HyperScale AI Data Platform

Cloudian HyperScale AI Data Platform is an on-premises AI infrastructure appliance designed for enterprises that need to extract actionable intelligence from unstructured data while maintaining full data sovereignty.

Reviewed by 7wData

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Publisher review

Cloudian HyperScale AI Data Platform is an on-premises AI infrastructure appliance designed for enterprises that need to extract actionable intelligence from unstructured data while maintaining full data sovereignty. It targets organizations in regulated industries—such as financial services, healthcare, and government—where sending sensitive data to public cloud AI services is not permissible. The platform bundles Nvidia RTX PRO 6000 Blackwell GPUs, Cloudian’s HyperStore object storage, and a pre-integrated AI software stack to eliminate the manual data preparation that typically consumes 80% of data science effort. It is positioned as a turnkey solution for sovereign AI, allowing companies to build retrieval-augmented generation (RAG) workflows and vector search applications entirely on premises.

The platform automatically ingests, embeds, and indexes multimodal unstructured content—including documents, images, videos, and audio files—using a built-in vector database. It supports the S3 storage protocol over RDMA, which accelerates object storage I/O by bypassing the kernel and reducing latency for GPU data feeds. The embedded Llama-3.2-3B-Instruct model handles dialogue and reasoning tasks, while the system enables near real-time access to enterprise data without requiring users to manually transform or label files. Cloudian claims the platform can unlock the 80-90% of enterprise data that is unstructured, a category that only 18% of organizations currently leverage effectively.

Cloudian competes directly with other on-premises AI data platforms such as Dell PowerScale with Nvidia DGX, NetApp AIPod, and Pure Storage AIRI. Unlike those offerings, which often require separate procurement of storage, compute, and AI software, Cloudian delivers a single-vendor appliance with pre-validated integration. However, Cloudian lacks the broad ecosystem partnerships and enterprise sales reach of Dell or NetApp. The platform’s reliance on Nvidia RTX PRO 6000 Blackwell GPUs ties its performance to Nvidia’s hardware roadmap, and the absence of a public cloud tier limits flexibility for hybrid deployments.

The primary trade-off is hardware lock-in: the platform requires Nvidia RTX PRO 6000 Blackwell GPUs, which are expensive and may be overkill for smaller datasets. Setup without extensive AI expertise is complex, as the system demands familiarity with vector databases, embedding pipelines, and RAG architectures. Pricing is not publicly disclosed, but the appliance model means upfront capital expenditure rather than consumption-based pricing. Organizations that already run Kubernetes or have existing GPU clusters may find the integrated appliance redundant, while those seeking a fully managed cloud AI service will need to look elsewhere.

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How it works

  1. Nvidia Blackwell GPU support

    Integrates Nvidia RTX PRO 6000 Blackwell GPUs for on-premises AI inference and embedding workloads.

  2. S3 over RDMA storage

    Uses the S3 storage protocol over RDMA to reduce object storage I/O latency for GPU data feeds.

  3. Automatic data transformation

    Ingests, embeds, and indexes multimodal unstructured content without manual data preparation.

  4. Built-in vector database

    Stores and indexes embeddings for unstructured data, enabling vector search and RAG workflows.

  5. Llama-3.2-3B-Instruct model

    Employs the Llama-3.2-3B-Instruct model for dialogue, reasoning, and retrieval-augmented generation tasks.

  6. Near real-time data access

    Provides low-latency access to enterprise data for AI applications without manual transformation.

  7. Secure on-premises control

    Maintains full data sovereignty with reduced overhead compared to public cloud AI services.

Strengths and trade-offs

Strengths

  • Automatically transforms 80-90% of enterprise unstructured data into indexed, queryable intelligence without manual labeling.
  • Eliminates manual data preparation processes that typically consume 80% of data science effort in AI projects.
  • Maintains full data sovereignty on premises, critical for regulated industries like healthcare and finance.
  • Provides near real-time access to enterprise data via a built-in vector database and S3 over RDMA storage.

Trade-offs

  • Requires Nvidia RTX PRO 6000 Blackwell GPUs, which are expensive and may not be cost-effective for smaller datasets.
  • Complex to set up without extensive AI expertise in vector databases, embedding pipelines, and RAG architectures.
  • Pricing is not publicly disclosed, making budget comparison with public cloud AI services difficult.
  • No public cloud tier exists, limiting hybrid deployment flexibility for organizations that want to burst to the cloud.

Pricing context

Not publicly disclosed; sold as an on-premises appliance with upfront hardware and software costs.

Getting started with HyperScale AI Data Platform

  1. Order the appliance

    Contact Cloudian sales to purchase the HyperScale AI Data Platform appliance. Specify your storage capacity and GPU requirements. The appliance includes pre-integrated hardware and software, so no separate procurement is needed.

