GPU Marketplace: Shadeform vs Prime Intellect vs Node AI
Finding available GPU capacity at reasonable prices has become a critical challenge for AI teams. While major cloud providers like AWS and Google Cloud offer GPU instances, they’re often at capacity or expensive. GPU marketplace aggregators have emerged as an alternative, connecting users to dozens of providers through a single interface.
See how these platforms work, their pricing models, and when to use them versus going directly to cloud providers.
What is a GPU marketplace?
A GPU marketplace is a platform where users can access graphics processing units (GPUs) for computational tasks such as AI training, inference, rendering, and scientific computing. However, not all GPU marketplaces operate the same way.
GPU marketplaces fall into two categories: direct cloud providers that own their infrastructure, and aggregator platforms that connect you to multiple providers. This guide focuses on aggregators, platforms that function like travel booking sites for GPU capacity, giving you access to dozens of clouds through one interface.
Shadeform
Shadeform is a GPU cloud marketplace founded in 2023 that connects developers with compute resources across more than 20 cloud providers, including Lambda, Nebius, and Crusoe. The platform offers a unified API and console for provisioning GPUs across any provider, eliminating the need to manage multiple accounts and APIs.
Key features:
- Single API access to 20+ cloud providers
- Automated compute brokerage system for getting quotes from 11+ providers in 24 hours
- Real-time pricing and availability data across all providers
- Centralized billing across multiple clouds
- No additional fees; users pay the same price as going directly to providers
- Launch GPUs in ready-to-go cloud accounts managed by Shadeform
Best for: Teams needing immediate GPU access across multiple clouds without the overhead of managing separate accounts and vendor relationships.
Prime Intellect
Prime Intellect operates a compute exchange that aggregates GPU resources from leading providers, with 12 clouds integrated and many more in the pipeline. The platform offers H100s at competitive rates and enables users to access computing resources without long-term contracts.
Key features:
- Unified resource pool from 12+ integrated cloud providers.
- Instant access to up to 8 GPUs on demand, with plans for 16-128+ GPU clusters.
- Integrates resources from major centralized and decentralized GPU vendors, including Akash Network, io.net, Vast.ai, and Lambda Cloud.
- Focus on distributed training frameworks for multi-node training across clusters.
- User-contributed reviews rating the speed and reliability of compute providers.
Best for: AI researchers and teams running distributed training workloads who need transparent provider performance data.
Node AI
Node AI launched its GPU Aggregator in June 2025 as a one-click gateway to global compute, connecting AWS, Azure, Vast AI, GCP, RunPod, and 50+ GPU providers through a single interface.
Key features:
- Real-time selection of best pricing and performance across 50+ providers
- One-click deployment solution
- Enterprise-ready infrastructure for training and inference
- Centralized management console
Best for: Enterprises seeking simplified multi-cloud GPU management with minimal operational overhead.
GPU Marketplace Pricing Models Explained
Understanding pricing models is critical to optimizing your GPU costs. Most marketplaces offer three primary pricing structures:
On-Demand Pricing
Pay-per-use with no long-term commitments. Prices are typically billed per minute or per hour.
Typical costs:
- H100 SXM: $2.25-$8.00/hour depending on provider
- A100 80GB: $1.29-$4.00/hour
- RTX 4090: $0.34-$0.50/hour
Best for: Short-term projects, testing, development, and unpredictable workloads.
Spot/Interruptible Instances
Access spare GPU capacity at 60-90% discounts with the trade-off that instances can be interrupted with 30 seconds to 2 minutes’ notice when providers need capacity back.
Typical savings:
- H100 instances: Up to 85% off on-demand pricing
- A100 instances: 60-75% discounts
- RTX series: 50-70% savings
Best for: Batch processing, model training with checkpointing, non-critical inference, and development environments.
Reserved Capacity
Commit to specific GPU types for 1-3 years in exchange for 40-72% discounts. Some providers require upfront payment.
Typical discounts:
- 1-year commitment: 30-50% savings
- 3-year commitment: 50-72% savings
Best for: Production workloads with predictable, consistent GPU requirements.
Key differences: Providers vs. Gateways
Infrastructure ownership
- Direct providers: Own and operate their data centers, hardware, and networking infrastructure
- Gateways: Don’t own infrastructure; they aggregate capacity from multiple providers
Pricing structure
- Direct providers: Set their own pricing based on hardware costs, overhead, and market positioning
- Gateways: Typically charge no additional fees, with users paying the same as going directly to providers
Account management
- Direct providers: Require individual account setup, quota management, and separate billing
- Gateways: Provide centralized account management and unified billing across all providers
API and integration
- Direct providers: Each has unique APIs, SDKs, and management interfaces
- Gateways: Offer a single, unified API that works across all integrated providers
Flexibility and lock-in
- Direct providers: Can lead to vendor lock-in as infrastructure and workflows become provider-specific
- Gateways: Reduce lock-in by enabling easy switching between providers through the same interface
Support and SLAs
- Direct Providers: Direct relationship with support teams and provider-specific SLAs
- Gateways: May have an additional support layer but ultimately rely on the underlying provider SLAs
Benefits of using GPU marketplaces
1. Simplified Multi-Cloud Management
Aggregators eliminate the need to set up accounts, obtain quotas, and navigate the complexities of multiple providers. Instead of managing credentials across 10+ platforms, you manage them through a single console. This is especially valuable during GPU shortages, when capacity can appear and disappear quickly across providers.
