Why People Are Looking for Paperspace Alternatives
Paperspace was once the go-to GPU cloud for researchers, ML engineers, and AI developers. It offered a clean interface, Jupyter notebooks, and reasonable pricing. Then DigitalOcean acquired it in 2023, and everything changed.
The acquisition brought confusion. Paperspace's standalone product is being sunset and absorbed into DigitalOcean's Gradient GPU Droplets. Existing users faced migrations, feature changes, and pricing restructures. The platform now requires a $39/month Growth subscription just to access high-end GPUs like A100s and H100s. That's on top of hourly compute costs.
The math stops working quickly. An A100 on Paperspace runs $3.09/hour on-demand (or $1.15/hour with a 36-month commitment). Add the monthly subscription, factor in GPU availability issues, and suddenly the platform that promised simplicity feels expensive and fragmented.
This is why teams are switching. They want better pricing, more GPU options, clearer billing, and platforms built specifically for GPU workloads instead of bolted-on features. The alternatives have gotten much better over the past two years.
Quick Comparison Table
| Provider | Best A100 Price | Best H100 Price | Min Commitment | Billing Model | Strength |
|---|---|---|---|---|---|
| Spheron | $0.76/hr | $1.33/hr | None | Pay-as-you-go | Best overall value |
| RunPod | $1.19/hr | $1.99/hr | None | Pay-as-you-go | Beginner-friendly UI |
| Lambda | $1.29/hr | $2.49/hr | None | Reserved instances | Reliable on-demand |
| Vast.ai | $1.10/hr | ~$1.87/hr | None | Marketplace | Lowest H100 price |
| CoreWeave | $1.80/hr | $3.20/hr | None | Monthly billing | Enterprise features |
| TensorDock | $1.20/hr | $1.90/hr | None | Pay-as-you-go | Developer tools |
| Nebius | $1.00/hr | $2.00/hr | None | Pay-as-you-go | Good availability |
| Modal | N/A | N/A | None | Usage-based | Serverless compute |
| Thunder Compute | $0.66/hr | N/A | None | Pay-as-you-go | Lowest A100 price |
| DataCrunch | $0.95/hr | $1.99/hr | None | Pay-as-you-go | Simple interface |
| Paperspace | $1.15/hr | N/A | 36 months | Reserved instances | Integrated ecosystem |
1. Spheron: Best Overall Paperspace Alternative
Spheron is a decentralized GPU cloud platform that competes directly with Paperspace but with fundamentally better pricing. It aggregates GPU compute from multiple data centers and lets you rent GPUs without vendor lock-in.
What they do well: Spheron's pricing is hard to beat. H100 SXM GPUs start at $1.33/hour. A100 80GB models are $0.76/hour. RTX 4090s go for $0.55/hour. These aren't promotional rates; they're the standard prices. There's no subscription fee. No commit required. You pay by the minute and stop paying when you turn off the machine. The platform offers full VM access, so you can install whatever you want, from JupyterLab to production ML serving frameworks.
The interface is clean without being oversimplified. You can spin up instances with custom configurations, connect via SSH, mount persistent storage, and manage everything through a reasonable dashboard. Spheron also publishes its pricing transparently, so there are no hidden costs or tier requirements unlocking better GPUs.
Where they fall short: Spheron is decentralized, which means it relies on a network of providers. During peak demand, you might not find the exact GPU configuration you want. The customer support is growing but not as established as Lambda or DigitalOcean. If you need guaranteed on-demand availability for critical workloads, you might need to pay a premium or choose a more traditional provider.
Best for: Teams doing research, training, or development who need serious GPU power without the markup. Anyone leaving Paperspace because of the Growth subscription requirement. Researchers with flexible timelines who can work around availability fluctuations.
Pricing: Visit Spheron's pricing page for current rates. A100 80GB at $0.76/hr is roughly 75% cheaper than Paperspace on-demand and 34% cheaper than Paperspace's best reserved pricing without any long-term commitment.
2. RunPod: Best for Developers and Teams
RunPod sits between simplicity and power. It's easier to use than Lambda or Vast.ai but offers more flexibility than Paperspace's managed notebooks.
What they do well: RunPod's killer feature is its templating system. You can spin up pre-configured environments for Common workloads. PyTorch, TensorFlow, Stable Diffusion, LLM fine-tuning, and more come pre-installed. If you don't want to manage dependencies, just pick a template and go. The community is active and shares custom templates constantly.
