Lambda vs FluidStack
A detailed comparison to help you choose between Lambda and FluidStack.
Quick Verdict
3.7/5
Lambda
0 reviews
3.6/5
FluidStack
0 reviews
Lambda is rated higher (3.7 vs 3.6). Lambda is a GPU cloud for training and running AI models, offering on-demand NVIDIA GPU instances and reserved clusters for AI teams and researchers. FluidStack aggregates GPU capacity from multiple providers to offer H100, A100, and consumer GPU instances. Pay-as-you-go and reserved capacity. EU and US datacenters with competitive pricing.
Lambda On-demand GPU cloud for ML training and inference | FluidStack Enterprise GPU cloud at competitive pricing | |
|---|---|---|
| Overview | ||
| Rating | 3.7 (0 reviews)✓ | 3.6 (0 reviews) |
| Pricing model | usage-based | usage-based |
| Starting price | From $0.69/mo✓ | From $1.3/mo |
| Best for | ML researchers and engineering teams optimizing cost per GPU-hour for training, fine-tuning, and batch inference who can tolerate best-effort capacity. | AI labs and platform teams committing to 8-10k+ GPUs for training or heavy inference who want managed Slurm/Kubernetes, per-GPU-hour transparency, and a hands-on infrastructure partner |
| Specifications (entry plan) | ||
| CPU cores | 14 vCPU | — |
| RAM | 46 GB | — |
| Storage | — | — |
| Bandwidth | — | — |
| SLA uptime | — | — |
| Data-center count | 14 | — |
| €/vCPU/mo | €0.05 | — |
| €/GB RAM/mo | €0.01 | — |
| Features | ||
| IPv6 | ✓ | |
| DDoS protection | ✓ | |
| Automated backups | ||
| Snapshots | ||
| Managed option | ||
| Bare metal | ||
| GPU available | ||
| S3-compatible | ||
| Hourly billing | ||
| Free tier | ||
| Data-center locations | ||
| Regions | [US x10 regions, Tokyo, Osaka, India, Germany, Israel] | Canada, Netherlands, Norway, Sweden |
| Tags | ||
| Tags | hourly billinggpu availableus datacenterapi access | hourly billinggpu availableeu datacenterus datacenter |
| Visit Lambda → | Visit FluidStack → | |
Lambda
Pros
- + Published per-minute GPU pricing with no egress fees
- + Preinstalled ML stack (Lambda Stack) on all images
- + Full cloud API for instance automation
- + Free egress keeps large checkpoint downloads cheap
- + Cluster tier scales from 16 to 2,000+ GPUs for distributed training
Cons
- - No published uptime SLA for on-demand instances
- - Popular H100 configurations sell out regularly
- - One European region (Germany); no Latin America or Australia
- - No managed databases, snapshots, or backup products
- - Filesystem billing continues after instance deletion
FluidStack
Pros
- + Published per-GPU-hour rate card with no egress or ingress fees
- + Fully managed Slurm and Kubernetes on bare metal with 24/7 engineering support
- + Scales from 8 GPUs on-demand to 10k+ reserved — cluster sizes most clouds cannot match quickly
- + EU capacity in the Netherlands and Nordics for data-residency-sensitive training
- + Engineering response times measured in hours by SemiAnalysis during ClusterMAX testing
Cons
- - Aggregated third-party capacity means hardware and day-one polish vary by deployment
- - Contract-first commercial model — 8-GPU on-demand floor, 256-GPU reserved floor, no free tier
- - No public uptime SLA, datacenter list, or documentation library
- - Reactive software supply chain security — unpatched NVIDIA container toolkit found at cluster handover in evaluation
- - Track record is short relative to the scale of its recent customer commitments
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