Cloudian Wants to Be the Backbone of Your AI Stack—Here’s How
Enterprise AI just got a lot less complicated.
Cloudian, known for its ultra-scalable object storage, is now fusing that storage power with AI inferencing capabilities—all in one platform. The result? A unified solution that eliminates the traditional bottlenecks between storing vast vector datasets and running real-time AI workloads like recommendations, NLP, and RAG (retrieval-augmented generation).
This isn’t just a performance bump—it’s a structural rethink of how enterprise AI infrastructure should operate.
Storage Meets Inferencing, No Middleman Required
With its flagship HyperStore platform now supporting integrated vector database functionality via Milvus, Cloudian is directly addressing the thorniest problem in modern AI: managing massive, petabyte-scale datasets while keeping inference response times snappy.
Normally, AI teams must bridge two worlds: fast-access, low-latency compute environments for inferencing and high-capacity storage for vector embeddings, indexes, and logs. Moving data back and forth between them slows everything down. Cloudian’s new approach collapses that distance—literally.
“We’re fundamentally shifting how enterprises think about AI infrastructure,” said Cloudian CTO Neil Stobart. “Instead of siloed systems, we’re offering a unified platform that’s both faster and easier to deploy.”
Built for Billion-Scale Vectors
The platform integrates Milvus, the open-source vector database known for its ability to store, index, and search high-dimensional vector embeddings with millisecond latency—even at billion-vector scale. That makes it ideal for inference-heavy workloads in:
- Recommendation engines
- Computer vision
- Natural language processing
- RAG (Retrieval-Augmented Generation)
HyperStore’s 35GB/s read throughput per node ensures those vectors don’t just sit pretty—they move fast. That’s key for enterprises needing real-time decision-making and inference feedback.
Scalable, Performant, and Surprisingly Simple
Here’s what Cloudian is really selling: simplicity without sacrificing scale.
The combined platform supports exabyte-scale storage needs but keeps operations lean by removing the overhead of stitching together separate AI and storage stacks. It’s also S3-compatible, meaning enterprises can plug it into existing AI pipelines without refactoring their entire toolchain.
It works on-prem or in hybrid cloud setups, offering flexibility for organizations juggling AI workloads across different environments.
Why This Matters Now
As AI moves out of the lab and into enterprise production, infrastructure complexity is becoming a showstopper. Most teams cobble together AI pipelines with disparate tools—some optimized for storage, others for inference. That leads to latency, inefficiency, and rising costs.
Cloudian is among the first to marry high-performance object storage with native inferencing in a single deployable unit. That’s more than a tech upgrade—it’s a strategic pivot toward the AI Data Platform (AIDP) model, where storage, compute, and AI converge into a unified ecosystem.
Other vendors are moving in this direction—think Snowflake acquiring Neeva or Databricks integrating vector search—but few are building from the storage layer up, as Cloudian is.
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