Multi-tenant Object Store for Neoclouds

Akave gives neoclouds S3-compatible object storage with tenant isolation, zero egress, customer-controlled keys, tamper-evident auditable integrity, and flexible deployment options. So you can offer AI customers a storage layer built for data mobility, cost predictability, and trust.

Try Risk-free Trial

Speak With a Storage Expert

Neoclouds are becoming AI infra platforms.

We built the storage to match.

Neoclouds are no longer just selling GPU capacity. They are hosting enterprise AI workloads across many customers, each with different requirements for data isolation, key control, auditability, retention, and deletion.

Traditional object storage was not built to make tenant isolation easy to prove. Customers need dedicated endpoints, controlled key boundaries, auditable data histories, and confidence that sensitive AI datasets, checkpoints, logs, and outputs remain governed across the full lifecycle.

Akave gives neoclouds S3-compatible storage for multi-tenant AI infrastructure: tenant-owned endpoints, customer-controlled key architectures, tamper-evident audit records, zero egress, and flexible deployment across hosted, hybrid, and customer environments.

Akave Object Store

Persistent AI Data Demands
Verifiability and Sovereignty

Observability
Logs

Why it matters:

Compliance: e.g. NIST AI RMF and SOC2 auditable inference records

Agent traces for debugging

Extend data retention

How Akave fit:

CID prove every log untampered

Immutable ledgers

WORM, object lock

Model
Checkpoints

Why it matters:

Fine-tuning lineage

Reproducibility

Large objects, versioned

How Akave fit:

Content addressed - CID

Versioning + metadata

Generative
AI Data

Why it matters:

RAG contains sensitive data

Outputs carry IP and liability

How Akave fit:

Deploy at the edge or hybrid

BYOK /HYOK*

AI Data Storage Built for Neoclouds

Tenant-Isolated

Give every customer a separated storage control plane with dedicated endpoints, policies, and key boundaries. Akave helps neoclouds support multi-tenant AI workloads without forcing customers into shared, opaque storage environments.

AI-Ready

Persistent storage for training data, model checkpoints, logs, embeddings, inference outputs, and customer datasets — built to support the full AI lifecycle beyond GPU compute.

Mobility-First

Zero-egress, S3-compatible storage that lets customers move data across GPU clusters, analytics tools, backup systems, and customer environments without data movement penalties.

Auditable by Design 

Tamper-evident audit records, content addressing, and cryptographic integrity attestation helps enterprise AI teams retain, review, and trust critical datasets, model inputs, and outputs.

Trusted by Major Customers

"The partnership with Akave/Snowflake is a game-changer for our customers. By leveraging this modern, high-performance architecture, we've removed friction and accelerated our Core Storage for AI training. This means Intuizi can now deliver the actionable insights our clients need to power their segmentation, measurement, and AI initiatives with greater value."

Ron Donaire

CEO, Intuizi

“Our mission has always been to make healthcare data both accessible and accountable. With Akave, we finally have a storage layer that matches that vision — verifiable by design, s3-compatible, programmable for access, and resilient across every partner and environment we work with.”

Boka

CTO, Asterisk Women's Health

“We’ve covered countless stories about decentralization and now we’re putting that ethos into practice. Storing our archives on Filecoin ensures that The Defiant’s reporting will remain accessible, no matter what happens.”

Cami Russo

Founder, The Defiant

“We essentially stream-execute the query directly against the file stored in Akave, which is pretty cool.”

Alfonso de la Rocha

CTO, Baselight

“Performant edge AI systems need fast, secure and verifiable file storage. Our work with Akave expands the 375ai network's ability to deliver high quality real world data and at dramatically lower costs.”

Trevor Branon

CEO, 375ai

Save up to ~ 80%
and keep AI data moving

AI workloads should not be trapped by storage costs.

Akave helps neoclouds cut storage and retrieval costs by up to ~80% with zero egress fees and no access/API fees. Customers can store training data, move it to GPU clusters, retain model checkpoints, analyze outputs, and bring data back to their own environments without surprise cloud bills.

Explore Pricing

See How It Works

Resources

Storage for AI

The Neocloud Storage Problem: Why GPU-Rich Clouds Still Need a Persistent Object Store

Moving 50TB from AWS S3 to a neocloud costs $4,300 in egress before your first GPU fires. Neoclouds solved compute—not persistent, S3-compatible storage. Akave Cloud sits as a neutral layer: $14.99/TB, zero egress, native Iceberg, and on-chain audit trails. For 100TB with 50TB monthly egress, that's $1,499/month vs $6,620 on AWS—77% savings.

Storage for AI

Sovereign AI

Verifiable AI Factories: Pairing NVIDIA Confidential Computing with a Sovereign Storage Substrate

GPU-layer confidential computing (NVIDIA H100/H200/Blackwell) attests the enclave computation. It does not prove data provenance. A verifiable AI factory requires five storage properties: (1) ingestion-time cryptographic proof, (2) integrity anchored on a separate immutable ledger, (3) protocol-layer geofencing, (4) tamper-detection guarantees verifiable without the provider, (5) retrieval verification closing the compute-attestation loop.

Storage for AI

The 2026 Data Infrastructure Landscape: 7 Trends Reshaping Enterprise Storage

2026 data infrastructure is being reshaped by AI workloads, regulation, sovereign data requirements, and cost pressure. Traditional cloud models are no longer sufficient for scale, compliance, or AI governance.

Storage for AI

Your AI Factory Is Confidential. Your Training Data Isn't.

Confidential computing using trusted execution environments secures AI computation, but it does not verify the integrity of training data before it enters the enclave. This post examines the gap between compute-side attestation and data-side provenance in AI factory architectures, drawing on recent research into training data poisoning and supply chain attacks. It explains how content-addressed, write-time verification closes the loop that TEEs cannot.