 2 GB RAM

• pip install startup

• embedded or client/server modes

This makes it suitable for:

• Agents

• Local tools

• Developer workflows

• Small on-prem edge setups

• Prototype-to-production pipelines

Open Source from Day One

Released under Apache 2.0, publicly available on GitHub.
Integrates with:

• HuggingFace

• Dify

• LangChain

• 30+ AI frameworks and MCPs

This gives it a wider surface area than many proprietary AI databases.

The Bigger Shift

The PR Newswire release included two telling datapoints:

1. Gartner projects that by 2028, 74% of all database spending will be tied to generative AI capabilities.

2. MIT Media Lab found that 95% of enterprise GenAI pilots show no measurable return — due to fragmented data, complex pipelines and access-control issues.

This reveals an uncomfortable truth:
Most AI systems don’t fail because the model is weak.
They fail because the data substrate is weak.

seekdb is essentially OceanBase saying:
“We need to redesign the base layer.”

Not the vector index.
Not the embedding pipeline.
The database itself.

If you’ve ever built or deployed a RAG system, you know how painful the fragmentation is.
This is the first mainstream attempt to collapse that complexity.

A Builder’s View

I’ve seen so many AI teams struggle not with AI — but with:

• Indexing

• Freshness

• Multimodal storage

• Latency spikes

• Search inconsistencies

• Distributed retrieval

• Messy JSON fields

• Schema drift

• Lack of ACID guarantees

• Brittle RAG pipelines

Most enterprise use cases don’t break at the model level.
They break in the retrieval layer.

seekdb sits at that exact friction point.

The idea of “as few as three lines of code to build AI apps”, if it holds up in practice, is something that many builders would welcome.
Because AI apps today are too often 20% model code and 80% data plumbing.

An AI-native database that brings everything closer together could meaningfully reduce that overhead.

Where the Opportunity Opens

If AI-native databases become standard, the ecosystem around them expands dramatically.

Founders and engineers should track opportunities in:

• RAG acceleration tools

• Hybrid query optimization

• Multimodal access policy layers

• Vector-text fusion search

• AI observability tied to the DB

• Context-window optimization

• Ingestion pipelines for multimodal data

• Digital twin + DB convergence

• Agent backends

• Real-time data validation

• Storage for simulation-generated embeddings

As companies begin to adopt hybrid DBs, we’ll see demand for:

• Connectors

• Caching layers

• Transformations

• Quality evaluation

• Indexing diagnostics

• Vector hygiene pipelines

This is early infrastructure.
The kind of layer that quietly defines what AI apps can become in 2–5 years.

The Deeper Pattern

Model improvements get the attention.
But database evolution determines the ceiling.

Every AI wave eventually faces the same question:

How do we store and retrieve knowledge fast enough for intelligence to matter?

The old stack — SQL + NoSQL + object storage + search engine + vector DB — breaks under multimodal load.

AI-native databases point toward a different future:

One engine.
One query.
One source of truth.
Structured + unstructured + vector + context metadata — together.

seekdb isn’t the only attempt at this, but it’s one of the first large-scale open-sourced ones with enterprise backing.

That matters.

Closing Reflection

It’s easy to miss stories like this.
They don’t show up in your feed with magical demos.
They don’t produce viral screenshots.
They don’t promise 200,000 context windows or reasoning upgrades.

They sit deeper in the stack.
Quiet but consequential.

Every AI system that works well in the long run shares one trait:
a reliable, unified, low-latency retrieval layer.

seekdb hints at what that layer could look like.

If you’re building AI products today, it’s worth asking:

Is your bottleneck the model… or the data stack beneath it?

Because the next generation of AI capabilities will be unlocked not by bigger models, but by cleaner, faster, AI-native data systems.