2. Real-Time Price Comparison and Optimization
Compare GPU types, memory sizes, and performance tiers in real time across multiple competing providers. See that an H100 costs $3.20/hour on Provider A but $2.60/hour on Provider B? Deploy to Provider B instantly. Dynamic pricing models enable providers with idle resources to adjust rates, fostering competitive marketplaces that prevent price monopolization.
3. Availability and Capacity Access
By aggregating resources under one roof, these platforms increase your chances of finding available capacity. During peak demand periods, if AWS is out of A100s in us-east-1, your gateway might find capacity on CoreWeave, Lambda, or Vast.ai without you changing a single line of code.
4. Reduced Infrastructure Complexity
Instead of learning multiple cloud platforms’ interfaces and APIs, developers use a single consistent experience regardless of the underlying provider. Your DevOps team doesn’t need to become experts in 15 different cloud platforms; they master one gateway API.
5. Cost Efficiency Through Market Competition
Gateways create transparent marketplaces where providers compete on price and availability. This competition naturally drives prices down compared to monopolistic single-provider scenarios. Some teams report 40-60% cost savings by switching from major cloud providers to GPU gateways.
6. Instant Failover and Redundancy
If a provider experiences downtime or reaches capacity limits, gateways can automatically fail over to alternative providers. This geographical and vendor diversity creates a more resilient AI infrastructure.
GPU Availability and Scarcity
The GPU market faces significant supply constraints, especially for high-demand chips like the NVIDIA H100 and H200. GPU shortages make it difficult and expensive to get GPUs on major cloud providers, which is why aggregators have become essential infrastructure.
Key factors affecting availability:
- AI boom demand: The explosion in generative AI and large language model training has created high GPU demand
- Limited manufacturing capacity: NVIDIA’s production can’t keep pace with global demand
- Data center buildout lag: New facilities take 18-24 months to come online
- Geographic concentration: Most GPU capacity is concentrated in the US and European data centers
GPU marketplace gateways help navigate scarcity by giving you visibility into capacity across dozens of providers simultaneously. When major clouds are sold out, smaller regional providers often have availability.
Challenges
Dependency on Underlying Providers
Service quality and reliability ultimately depend on the underlying provider infrastructure. A gateway can’t fix fundamental issues with a provider’s hardware or networking.
Abstraction Limitations
Gateways may not support all provider-specific features. If you need specialized AWS services like SageMaker or GCP’s TPUs, you’ll need direct provider access.
Market Fragmentation
Limited standardization means that no established spot markets or futures contracts exist yet. Pricing transparency varies between gateways, and not all providers are available on all platforms.
Performance Variability
Different providers have different network topologies, storage configurations, and interconnect options. An H100 on Provider A might perform differently from an H100 on Provider B for multi-node training due to network differences.
Security and Compliance Considerations
When using GPU marketplace gateways, security depends on both the gateway operator and the underlying providers. Most gateways implement:
- Data encryption: End-to-end encryption for data in transit and at rest
- Access controls: Role-based access control (RBAC) and API key management
- Compliance certifications: SOC 2, ISO 27001, and GDPR compliance, where available
- Network isolation: Private networking options and VPC support
For enterprises with strict data sovereignty requirements, verify that your gateway supports selecting specific geographic regions and providers that meet your compliance needs.
Choosing the Right Approach for Your Workload
Choose Direct GPU Cloud Providers When:
- You need deep integration with provider-specific services (e.g., AWS SageMaker, GCP Vertex AI)
- Enterprise support and strict SLAs are critical for production workloads
- You’re building on provider-native tools and services that aren’t abstracted by gateways
- Compliance requires specific data center certifications or audit trails
- You prefer direct vendor relationships for procurement and support
- Your workload requires specialized hardware configurations only available from particular providers
Choose GPU Marketplace Gateways When:
- You need flexibility across multiple providers to avoid capacity constraints
- Price optimization is a priority, and you want to leverage market competition
- You want to avoid vendor lock-in and maintain infrastructure portability
- Simplified management across clouds is important for your DevOps team
- You need quick access to available capacity across the global market
- Your team is small and can’t dedicate resources to managing multiple cloud relationships
- You’re running experimental or research workloads where flexibility matters more than provider-specific features
FAQs
Further reading
- Top 30 Cloud GPU Providers & Their GPUs
- Top 20+ AI Chip Makers: NVIDIA & Its Competitors
- Multi-GPU Benchmark: B200 vs H200 vs H100 vs MI300X
- GPU Concurrency Benchmark: H100 vs H200 vs B200 vs MI300X
If you need help finding a vendor or have any questions, feel free to contact us:
Find the Right VendorsCem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
Be the first to comment
Your email address will not be published. All fields are required.