Finding the right GPU cloud provider can make or break your AI workflows in 2025. While Runpod remains popular, alternatives like Paperspace, Google Compute Engine, CoreWeave, NVIDIA vGPU and AWS UltraClusters are competing hard with unique features.
In our latest article below, we compare the top 5 Runpod alternatives.
Runpod Alternatives : A Quick Comparison
| Provider | Best For | GPU Options | Pricing (Starting) | Key Limitation |
| Runpod | AI workloads with serverless GPUs | A10, A100, RTX 4090 | ~$0.20/hr (A10) | Smaller global reach |
| Paperspace | Startups & devs needing quick setup | T4, V100, A100 | $0.078/hr (T4) | Limited scalability, fewer regions |
| Google GCE | Enterprises needing flexibility | T4, V100, A100, L4 | $0.070/hr (CPU VM) | Complex pricing, regional limits |
| CoreWeave | AI training & VFX | A100, H100, RTX Series | $2.46/hr (A100) | NVIDIA-only, service issues |
| NVIDIA vGPU | Enterprises with remote teams | vGPU-enabled NVIDIA cards | $20/CCU + $5/yr support | Hardware + license costs |
| AWS UltraClusters | Large-scale LLM & HPC workloads | A100, H100 | $40.97/hr (8×A100 instance) | Expensive & complex to manage |
1. Paperspace
Paperspace is a developer-friendly GPU cloud platform widely used by startups, researchers, and small teams who need powerful compute without the complexity of enterprise cloud services. It offers both virtual machines and managed workflows, with Gradient, its integrated MLOps platform, making it easy to train, manage and deploy ML models. The platform is designed to minimise setup time, so you can spin up environments in just minutes.
Its web-based console is intuitive, offering pre-configured environments for machine learning and AI experimentation. This makes Paperspace ideal for data scientists and engineers who value speed and accessibility.
Why Choose Paperspace
Here’s why you should choose Paperspace :
- VM-based GPU instances for training and inference.
- Gradient MLOps platform for workflows and automation.
- Intuitive web console with pre-built environments.
- Support for popular ML frameworks (PyTorch, TensorFlow, etc.).
- API access for automation and integration.
Limitations
Despite its great features, Paperspace has a smaller global data centre footprint compared to hyperscalers like AWS or Google. This can introduce latency for distributed teams. While powerful, it may not scale as effectively for very large enterprise deployments.
Pricing
Paperspace follows a pay-as-you-go billing model. GPU instances start at $0.078/hour for NVIDIA T4s, going up to $3.09/hour for A100 GPUs. Gradient services incur additional costs depending on usage.
2. Google Compute Engine (GCE)
Google Compute Engine is Google Cloud’s highly flexible VM service, widely recognised for its customizable machine types and global reach. It’s a strong fit for enterprises that want to integrate AI workloads into a broader cloud ecosystem. With access to different VM configurations, ranging from general-purpose to memory-optimised and compute-optimised. You can finely tune deployments for cost and performance.
What makes GCE stand out is its seamless integration with other Google services, from BigQuery to AI APIs, helping teams to run complex pipelines across multiple products. Its sustained-use discounts automatically reduce costs for long-running workloads.
Why Choose Google Compute Engine
Here’s why you should choose Google Compute Engine :
- Customisable VM machine types (standard, high-memory, high-CPU, GPU-optimised).
- Global network infrastructure with low-latency deployments.
- Automatic sustained-use discounts for cost savings.
- Integration with Google Cloud services (BigQuery, Vertex AI, etc.).
- Per-second billing with a one-minute minimum.
Limitations
The pricing model can be complex and intimidating for new users. Some GPU models and VM types aren’t available in every region, creating limitations for global scaling. The steep learning curve can be a barrier for smaller teams.
Pricing
Uses per-second billing. A baseline VM with 1 vCPU and 3.75 GB RAM starts at $0.070/hour, with GPU costs added on top, depending on type and region.
3. CoreWeave
CoreWeave is a cloud provider built specifically for compute-heavy industries such as AI research, visual effects, and scientific computing. Unlike traditional providers, it emphasises raw performance and specialised GPU infrastructure, with a lineup that includes NVIDIA H100s for next-gen workloads.
One standout feature is its managed Kubernetes service, which allows organisations to deploy, scale, and manage applications more easily. Combined with high-performance storage solutions and a growing data centre footprint across North America and Europe, CoreWeave is designed to deliver low-latency, high-throughput performance for demanding workloads.