Pricing on community cloud instances is aggressive. H100s run $1.99/hour, A100s at $1.19/hour. You also get access to secure cloud instances if you need guaranteed availability. The UI is modern and responsive. GPU selection is deep, from consumer cards to enterprise options.
Where they fall short: Community cloud instances are less reliable than reserved options. Your instance might be terminated if demand spikes. The platform is less transparent about pricing variations between data centers. For production workloads, you pay more for guaranteed resources.
Best for: ML engineers building models, fine-tuning LLMs, or deploying Stable Diffusion. Teams that want pre-built environments and don't want to manage infrastructure details. Solo developers with flexible compute timelines.
Pricing: Community cloud H100s at $1.99/hr are slightly more expensive than Spheron but more reliable. A100s at $1.19/hr are in line with Lambda.
3. Lambda: Best for Predictable Workloads
Lambda focuses on on-demand GPU availability without marketplace complexity. You won't find the absolute lowest prices here, but availability is consistent.
What they do well: Lambda's reserved instances offer genuine savings if you can commit to a month or longer. An A100 80GB costs $1.29/hour with no long-term lock-in. Their cluster management tools are mature. If you're running distributed training or large inference jobs, Lambda's API and orchestration tools work well.
The pricing is straightforward. One price per GPU, same across all data centers. No surprise price variations. The platform handles large workloads smoothly, and their support team responds to questions about complex deployments.
Where they fall short: Spot pricing is limited. If you want maximum savings with marketplace pricing, Vast.ai is better. Lambda's UI is functional but not as polished as Paperspace's was. The platform doesn't include managed notebooks, so you're bringing your own development environment.
Best for: Teams running production inference servers or large training jobs. Anyone who values pricing predictability over absolute minimum cost. Companies needing support contracts and SLAs.
Pricing: A100 reserved instances at $1.29/hour. H100s at $2.49/hour. Both solid alternatives to Paperspace's pricing, especially at scale.
4. Vast.ai: Best for Lowest Spot Pricing
Vast.ai operates a marketplace where individual GPU providers list their hardware. This creates competition that drives prices down dramatically.
What they do well: You'll find the lowest GPU prices anywhere on Vast.ai. H100s listed from as low as $1.87/hour. A100s from $1.10/hour. Some RTX 4090s for under $0.30/hour. This is because individual providers are competing to fill their hardware. The platform handles pricing negotiation and instance management.
You get complete SSH access and can install anything. Storage options are flexible. The web UI is functional for browsing, filtering, and launching instances.
Where they fall short: This is a marketplace, so quality varies. A $0.20/hour RTX 4090 might have unreliable providers, network latency, or occasional downtime. You need to read provider reviews and understand that the cheapest option isn't always the best option. Customer support is community-driven, so responses are slower than traditional providers.
Best for: Cost-optimized research and development. Workloads that can tolerate occasional interruptions. Teams with experience managing distributed compute and provider variability.
Pricing: H100s from $1.87/hour on average. A100s from $1.10/hour. You'll always find cheaper options than Paperspace, but with some caveats about reliability.
5. CoreWeave: Best for Enterprise Reliability
CoreWeave specializes in large-scale GPU infrastructure for production AI workloads.
What they do well: CoreWeave operates multiple data centers with redundancy and enterprise SLAs. Their pricing is higher than Spheron or RunPod, but you get 99.9% uptime guarantees, priority support, and integration with Kubernetes. A100s run $1.80/hour. H100s at $3.20/hour.
If you're running revenue-generating AI services, CoreWeave's reliability is worth the premium. They handle large distributed jobs well and integrate with enterprise infrastructure.
Where they fall short: Price premium over alternatives for the same hardware. Minimum GPU requirements and billing thresholds. Less suitable for hobbyist projects or small-scale research.
Best for: Production AI services, inference servers, and large-scale training. Companies needing uptime guarantees and enterprise support. Anyone concerned about infrastructure stability over raw cost savings.
Pricing: Enterprise pricing with higher costs than alternatives. See CoreWeave alternatives for a detailed comparison.
6. TensorDock: Best for Developers with Infrastructure Needs
TensorDock combines GPU cloud compute with developer-focused tools like integrated Jupyter, volume management, and multi-GPU coordination.
What they do well: You get managed Jupyter notebooks similar to Paperspace, but with better GPU selection and lower costs. A100s are $1.20/hour. H100s at $1.90/hour. The platform handles multi-GPU communication efficiently, which matters for distributed training. Persistent storage is included and easy to manage.
The community is helpful and the platform feels designed by engineers for engineers. Instance templates are available for common frameworks.
