High-Performance Compute for Enterprise AI
Reserved NVIDIA B300, B200, H200, H100, and A100 GPU capacity for enterprise AI workloads. Run training, inference, and production AI workloads with confidential computing and flexible infrastructure.
Trusted by leading AI teams
Built for enterprise AI at scale
Reserved GPU Capacity
Guaranteed capacity for your term
Reserved B300, B200, H200, H100, and A100 — committed for your term, bare metal available, SLA-backed. No multi-year lock-in.
Confidential Computing
Hardware-level data protection
Protect sensitive AI workloads with hardware-based Trusted Execution Environments (TEEs), keeping data, model inputs, outputs, and model weights protected.
InfiniBand GPU Networking
High-bandwidth interconnect
Deploy high-bandwidth GPU-to-GPU interconnect for multi-node distributed training, including NCCL workloads.
Bare Metal GPU Access
Full hardware control
Dedicated physical GPU servers with no virtualization layer — full hardware control, near-native performance, and no shared tenancy. Available under reserved arrangements.
Dedicated Enterprise Support
Direct access to the technical team
Direct access to the technical team for AI infrastructure onboarding, architecture guidance, deployment support, and defined response times.
Transparent Pricing
No hidden fees
Simple, upfront pricing for GPU compute with no hidden fees or unexpected infrastructure costs. No egress fees.
Results from teams running production on Akash
Razer
$0.01 per image · 3.24s avg response · 15× lower inference costProduction AI image generation for the AVA Mini campaign — running across distributed GPU infrastructure at commodity pricing.
Venice
H100 GPUs · strict data retention · no prompt or output loggingVenice runs its image generation models on H100 GPUs rented through Akash. Built around user privacy, with no prompt or output retention — dedicated GPU capacity with strict data retention policies is a structural requirement.
"The future of AI isn't just better models, it's efficient infrastructure. With Akash Network, we extend into a decentralised cloud to scale efficiently."
QUYEN QUACH
Vice President of Software, Razer
Astria
Docker startup dropped from 40–60 min to secondsAstria runs production image generation on Akash GPUs, fine-tuning on their own product shots and generating on-brand campaign assets. After more than a year in production, Docker image startup dropped from as long as 40–60 minutes on a prior provider to seconds on Akash.
Passage
50% lower GPU cost vs. reserved AWS pricingPassage powers immersive virtual events for brands and creators with high-performance GPUs at a fraction of hyperscaler pricing.
"Akash gave us exactly what we needed: high-performance GPUs at 50% lower cost... Even if we were to reserve GPUs on AWS for an entire year, we would not see the same cost savings as Akash provides."
AREL AVELLINO
CEO of Passage
FAQs
No. You run open models on infrastructure you control, so your prompts, outputs, and proprietary data never enter a vendor's training loop. Unlike commercial LLM APIs, whose terms often allow training on your content, nothing you send leaves your boundary.
Uptime commitments are set per engagement. Akash works with you to define the availability target your workload requires, then matches you with providers and a deployment architecture that can meet it. Reserved capacity agreements can include contractual uptime terms defined in the SLA.
By invoice, in USD. Terms are set per customer, with standard net terms as the default.
Any containerized workload. Deploy open models such as GLM, Kimi, Qwen, and DeepSeek (or your own models) using standard Docker, with any framework, language, or runtime. Common workloads include inference, training and fine-tuning, RAG, rendering, and batch compute.
Enterprise customers are matched with SOC 2 compliant providers. HIPAA and other certifications are handled case by case, since requirements vary by industry and workload. Bring your compliance requirements to the conversation and Akash will scope provider selection, deployment architecture, and controls against them.
Yes. Akash supports multi-GPU and multi-node workloads, including distributed AI training and inference. InfiniBand networking and NCCL can be used for high-performance GPU-to-GPU communication where supported by the deployment configuration.
GPU capacity is available across multiple regions through Akash's global provider network. Enterprise customers can discuss preferred regions, GPU types, capacity requirements, and deployment locations with the Akash team.
Yes. Teams can validate on the open marketplace at on-demand GPU pricing first, then move steady workloads to reserved capacity. The console.akash.network is self-serve; no contract is needed to evaluate.
Talk to an enterprise specialist.
Scope capacity, terms, and onboarding for your workloads. We typically respond within 3 business days.