Lambda
The superintelligence cloud for GPU compute and AI infrastructure
About Lambda
Lambda is a GPU cloud platform purpose-built for AI training and inference at scale. Founded in 2012 by ML engineers, it provides on-demand and reserved GPU instances, 1-Click Clusters, and single-tenant superclusters powered by NVIDIA B200, H100, and A100 GPUs across 15+ global data centers.
Pros & Cons
Pros
- Highly competitive GPU pricing — H100 at $2.29/hr vs $4.10+ on AWS, saving 40-60% on compute
- Zero egress fees eliminate surprise data transfer costs that can add thousands on other clouds
- InfiniBand networking included at no extra cost for high-performance distributed training
- Single-tenant architecture with SOC 2 Type II provides enterprise-grade security and isolation
- Frictionless setup with SSH keys, pre-installed frameworks, and one-click cluster deployments
- Wide GPU range from V100 to cutting-edge B200 and GB300 covers every budget and workload
Cons
- GPU availability can be limited during peak demand — popular instances frequently sell out
- No serverless or auto-scaling inference offering — you manage your own deployment infrastructure
- Network download speeds reported as slow (~20 MiB/s) which impacts large dataset transfers
- No live chat or phone support — limited to email and ticket-based assistance
- Fewer regions than hyperscalers like AWS or GCP, limiting geographic flexibility
Key Features
1-Click Clusters
Pre-optimized multi-node NVIDIA HGX B200 and H100 clusters that scale from 16 to 2,000+ GPUs for distributed training workloads
GPU Instances
On-demand single and multi-GPU instances with B200, H100, GH200, A100, and A6000 options for rapid prototyping and smaller jobs
Superclusters
Single-tenant NVIDIA GB300 NVL72 clusters with dedicated power, cooling, and physical isolation for frontier AI training
Zero Egress Fees
No charges for data transfer out, saving thousands compared to AWS and other hyperscalers on large dataset workflows
InfiniBand Networking
High-speed InfiniBand interconnect included as standard for low-latency multi-node distributed training
SOC 2 Type II Compliance
Enterprise-grade security with single-tenant, shared-nothing architecture and optional caged-cluster deployments
Pre-Configured AI Stack
One-liner installation of PyTorch, TensorFlow, and other ML frameworks with SSH and JupyterLab access out of the box
Metrics Dashboard
Real-time GPU utilization monitoring and workload metrics without needing to set up external observability tools
Pricing
- 1x to 8x GPU configurations
- B200, H100, GH200, A100, A6000, V100
- SSH and JupyterLab access
- Pre-installed ML frameworks
- Zero egress fees
- 16 to 2,000+ H100 GPUs
- On-demand from $2.29/GPU-hr
- Reserved from $2.19/GPU-hr (3mo-3yr)
- InfiniBand interconnect included
- Managed orchestration
- 16 to 2,000+ B200 GPUs
- On-demand from $3.79/GPU-hr
- Reserved from $3.49/GPU-hr (1yr)
- NVIDIA HGX B200 systems
- Managed orchestration
- NVIDIA GB300 NVL72 clusters
- Single-tenant physical isolation
- Dedicated power and liquid cooling
- Caged-cluster security option
- Co-engineering support
Best For
Large Language Model Training
Train frontier LLMs on 1-Click Clusters with hundreds of H100 or B200 GPUs connected via InfiniBand at a fraction of hyperscaler costs
AI Research & Experimentation
Spin up on-demand GPU instances for rapid prototyping, paper reproduction, and model experiments without long-term commitments
Computer Vision & Diffusion Models
Train and fine-tune image generation, object detection, and video models on A100 or H100 instances with pre-configured PyTorch
Enterprise AI Infrastructure
Deploy single-tenant superclusters with SOC 2 compliance for regulated industries requiring physical isolation and security
Fine-Tuning Open-Source Models
Fine-tune Llama, Mistral, and other open-source models on cost-effective A100 or H100 instances starting at $1.29/hr
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