Paperspace (now part of DigitalOcean) is a popular cloud GPU platform known for its user-friendly UI and wide selection of GPU instances for ML and graphics workflows​. It offers both virtual machines (Core) and managed AI tools (Gradient) to over 500,000 users worldwide​.

However, teams may seek alternatives due to factors like cost, limited data center locations (only 3 globally)​, GPU availability constraints (quotas on high-end GPUs during peak demand)​, or the need for features and performance beyond Paperspace’s current ecosystem.

High-performance AI projects can quickly outgrow Paperspace’s offerings, prompting a search for platforms with more flexible pricing, newer GPU models, better scalability, or richer AI-centric tooling.

In this blog post, we will discuss the best paper space alternatives, so let’s get started without further ado.

What to Look for in a Paperspace Alternative?

When evaluating alternatives, key criteria include:

• Pricing and Flexibility – Transparent pay-as-you-go pricing (per-minute or per-second billing) and discounts for longer commitments or spot instances to optimize costs.
• GPU Model Support – Access to a broad range of GPUs (from budget RTX cards to cutting-edge NVIDIA A100/H100) to match workload needs and harness latest performance improvements.
• Scalability – Ability to scale up to multi-GPU clusters or down to serverless jobs, with features like auto-scaling, GPU pooling, or cloud clusters for large training runs.
• Performance – High-speed interconnects (e.g. NVLink, InfiniBand), fast NVMe storage, and robust networking (10–300 Gbps) for throughput-intensive tasks and distributed training.
• Serverless Workflows – Support for serverless GPU endpoints or job-based workflows that let you run inference or batch jobs without managing full VMs, enabling efficient pipeline integration.
• Developer Experience – Easy-to-use consoles or APIs, one-click Jupyter notebooks, and pre-configured environments for AI frameworks (TensorFlow, PyTorch, etc.) to accelerate development.
• Global Infrastructure – Multiple data center regions to minimize latency and improve availability, especially if your team or customers are geographically distributed.
• Ecosystem & Integration – Additional AI-centric services like managed model training, MLOps integrations, or compatibility with tools (Terraform, Kubernetes, etc.) for seamless workflow integration.
• Support & Reliability – Responsive support (especially critical for startups without large DevOps teams), clear SLAs, and enterprise-grade security/compliance if needed for production deployments.
• Community & Usage Model – For some platforms, a community marketplace or peer-to-peer rental model can drastically cut costs, albeit with trade-offs in support or consistency; consider if this fits your use case.

With these points in mind, here are 10 top alternatives to Paperspace for GPU computing in 2025, ranked with an eye toward AI/ML teams and creative professionals.

Best Paperspace Alternatives

1. Runpod.io

RunPod’s interface emphasizes simplicity for launching AI workloads​.

Runpod has emerged as a leading Paperspace alternative by offering on-demand GPUs with a developer-friendly platform.

It focuses on quick deployment and cost efficiency, making it ideal for rapid experiments and production inference alike.

Runpod is ideal for budget-conscious ML practitioners or indie creators who need flexible GPU access without long-term commitments​.

Its serverless endpoints and community-provided GPUs make it great for short workloads, demos, and scaling out inference on demand.

Runpod.io Key Features:

• Serverless Model Serving: Automatically provisions GPUs to run models via API calls, eliminating manual infrastructure management.
  replicate.com
• Automatic GPU Management: Dynamically scales and allocates resources behind the scenes, letting you focus solely on inference tasks.
  replicate.com
• User-Friendly Interface & API: Provides a streamlined web portal and REST API for rapid deployment and integration of AI models.
  replicate.com
• Diverse Model Library: Offers access to a wide variety of pre-trained models for on-demand inference across multiple domains.
  replicate.com
• Real-Time Monitoring: Delivers live metrics and logging for tracking inference performance and usage efficiency.
  replicate.com

Runpod.io Limitations:

• No Direct GPU Provisioning: Users cannot manually select GPU types; the hardware layer is entirely abstracted.
  replicate.com
  Limited Customization: Advanced configurations are restricted compared to raw GPU providers, reducing control for power users.
  replicate.com
• Variable Pricing Transparency: Inference costs vary by model usage, making budgeting challenging for unpredictable workloads.

Runpod.io Pricing

• Prices start around $0.22/hour for an RTX A4000 GPU on-demand​.
• Even high-end GPUs are affordable – e.g. an A100 40GB is about $1.19/hour​.

2. Lambda Labs (Lambda GPU Cloud)

‍Via Lambda Labs (Lambda GPU Cloud)

Lambda Labs offers a GPU cloud focused on AI research and deep learning, making it a strong contender for those transitioning from Paperspace.