  2. Connect data sources

    Configure the appliance to ingest your unstructured data by pointing it to your on-premises file shares or S3-compatible storage. The system automatically ingests documents, images, videos, and audio files without manual transformation.

  3. Configure the vector database

    Set up the built-in vector database to store embeddings for your data. Define indexing parameters and embedding models. The platform uses Llama-3.2-3B-Instruct for reasoning and dialogue tasks.

  4. Run a RAG query

    Submit a natural language query to the platform using the provided interface. The system retrieves relevant context from the vector database and generates a response using the embedded LLM. Verify the output is accurate and timely.

  5. Schedule data refresh

    Set up automated ingestion pipelines to periodically scan your data sources for new or updated files. Configure the frequency and scope of indexing to keep the vector database current without manual intervention.

Frequently Asked Questions

What is the Cloudian HyperScale AI Data Platform?

It is an on-premises AI infrastructure appliance that bundles Nvidia RTX PRO 6000 Blackwell GPUs, Cloudian HyperStore object storage, and a pre-integrated AI software stack. It helps enterprises extract intelligence from unstructured data while keeping data on premises for sovereignty.

How does the HyperScale AI platform handle unstructured data automatically?

It ingests, embeds, and indexes multimodal content like documents, images, and videos without manual preparation. A built-in vector database stores embeddings for vector search and RAG workflows, reducing the data science effort that typically consumes 80% of project time.

What industries benefit from an on-premises AI appliance like HyperScale?

Regulated industries such as financial services, healthcare, and government benefit because they cannot send sensitive data to public cloud AI services. The platform ensures full data sovereignty while enabling AI workloads like retrieval-augmented generation and vector search entirely on premises.

What are the main competitors to the Cloudian HyperScale AI Data Platform?

Competitors include Dell PowerScale with Nvidia DGX, NetApp AIPod, and Pure Storage AIRI. Cloudian differentiates by offering a single-vendor appliance with pre-validated integration, avoiding separate procurement of storage, compute, and AI software.

What are the trade-offs of using the HyperScale AI platform?

The platform requires expensive Nvidia RTX PRO 6000 Blackwell GPUs, which may be overkill for smaller datasets. Setup demands expertise in vector databases and RAG architectures. There is no public cloud tier, limiting hybrid deployment flexibility.

How does S3 over RDMA improve performance in the HyperScale platform?

S3 over RDMA accelerates object storage I/O by bypassing the kernel, reducing latency for GPU data feeds. This enables faster data access for AI inference and embedding workloads, supporting near real-time enterprise data queries.

Alternatives

How HyperScale AI Data Platform compares

Direct head-to-head against 3 competitors. Picked by 7wData.

This tool

HyperScale AI Data Platform

Pricing
Not publicly disclosed; sold as an on-premises appliance with upfront hardware and software costs.
Target
Cloudian HyperScale AI Data Platform is an on-premises AI infrastructure appliance designed for enterprises that need to extract actionable intelligence from unstructured data while maintaining
Strength
Automatically transforms 80-90% of enterprise unstructured data into indexed, queryable intelligence without manual labeling.
Watch for
Requires Nvidia RTX PRO 6000 Blackwell GPUs, which are expensive and may not be cost-effective for smaller datasets.

Cloudian HyperScale AIDP

Pricing
Custom/Contact sales; based on capacity and support tier.
Target
Enterprises needing on-prem S3-compatible object storage for AI data lakes.
Deployment
On-premises, hybrid cloud.
Strength
S3-compatible object storage with strong data protection and ransomware defense.
Watch for
Pricing not transparent; requires sales engagement for quote.

GMI Cloud

Pricing
H100 GPU at $2.10/hour, H200 at $2.50/hour; pay-as-you-go.
Target
Startups and teams needing cost-effective, on-demand GPU compute for AI training.
Deployment
Cloud (bare metal, containers).
Strength
40-60% cost savings vs hyperscale clouds with instant GPU provisioning.
Watch for
Limited to GPU compute; no integrated data management or storage.

Lambda Labs

Pricing
H100 GPU from $2.49/hour; pre-configured ML environments.
Target
ML engineers and researchers needing pre-configured GPU clusters.
Deployment
Cloud, on-premises.
Strength
Pre-configured ML environments reduce setup time for deep learning.
Watch for
Higher per-hour pricing than specialized providers; limited storage features.

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Sources

Reporting on this tool draws on these publicly available sources.

  1. cloudian.com
  2. cloudian.com
  3. www.blocksandfiles.com
  4. siliconangle.com