Why Choose CoreWeave
Here’s why you should choose CoreWeave :
- Access to diverse NVIDIA GPUs, including H100 and A100.
- Bare-metal and VM configurations for flexibility.
- Managed Kubernetes service for containerised workloads.
- High-performance storage with ultra-fast access speeds.
- Data centres in North America and Europe.
Limitations
CoreWeave’s NVIDIA-only ecosystem may limit flexibility for users who want AMD or other hardware.
Pricing
An NVIDIA A100 PCIe GPU costs about $2.46/hour on-demand. Discounts of up to 60% are available via reserved capacity.
4. NVIDIA Virtual GPU (vGPU)
NVIDIA vGPU is a virtualisation technology that brings GPU acceleration into shared environments. It enables IT teams to allocate GPU resources fractionally, giving multiple users access to powerful compute without each requiring a dedicated GPU. This makes it especially useful in industries like engineering, design, media and healthcare, where professionals need workstation-grade performance delivered remotely. The platform also supports live migration, ensuring workloads continue running during server maintenance.
Why Choose NVIDIA Virtual GPU (vGPU)
Here’s why you should choose NVIDIA Virtual GPU (vGPU) :
- Fractional GPU allocation for multiple VMs.
- Multi-GPU VM support for heavy workloads.
- Integration with leading virtualisation stacks (VMware, Citrix, etc.).
- Live migration for continuous uptime during updates.
- Centralised security with no data exposure on endpoints.
Limitations
The platform requires certified servers and compatible NVIDIA GPUs, which means higher upfront costs. Licensing can also be complex and costly.
Pricing
Licenses start around $20 per concurrent user (CCU), plus a $5 annual support fee per CCU. Contracts typically require a 4-year upfront commitment, totalling about $40 per CCU.
5. Amazon EC2 UltraClusters
AWS EC2 UltraClusters are designed for workloads like LLM training, HPC simulations and generative AI development. By connecting thousands of GPU instances within a single availability zone, UltraClusters deliver on-demand supercomputing power without the need for organisations to build physical infrastructure.
These clusters feature Elastic Fabric Adapter (EFA) networking with speeds up to 400 Gbps, ensuring high-throughput inter-instance communication. Paired with Amazon’s FSx for Lustre storage, UltraClusters eliminate bottlenecks and enable massive-scale workloads to run efficiently.
Why Choose Amazon EC2 UltraClusters
Here’s why you should choose Amazon EC2 UltraClusters :
- Scale from dozens to thousands of NVIDIA A100 or H100 GPUs.
- Elastic Fabric Adapter networking (up to 400 Gbps).
- Fully managed FSx for Lustre high-performance storage.
- Tight integration with AWS AI and ML ecosystem.
- On-demand access to supercomputing capacity.
Limitations
UltraClusters are expensive and often overkill for smaller projects. Setup and management can be highly complex, requiring deep AWS expertise and specialised distributed computing skills.
Pricing
The pricing varied by instance type. A p4de.24xlarge instance with 8 NVIDIA A100s costs around $40.97/hour. Newer p5 instances with H100 GPUs provide up to 4x more performance but at a higher hourly cost.
What Alternative Should You Choose?
The best Paperspace alternative depends on your goals. If cost-efficiency matters most, prioritise providers with flexible pricing and discount options. For heavy AI training or inference, go for A100 or H100 GPUs with NVLink/InfiniBand. Startups may benefit from quick deployment tools, while enterprises should consider SLAs, uptime and premium support.
FAQs
1. Why switch from Paperspace?
Switching ensures better pricing transparency, wider GPU variety, faster networking and reliable support tailored for AI workloads.
2. Which GPUs should I prioritise?
For lightweight tasks, use RTX cards; for training large AI models, you must opt for A100 or H100 GPUs with NVLink.
3. Are Paperspace alternatives more affordable?
Yes. Many providers offer per-second billing, spot instances and long-term discounts to optimise costs for startups and enterprises.
4. What matters most in a provider?
Transparent pricing, GPU variety, scalability, deployment simplicity and reliable support ensure long-term efficiency and productivity for workloads.
5. How reliable are these GPU cloud providers?
Top alternatives offer strong SLAs, global data centres and responsive support to ensure high uptime for critical AI workloads.