Where they fall short: Smaller platform means less brand recognition and community support than RunPod. Feature set is narrower than CoreWeave or Lambda.
Best for: ML researchers using Jupyter who want lower costs than Paperspace. Teams building custom training pipelines who appreciate developer-first tools.
Pricing: A100 80GB at $1.20/hour is a middle ground between budget options and enterprise platforms.
7. Nebius: Best for High Availability and Pricing
Nebius is a Russian-backed cloud provider offering competitive GPU pricing with multiple data centers.
What they do well: A100 pricing starts at $1.00/hour. H100s at $2.00/hour. These are reasonable prices with no surprise fees. The platform has redundancy and consistent uptime. Support is available in multiple languages. Infrastructure is designed for both research and production workloads.
Where they fall short: Less brand recognition in Western markets. Customer support is good but not as established as Lambda or CoreWeave. Some features are less polished than competitors.
Best for: Developers outside the US looking for stable pricing. Teams already familiar with international cloud providers. Workloads where geographic location in Eastern Europe is acceptable.
Pricing: A100 at $1.00/hour is competitive with Spheron and cheaper than most other alternatives.
8. Modal: Best for Serverless GPU Workloads
Modal takes a different approach. Instead of renting VMs, you define functions and Modal handles the infrastructure.
What they do well: No infrastructure management. Write Python functions, decorate them with GPU requirements, and Modal handles spinning up, scaling, and tearing down hardware. You pay only for actual compute time. This is genuinely useful for applications with variable demand, webhooks, scheduled jobs, or API endpoints.
Modal's pricing is usage-based with per-second billing. If your workload doesn't need continuous GPU access, you save money.
Where they fall short: If you need to rent a single GPU for eight hours of straight development work, Modal is worse than VM-based providers. The serverless model has overhead that makes continuous, long-running workloads less cost-effective. Not suitable for interactive development like Jupyter notebooks.
Best for: Inference endpoints for applications. Scheduled batch jobs. Variable-demand applications. Anyone tired of managing server infrastructure.
Pricing: Usage-based. No fixed hourly cost. Better for sporadic compute than continuous training.
9. Thunder Compute: Best for Budget A100 Training
Thunder Compute operates a smaller network but offers aggressive A100 pricing.
What they do well: A100 80GB GPUs start at $0.66/hour, undercutting almost everyone. The platform is straightforward for experienced users. Full SSH access and persistent storage. Simple billing with no hidden fees.
Where they fall short: Smaller provider means less brand name recognition and potentially less consistent support. GPU selection is limited compared to Spheron or RunPod. During peak demand, availability can be tight.
Best for: Budget-conscious teams doing A100 training with flexible timelines. Researchers who can wait for availability. Organizations doing cost comparisons with hard time constraints.
Pricing: A100 at $0.66/hour is the cheapest A100 option listed, beating Spheron by about $0.10/hour.
10. DataCrunch: Best for Simplicity Without Complexity
DataCrunch is a Czech-based provider focused on straightforward GPU rental without enterprise overhead.
What they do well: Simple pricing. A100s at $0.95/hour. H100s at $1.99/hour. No subscription fees, no hidden costs. The interface is intuitive. Instance setup is fast. Support is responsive for basic questions.
Where they fall short: Fewer data center locations means less geographic diversity. Smaller community means fewer shared resources and templates. Not ideal for teams needing enterprise support or SLAs.
Best for: Individual developers and small teams wanting straightforward GPU rental. Anyone overwhelmed by marketplace options or complex platforms. Quick prototyping and small-scale training jobs.
Pricing: A100 at $0.95/hour and H100 at $1.99/hour are solid middle-ground options.
What to Look for in a Paperspace Alternative
Choosing the right GPU provider involves more than just comparing hourly rates. Here's what to evaluate.
Pricing Transparency: Can you see all costs upfront, or are there hidden fees? Paperspace's $39/month Growth subscription was a hidden cost many users resented. Good alternatives show base pricing, then tell you about add-ons. The best providers don't have hidden tiers. This is crucial for GPU cost optimization.
Commitment Requirements: Paperspace now requires 12-month commitments for some tiers. Every good alternative on this list requires zero long-term commitment. You pay as you go. This matters because GPU prices are falling and technology is changing fast.
GPU Availability: Check how many GPUs are listed and what the wait times are. Paperspace's limited selection drove many users away. Spheron and RunPod have deep inventories. Vast.ai's marketplace has even more options but with variability.