Lambda’s cloud is ideal for ML teams who need high-end NVIDIA GPUs with minimal setup, and prefer a lean platform without the complexity of a hyperscaler​.

It’s a go-to for training large models (vision or NLP) on reliable hardware at lower cost, and even supports hybrid cloud + on-prem setups (Lambda sells GPU workstations too).

Lambda Key Features:

• AI-focused stack: Purpose-built for deep learning; launch PyTorch or TensorFlow instances in minutes without navigating extra cloud complexity.
• Latest NVIDIA GPUs: Offers NVIDIA A100 (40GB/80GB), H100, RTX 6000/A6000, and V100, with high-memory multi-GPU configurations for large-scale training.
• High-performance networking: Supports ultra-fast interconnects for multi-GPU jobs, with clusters reaching up to 400 Gbps speeds.
• No egress fees: Data transfers incur no extra charges, ideal for data-heavy workloads moving in and out.
• Hybrid flexibility: Provides both cloud and on-prem hardware options, ensuring consistent environments across deployments.

Lambda Limitations:

• Limited regions: Operates from only two U.S. data centers (San Francisco and Allen, TX), which may lead to higher latency internationally.
• Fewer managed services: Focuses solely on raw compute power without integrated databases, serverless functions, or advanced analytics tools.
• Scaling constraints: Suitable for moderate workloads; very large-scale demands might encounter capacity limits compared to hyperscalers.

Lambda Pricing:

• Aggressive rates: An NVIDIA H100 80GB instance is about $2.49 per hour, while an A100 40GB starts around $1.10 per hour—significantly lower than major cloud providers.
• Simple billing: Charged hourly with per-second granularity, with no upfront commitment or hidden fees; volume discounts are available for enterprise reservations.

3. Vast.ai

‍Via Vast.ai

Vast.ai’s homepage highlights its status as a global GPU market, promising 5–6× cost savings through its decentralized rental model.

Vast.ai takes a completely different approach: it’s a marketplace for GPU power. Individuals or data centers rent out their idle GPUs, and users bid on them – yielding some of the lowest cloud GPU prices in the industry.

What It's Best For? Maximum cost savings for flexible workloads.

If you can tolerate occasional variability and minimal support, Vast lets you harness GPUs for training, rendering, or inference at rock-bottom prices. It’s popular for hobbyist AI projects, student experiments, and even startup prototyping where cost trumps guaranteed uptime.

Vast.ai Key Features:

• Real-time bidding marketplace: Offers both fixed-price on-demand instances and cheaper interruptible instances via bidding, enabling significant savings.
• Extensive GPU variety: Provides a wide range from older GTX/RTX cards to top-tier datacenter GPUs such as RTX 3090, 4090, Tesla V100, and A100.
• Custom environments: Utilizes Docker containers for custom images, with a community image library available or the option to bring your own.
• Transparent pricing and performance stats: Displays host specs, historical reliability, bandwidth, and pricing (e.g., an RTX 3090 at $0.16/hr with 99% reliability) to help select the right instance.
• Global availability: Hosts are located worldwide, allowing users to choose hardware in regions that minimize latency.

Vast.ai Limitations:

• Reliability varies: Renting from individual providers means uptime isn’t guaranteed; instances can go down if a provider reboots or quits, and migration isn’t instantaneous.
• Minimal support and ecosystem: As a barebones marketplace and scheduler, it offers limited customer support and lacks additional management tools, requiring self-sufficiency for instance issues and data persistence.

Vast.ai Pricing:

• Ultra-low hourly rates: Consumer GPUs like the RTX 3090 rent for about $0.16/hr, RTX 4090 around $0.24–$0.35/hr, and even powerful cards like NVIDIA A40 (48GB) for around $0.28/hr—far below the ~$2–$3/hr rates of major clouds.
• Interruptible discounts: Opting for interruptible instances can save 50% or more off the on-demand rate; billing is per minute with no commitments, ideal for short-term, batch processing workloads.

4. Google Cloud Platform (GCP)

Via Google Cloud Platform (GCP)

Google Cloud offers a comprehensive suite of compute services, including a rich portfolio of GPU instance types.

As an alternative to Paperspace, GCP provides the familiarity of Google’s infrastructure and advanced services, albeit with more complexity (and cost).

GCP is ideal for teams that want access to Google’s broader ecosystem – from BigQuery to TensorFlow integrations – and need the ability to scale to production seamlessly.

It’s a top choice if you already leverage Google services or require specialty offerings like TPUs (Tensor Processing Units) alongside GPUs. Enterprises and startups that received Google Cloud credits also find it convenient to use GCP for GPU workloads.

