7 Best AWS GPU Alternatives (2026)
AWS GPU instances are expensive. A single A10G costs $1.21/hr, an A100 runs $3.67/hr, and don't even think about H100s without a serious budget. Here are 7 alternatives that deliver the same GPU performance at 50-85% lower cost.
Why Leave AWS for GPU Workloads?
AWS is excellent for general cloud computing, but for GPU-specific workloads, it has several pain points:
- •Price premium: AWS GPUs cost 2-5x more than dedicated GPU clouds
- •Hidden fees: Egress ($0.09/GB), EBS storage, NAT Gateway, Elastic IP — adds 30-50% to your bill
- •Complexity: IAM roles, VPC, security groups, ASG — hours of setup before your first inference
- •No consumer GPUs: The RTX 4090 outperforms AWS A10G at inference, but AWS doesn't offer it
- •GPU capacity limits: Popular instances frequently unavailable in desired regions
The 7 Best AWS GPU Alternatives
1. VectorLay
Distributed GPU inference — 70-85% cheaper than AWS
VectorLay offers the most dramatic savings compared to AWS. By distributing inference across consumer and enterprise GPUs via an overlay network, VectorLay delivers fault-tolerant inference at a fraction of AWS pricing. No IAM roles, no VPC configuration, no egress fees.
- 70-85% cheaper than AWS GPUs
- Built-in auto-failover
- No hidden fees (egress, EBS, NAT)
- Deploy in minutes, not hours
- Consumer GPU access
- —No AWS service integration
- —Inference-focused (not for training)
2. RunPod
Cloud built for AI — GPU pods + serverless
RunPod is the most popular dedicated GPU cloud alternative to AWS. Offers both persistent GPU pods and serverless endpoints with per-second billing. Much simpler than SageMaker for inference deployment.
- Serverless GPU endpoints
- Template library
- 50-65% cheaper than AWS
- Active community
- —No AWS ecosystem integration
- —Smaller feature set than SageMaker
3. Lambda Labs
GPU cloud for AI — simple and competitive
Lambda offers straightforward GPU cloud access with SSH. Their A100 pricing ($1.29/hr) significantly undercuts AWS ($3.67/hr). Good for teams that want bare-metal GPU access without cloud platform overhead.
- Very competitive A100 pricing
- Simple SSH access
- InfiniBand clusters
- —No consumer GPUs
- —Capacity often constrained
- —Limited managed services
4. Google Cloud
ML-native cloud with Vertex AI and TPUs
If you're leaving AWS but still want a hyperscaler, GCP offers Vertex AI for managed ML and unique TPU access. GPU pricing is similar to AWS, but TPUs can be more cost-effective for certain training workloads.
- TPU access (unique)
- Vertex AI platform
- BigQuery ML integration
- —Similar GPU pricing to AWS
- —Quota approval delays
- —Complex billing
5. Vast.ai
GPU rental marketplace — variable pricing
Vast.ai operates a marketplace where GPU owners set prices. During off-peak times, you can find RTX 4090s for as low as $0.30/hr. But pricing and availability fluctuate, and reliability depends on individual GPU providers.
- Can be extremely cheap
- Wide GPU variety
- Marketplace flexibility
- —Unreliable nodes
- —Variable pricing
- —No fault tolerance
6. CoreWeave
Kubernetes-native GPU cloud
CoreWeave is the closest enterprise alternative to AWS for GPU workloads. Kubernetes-based, with InfiniBand networking and competitive H100 pricing. Strong for teams that want Kubernetes but at lower prices than AWS.
- Kubernetes-native
- Competitive enterprise pricing
- InfiniBand
- —Requires Kubernetes expertise
- —Enterprise-focused
- —Minimum commitments
7. TensorDock
Affordable GPU marketplace
TensorDock offers some of the cheapest GPU instances available — H100s at $2.25/hr. Minimal platform overhead. Good for cost-conscious teams that don't need managed services.
- Very cheap H100 pricing
- Simple interface
- No frills
- —Minimal documentation
- —No content or tutorials
- —Basic feature set
Cost Comparison: AWS vs Alternatives
Scenario: Running 2× 24GB GPUs 24/7 for inference
Save $12,432/year by switching from AWS to VectorLay
Frequently Asked Questions
What is the best alternative to AWS GPU instances?
VectorLay is the best AWS GPU alternative for cost-sensitive inference. It's 55-80% cheaper with built-in fault tolerance and no hidden fees. For budget research, Vast.ai offers the lowest raw prices. For enterprise Kubernetes, CoreWeave provides competitive data center GPU pricing.
How much can I save by switching from AWS GPU to VectorLay?
Typical savings are 55-80% depending on the GPU. An RTX 4090 on VectorLay ($0.49/hr) delivers comparable inference performance to AWS's A10G ($1.21/hr) for most models under 24GB VRAM. Factor in AWS egress, EBS, and NAT gateway costs, and savings can exceed 80%.
Do AWS GPU alternatives have the same compliance certifications?
Most alternatives do not match AWS's comprehensive compliance portfolio (HIPAA, FedRAMP, SOC 2, ISO 27001). If you need these certifications, AWS or GCP may be necessary. VectorLay provides strong workload isolation via Kata Containers but doesn't yet offer formal compliance certifications.
Can I migrate from AWS SageMaker to a cheaper GPU provider?
Yes. If your SageMaker model is containerized (which most are), you can export the Docker image and deploy on VectorLay or other providers. You'll lose SageMaker's managed features (A/B testing, model monitoring), but save 55-80% on compute costs.
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