Development Environment: Do you need Jupyter notebooks, or are you comfortable with SSH and command-line tools? Paperspace excelled at managed notebooks. Most alternatives give you full VM access, letting you install your preferred tools. This is actually more flexible, but requires a bit more setup.
Support and Community: How responsive is customer support? How active is the community? Lambda and CoreWeave have good support. RunPod and TensorDock have active communities. Modal has good documentation. Vast.ai is community-driven.
Performance and Reliability: For production workloads, you need reliability. CoreWeave offers SLAs. Lambda has consistent availability. Vast.ai and decentralized options like Spheron are cheaper but with some variability. Check our GPU cloud benchmarks for performance comparisons.
Regional Availability: Where do you want your GPUs running? Some providers have global data centers. Others focus on specific regions. This affects latency and potentially pricing.
Integration and Tooling: Do you need Kubernetes integration, managed notebooks, or orchestration tools? CoreWeave and Lambda have mature infrastructure tools. TensorDock and Spheron have developer-friendly features. RunPod has templates.
The Paperspace Equation That No Longer Works
Paperspace made sense at $0.50/hour for an A100. The notebook interface was slick. The ecosystem was cohesive. You paid a fair price for a great product.
Then DigitalOcean bought it and introduced the $39/month Growth subscription for high-end GPUs. Suddenly, the math changed. An A100 that cost $3.09/hour in on-demand pricing plus $39/month in subscription fees adds $1.82/hour if you use it 40 hours a month. The effective hourly cost jumped to $4.91/hour just for the privilege of accessing decent GPUs.
Compare that to Spheron's $0.76/hour for the same A100 with zero subscription fees. You're looking at a 85% price difference.
Even Paperspace's reserved pricing at $1.15/hour for a 36-month commitment doesn't make sense. You're locked in for three years while GPU technology improves and prices fall. The industry is moving toward cheaper, more abundant compute, not longer commitments. For more context, see our guide on top cloud GPU providers.
The Real Reason Teams Are Switching
Yes, price matters. A team saving 75% on GPU costs will absolutely switch providers. But that's not the only reason people are leaving Paperspace.
They're switching because DigitalOcean's transition created doubt. When your provider gets acquired, you start wondering when the next change will come. Will the UI change again? Will prices go up further? Will the product get deprecated? Switching costs feel lower than staying put.
They're switching because the requirement for a Growth subscription feels hostile. It's the right product at the right price, but the packaging feels extractive. They're switching to platforms where they understand the cost structure without needing a decoder ring.
They're switching because the alternatives got really good. Spheron, RunPod, Lambda, and others have invested in their platforms. They've built better tools, deeper GPU inventories, and clearer pricing. What was a small inconvenience a year ago is now an obvious alternative.
Making the Switch
If you're currently on Paperspace, moving to an alternative is easier than you think.
Most of your code will run unchanged. Python dependencies, notebooks, training scripts, all of it ports directly. The main learning curve is the new platform's interface and SSH setup if you're used to managed notebooks.
Start with a test project. Spin up a small instance on your chosen alternative, run your training code, and see how it feels. Most platforms offer credits for new users, so you can try before committing.
If you're using Paperspace's notebook interface heavily, look at RunPod or TensorDock first. They offer managed notebooks. If you're comfortable with SSH and terminal access, Spheron or Lambda give you more control and better pricing.
Document your setup. Write down what you install, what dependencies you need, and what your typical workflow looks like. This makes switching future providers faster and prevents vendor lock-in.
Conclusion
Paperspace's acquisition by DigitalOcean was a turning point. For a platform that built its reputation on simplicity, the transition introduced complexity and higher costs. The Growth subscription requirement, pricing changes, and brand confusion have driven users to look elsewhere.
The good news is that alternatives are abundant and genuinely better. Spheron offers the best overall value with no subscription fees and 75% lower pricing than Paperspace's on-demand A100s. RunPod prioritizes ease of use and beginner-friendly features. Lambda provides reliability and predictable pricing. Vast.ai enables marketplace competition that drives prices lower. Each alternative brings different strengths.
The best choice depends on your specific needs, but any of these platforms will save you money compared to Paperspace while giving you more control and fewer long-term commitments. You've outgrown Paperspace. These alternatives are built for where you're heading next.
Ready to migrate? Check out Spheron's GPU rental options or explore RunPod alternatives if you want even broader comparisons. For more GPU options, see our guide on renting NVIDIA A100 GPUs and renting NVIDIA H100 GPUs. Your GPUs are waiting, and they're cheaper than you think.