GCP Key Features:

• Wide GPU selection: Offers GPUs ranging from older NVIDIA K80s to Tesla T4, V100, A100, and the latest NVIDIA L4 for genAI and video workloads. Notably, GCP was the first among major clouds to introduce the L4 GPU in their G2 instances.
• Managed services for AI: Provides an integrated ecosystem with Vertex AI, AutoML, BigQuery ML, and AI Platform Pipelines, allowing seamless transitions from training to deployment.
• Global infrastructure: Boasts a robust worldwide network across the Americas, Europe, Asia, and more, with co-located Cloud CDN and Cloud Storage for efficient data handling.
• Preemptible GPUs: Offers short-lived instances at 70–80% lower costs, ideal for non-critical training jobs that can handle occasional interruptions.
• Sustained use discounts: Automatically reduces hourly rates for long-running VMs, benefiting continuous GPU workloads without requiring upfront commitments.

GCP Limitations:

• Cost and complexity: On-demand GPU prices can be high (e.g., around $2.48/hour for V100 and $0.71/hour for L4) plus additional VM costs, and managing these expenses requires expertise.
• Egress and ancillary fees: Data transfers out of GCP can be expensive, especially for heavy cross-region or external downloads.
• No seamless pause/resume: GPU VMs incur full charges even when paused; to stop billing, you must snapshot and terminate instances, which is less convenient for intermittent usage.

GCP Pricing:

• On-demand examples: GPUs are billed per second (with a one-minute minimum). Typical rates are approximately $0.35/hour for T4 GPUs, $2.48/hour for V100s, and $0.71/hour for L4 GPUs, with additional VM costs (around $0.50/hour) for CPU/RAM.
• Discounts and credits: New users receive a $300 free credit; sustained-use discounts (up to 30% off) apply for long-running workloads, and committed use reservations can lower V100 pricing below $1.50/hour. Preemptible GPUs can cost about 20–30% of on-demand prices, for example, a V100 around $0.74/hour.

5. Amazon Web Services (AWS)

‍Via Amazon Web Services (AWS)

Amazon’s AWS is the incumbent cloud leader, offering the broadest range of compute instances – GPUs included.

AWS’s GPU instances (P- and G-series EC2 instances, and AWS SageMaker for managed ML) are a reliable alternative to Paperspace, particularly for production at scale.

Choose AWS if you require deep integration with a wide array of cloud services or need robust enterprise features.

It’s best for companies already on AWS for other resources (storage, databases) that want GPUs in the same environment, or those needing features like IAM security controls, compliance certifications, and 24/7 enterprise support.

AWS is also a go-to for cutting-edge custom AI hardware (like AWS Trainium or Inferentia chips) alongside GPUs.

AWS Key Features:

• Wide GPU instance lineup: Offers instances optimized for training and inference, including P3 (V100 GPUs), P4d/P4de (A100 40GB GPUs with 400 Gbps networking), and upcoming P5 with H100 GPUs.
• Global availability & scale: Operates in numerous regions and can deliver 100+ GPUs for large-scale training or high-volume inference workloads.
• Ecosystem and services: Integrates with AWS S3, FSx, AWS Batch, and SageMaker to provide seamless end-to-end ML workflows with built-in security and monitoring.
• Advanced networking: Features high-bandwidth connectivity with NVLink/NVSwitch and Elastic Fabric Adapter (EFA) for efficient multi-GPU training across nodes.
• Spot instances: Provides significant cost savings (often 70–90% off on-demand) for GPU workloads that can tolerate interruptions.

AWS Limitations:

• High cost for on-demand: On-demand GPU pricing is expensive; for instance, a single V100 costs about $3.06 per hour and an 8× A100 instance around $32.77 per hour.
• Steep learning curve: Managing GPU instances requires configuring VPCs, security groups, EBS volumes, and often manual setup for SSH or Jupyter, which can be complex compared to plug-and-play solutions.
• Data transfer fees: Charges for data egress can add up significantly, especially for workflows involving large-scale data movement or cross-region transfers.

AWS Pricing:

• On-demand examples: Roughly $3.06 per hour for a 1× V100 instance; about $32.80 per hour for an 8× A100 40GB instance; approximately $1.19 per hour for a g5.xlarge instance (NVIDIA A10G) and around $0.59 per hour for a g4dn.xlarge instance (T4 GPU). Billing is per second.

6. CoreWeave

‍Via CoreWeave

CoreWeave is a cloud provider specializing in GPU compute, offering a scalable and high-performance infrastructure designed for intensive GPU workloads.

