Cloud GPU Pricing Shifts in Q1 2026

Pricing Comparison: H100 Instances Across Providers (February 2026)

Provider	Instance Type	GPUs	On-Demand ($/hr)	1yr Reserved ($/hr)	Spot Median ($/hr)
AWS	p5.48xlarge	8x H100 SXM	$80.62	$52.40 (35% off)	$33.86 (58% off)
GCP	a3-highgpu-8g	8x H100	$78.17	$47.68 (39% off)	$31.27 (60% off)
Azure	ND H100 v5	8x H100	$80.88	$51.77 (36% off)	$35.49 (56% off)
CoreWeave	H100-80GB-SXM	8x H100	$22.76*	$16.56 (27% off)	N/A
Lambda	gpu_8x_h100_sxm5	8x H100	$19.92*	$15.14 (24% off)	N/A

Per-GPU pricing multiplied by 8 for comparison. CoreWeave/Lambda price per GPU: $2.85 and $2.49 respectively.

Optimizing Your Procurement Mix

Baseline load (60-70% of capacity): Reserved instances. Identify your steady-state GPU utilization over the past 90 days. That floor is your reserved commitment target. At 35-40% off on-demand, the breakeven on a 1-year commitment is reached in under 4 months — anything beyond that is pure savings. GCP currently offers the deepest reserved discount (39%) on a3-highgpu-8g.

Burst and batch (15-25% of capacity): Spot instances. Configure your orchestrator for multi-region spot with automatic failover. Key settings for Karpenter:

# Karpenter NodePool for GPU spot instances
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: gpu-spot-h100
spec:
  template:
    spec:
      requirements:
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["spot"]
        - key: node.kubernetes.io/instance-type
          operator: In
          values: ["p5.48xlarge"]
      nodeClassRef:
        group: karpenter.k8s.aws
        kind: EC2NodeClass
        name: gpu-spot
  disruption:
    consolidationPolicy: WhenEmpty
    expireAfter: 4h
  limits:
    cpu: "768"
    nvidia.com/gpu: "64"

Emergency headroom (10-20% of capacity): On-demand. Keep a small on-demand allocation for traffic spikes and spot preemption recovery. This is your insurance policy. Over-provisioning here by 5% is cheaper than the latency spike from scrambling for capacity during a preemption cascade.

A100 as budget tier. If your models fit in 80GB and you do not need the H100’s higher memory bandwidth (3.35 TB/s vs 2.0 TB/s), A100 instances are now priced at $4.30-5.10/GPU/hr on-demand — 40-50% cheaper than H100. For inference on models under 30B parameters, the cost-per-token difference is marginal.

Winners and Losers

Winners:

• Mid-scale inference operators running 100-500 GPUs. The 15-20% on-demand cut plus optimized procurement can reduce their annual GPU bill by $1-4M without any migration or architectural changes. This is pure operational leverage.
• Spot-native ML platforms (Anyscale, Modal, SkyPilot users). The widening gap between spot and on-demand rewards teams that invested in preemption-tolerant infrastructure. Their effective cost basis just dropped to $4.00-4.50/GPU/hr.
• A100 holdouts. Teams that delayed the H100 migration are now validated. A100 clearance pricing makes the 80GB SKU the best price-performance option for inference workloads under 30B parameters. No migration needed.

Losers:

• Teams locked into H100 reserved instances from mid-2025. Those contracts were priced at Q3 2025 rates — 20-30% above current market. Depending on the provider, there may be limited options to renegotiate. Azure allows reserved instance exchanges; AWS does not for Savings Plans already purchased.
• Tier-2 providers competing purely on price. CoreWeave and Lambda’s value proposition was “H100s cheaper than hyperscalers.” With the hyperscaler price cuts, the gap has narrowed from 40% to 15-20%. Their remaining edge is availability and bare-metal access, not cost.
• On-demand-only teams. Any organization still running production GPU workloads entirely on on-demand pricing is now paying a 35-60% premium over an optimized mix. At scale, this is the difference between a viable unit economics model and one that does not close.