It has rapidly expanded by building one of the largest independent GPU fleets, notably securing large volumes of NVIDIA H100 GPUs.

It is well-suited for AI startups and digital studios that require dedicated, configurable GPU resources without the need to build on-premises infrastructure.

It is frequently chosen for training large-scale models, such as advanced language models and distributed implementations of Stable Diffusion, as well as for complex rendering tasks.

CoreWeave Key Features:

• GPU-centric infrastructure: Offers granular control over instance specs (GPU type, count, CPU, memory, fractional options) to avoid overprovisioning.
• Latest NVIDIA hardware: Provides A40, A100, and H100 GPUs with NVLink/NVSwitch; superior H100 availability at lower prices.
• Managed Kubernetes & orchestration: Supports GPU clusters with Kubernetes and Container Cloud, ideal for ML ops and scheduling many GPUs.
• High bandwidth networking: Provides fast intra-data center speeds and direct interconnects for low-latency hybrid cloud setups.
• Enterprise support and SLAs: Offers 24/7 support, expert assistance, high security, and isolated reserved infrastructure for enterprise clients.

CoreWeave Limitations:

• Limited public regions: Primarily operates in US data centers; limited EU/Asia presence may affect latency or compliance.
• No low-end GPUs/CPUs: Focuses solely on high-performance GPUs; not suitable for small workloads or quick experiments.
• Monthly billing: Bills on a monthly postpaid cycle; users must monitor usage to avoid unexpected costs.

CoreWeave Pricing:

• Competitive GPU pricing: NVIDIA A100 80GB at ~$2.21/hour and H100 80GB SXM at ~$4.75/hour on-demand. Prices are lower than AWS.
• No ingress/egress fees: Data transfers are free within the same data center, with volume discounts and reserved capacity deals available.

7. Hyperstack (NexGen Cloud)

‍Via Hyperstack

Hyperstack is a GPU-as-a-service platform by Europe’s NexGen Cloud. It delivers high-performance AI and HPC workloads using cutting-edge NVIDIA GPUs, such as H100 and A100, with advanced features like NVLink connectivity and VM hibernation.

This innovative service offers fine-grained performance control and cost optimizations, making it ideal for AI companies and research labs needing powerful, scalable GPU infrastructure in Europe.

Hyperstack Key Features:

• Latest GPUs with NVLink: Provides NVIDIA H100 and A100 GPUs with multi-GPU NVLink support for high-bandwidth memory sharing.
• 350 Gbps Networking: Offers ultra-fast networking up to 350 Gb/s, reducing latency for distributed training and HPC tasks.
• VM Hibernation: Supports pausing and resuming VMs, saving costs during idle periods with minimal storage charges.
• No Oversubscription: Guarantees dedicated CPU/GPU resources with NUMA-aware scheduling and CPU pinning for optimal performance.
• DevOps Integration: Supplies Terraform providers, SDKs, and Kubernetes support for seamless infrastructure automation.

Hyperstack Limitations:

• Newer Entrant: May lack extensive documentation and community support compared to established platforms.
• Geographic Focus: Primarily serves Europe and some North American regions; other regions have limited local options.
• High-end Focus: Only high-end GPUs are offered; low-cost or older generation GPUs are not available.

Hyperstack Pricing:

• Pay-as-you-go Billing: Bills per minute with transparent pricing; no data ingress/egress fees.
• Aggressive GPU Rates: NVIDIA H100 costs approximately $1.95/hour and A100 around $1.40/hour on-demand.
• Cost Optimization: Allows reservations and hibernation to further reduce expenses on long-term projects.

8. JarvisLabs.ai

‍Via JarvisLabs.ai

JarvisLabs is a cloud GPU provider that has gained traction among the AI developer community for its low-cost offerings and easy-to-use platform.

It’s a nimble startup (based in India) providing on-demand GPU rentals with a simple web interface and Jupyter notebook support.

JarvisLabs is particularly popular in communities like Kaggle and fast.ai for training models without buying expensive hardware.

If you liked Paperspace’s Gradient notebooks, JarvisLabs provides a similar vibe but with potentially lower pricing and community vibes.

JarvisLabs Key Features:

• One-click notebooks and Web UIs: Instantly launch JupyterLab or Automatic1111 setups.
• Pause and resume instances: Pause VMs at minimal storage cost (~$0.0003/hr).
• Reserved vs. non-reserved pricing: Reserve for full uptime or opt for cheaper, pausable instances.
• Wide GPU range with clear specs: Offers RTX A5000, A6000, A100, etc., with detailed resource limits.
• Community and support: Provides fast, responsive help and initial free credits (~$20).

JarvisLabs Limitations:

• Capacity and scale: Supports up to 8 GPUs per instance; limited concurrent GPU availability.
• Geographic location: Servers mainly in India may increase latency for distant users.
• Less polish: The UI and advanced features are less refined than larger cloud services.

JarvisLabs Pricing:

• NVIDIA A100 40GB: Approximately $1.29/hr reserved; ~$0.79/hr non-reserved.
• RTX A6000: Approximately $0.99/hr reserved; ~$0.59/hr non-reserved.
• RTX A5000: Approximately $0.59/hr reserved; ~$0.39/hr non-reserved.

9. DataCrunch.io

‍Via DataCrunch.io

DataCrunch is a newer, AI-focused cloud provider offering cost-effective, eco-friendly GPU compute. It uses renewable energy data centers and transparent pricing, with savings up to 8× compared to hyperscalers.

Ideal for budget-conscious AI training and sustainable computing, especially for startups and EU-based teams.

DataCrunch Key Features:

• High-end GPUs at low cost: Modern GPUs like V100, A100, and H100 are offered at drastically lower rates.
• 100% renewable energy data centers: All infrastructure runs on green, renewable power.
• Enterprise-grade security: Implements robust physical, encryption, and compliance measures.
• Simple interface and API: Provides an easy web console and API for quick instance launches and management.
• Personalized support: Offers expert, responsive customer service with trial credits for new users.

DataCrunch Limitations:

• Limited track record: As a new provider, long-term reliability is still unproven.
• Basic feature set: Focuses on GPU VM rental without extra managed ML services.
• Geographical limitations: Primarily serves Europe and North America; other regions may face higher latency.

DataCrunch Pricing:

• Up to 8× savings: For example, a V100 costs about $0.39/hr versus ~$3.06/hr elsewhere; H100 is ~$3.35/hr.
• Straightforward hourly billing: No hidden fees; you only pay while running, with occasional trial credits offered.

10. Microsoft Azure (Azure ML / N-Series VMs)

‍Via Microsoft Azure

Microsoft Azure is another major cloud platform offering GPU instances and a suite of AI services.

Azure’s GPU offerings (the N-series virtual machines and the Azure Machine Learning service) are a viable Paperspace alternative, especially for organizations aligned with Microsoft’s ecosystem.

What It's Best For? Organizations in the Microsoft ecosystem and those needing enterprise AI at scale.

Azure is ideal if your infrastructure is already Windows- or Azure-centric (e.g., .NET applications, Office 365 integration, etc.) and you want your AI workloads in the same cloud.

It’s also a top choice for companies requiring compliance certifications and support contracts that a smaller provider might not offer.

Azure’s AI offerings (including Azure ML and even partnerships like Azure OpenAI Service) make it attractive for full-stack AI development and deployment under one roof.

Azure Key Features:

• Wide GPU lineup: Offers P3 (V100), P4d (A100), ND H100 clusters, NV-series for visualization, and ND96amsr for massive projects.
• Managed AI services: Azure ML streamlines model training, automation, and deployment with integrated DevOps tools.
• Windows support: Robust GPU support for Windows workloads, including DirectX and Visual Studio integration.
• Hybrid cloud integration: Azure Stack and Arc enable consistent deployment across on-premises and cloud environments.
• Enterprise compliance: Meets numerous standards with Active Directory, role-based access, and detailed cost management.

Azure Limitations:

• High on-demand pricing: GPU instances (e.g., V100 at ~$3.06/hr) are expensive without discounts.
• Complex setup: Configuring networks, storage, and security can be challenging for new users.
• Limited startup community: Fewer plug-and-play examples and community resources compared to AWS or GCP.

Azure Pricing:

• On-demand rates: V100s around $3.06/hr; multi-GPU ND96amsr instances cost approximately $26–$30/hr.
• Discount options: Reserved Instances can save up to 72%; spot VMs may cut costs by 50–80%.
• Free credits: New users may receive $200 in free Azure credit for 30 days.

Conclusion

Each of these alternatives brings unique strengths to the table.

Runpod.io stands out for its combination of low cost, ease of use, and serverless capabilities, making it especially attractive to agile AI/ML teams looking to maximize productivity and value.

Users have praised RunPod.io for its affordability, variety of GPU options, and excellent customer service.

When evaluating alternatives to Paperspace, it's essential to consider the specific requirements of your projects.

Platforms like RunPod.io offer compelling features that may align well with the needs of dynamic AI development teams seeking efficient and scalable solutions.

Ready to give runpod.io a shot? Try it’s serverless GPU here.

‍