7 Best RunPod Alternatives (2026)

Why Look for RunPod Alternatives?

RunPod has built a solid GPU cloud platform with both on-demand pods and a serverless inference offering. It's a go-to for many ML engineers and hobbyists who need quick access to GPU compute without enterprise pricing. The platform supports a wide range of GPUs, from consumer-grade RTX cards to data center A100s and H100s, and their templates make it easy to spin up pre-configured environments for popular frameworks.

However, RunPod isn't perfect for every use case. Common reasons people look for alternatives include pricing that's still too high for 24/7 workloads, occasional availability issues with popular GPU types, the lack of built-in failover for production inference, and limited support for teams that need enterprise-grade reliability. If you're running inference at scale, experimenting with large models, or need guaranteed uptime, you may want to explore other options.

We've evaluated dozens of GPU cloud providers and narrowed down the seven best RunPod alternatives based on pricing, reliability, GPU selection, ease of use, and support for ML inference workloads.

2. Vast.ai

Vast.ai operates as a decentralized GPU marketplace where individual hosts list their hardware and renters bid on compute time. This auction-style pricing model can yield some of the lowest prices in the market, with RTX 4090s occasionally available for under $0.40/hr during off-peak periods. The platform supports an enormous range of hardware, from older GTX 1080 Ti cards to cutting-edge H100s.

The trade-off is reliability. Since you're renting from individual hosts, machine quality varies significantly. A host might reboot their machine, take it offline for maintenance, or simply disappear. There's no built-in failover — if your host goes down, your workload stops. Vast.ai offers "interruptible" and "on-demand" tiers, but even on-demand instances can experience host-side issues.

For development and experimentation, Vast.ai is excellent. The DiskFilter and GPU Filter tools make it easy to find exactly the configuration you need. For production inference, however, the lack of guaranteed uptime makes it a risky choice without significant engineering to handle failover yourself.

Pricing: Variable, typically $0.30–$0.80/hr for RTX 4090 depending on market conditions.

Best for: Budget-conscious researchers and hobbyists who can tolerate interruptions and don't need production-grade reliability.

3. Lambda Labs

Lambda Labs has been a staple of the ML infrastructure space for years, initially known for their GPU workstations before expanding into cloud compute. Their cloud offering focuses on high-end data center GPUs — A100s, H100s, and multi-GPU clusters — making them a strong choice for training workloads that need NVLink interconnects and high-bandwidth networking.

Lambda's pricing for data center GPUs is competitive, with A10 instances at around $0.75/hr and H100s at $2.49/hr. They run their own data centers, which gives them more control over hardware quality and availability than marketplace providers. The platform includes pre-installed ML frameworks, persistent storage, and SSH access for a familiar development experience.

The main limitation is capacity — Lambda frequently sells out of popular GPU types, and their waitlist for H100 instances can stretch for months. They also don't offer consumer-grade GPUs, so if you want an RTX 4090 for cost-effective inference, you'll need to look elsewhere. Their pricing, while reasonable for data center hardware, is still 2–5x more expensive than consumer GPU alternatives.

Pricing: A10 at $0.75/hr, A100 (40GB) at $1.29/hr, H100 at $2.49/hr.

Best for: ML teams doing training that need high-end multi-GPU clusters with NVLink.

4. CoreWeave

CoreWeave positions itself as the "AI hyperscaler" — a GPU-specialized cloud provider built from the ground up for compute-intensive workloads. Their Kubernetes-native platform makes it straightforward to orchestrate multi-GPU deployments, and they offer bare-metal performance with enterprise-grade networking. CoreWeave operates multiple data centers across the US with A100, H100, and even H200 GPU clusters.

Pricing at CoreWeave is significantly lower than hyperscalers for equivalent hardware. An A100 (40GB) runs about $2.21/hr compared to $3.67/hr on AWS. For large-scale training jobs, the savings compound quickly. CoreWeave also offers reserved pricing with substantial discounts for committed usage, making them attractive for enterprises with predictable workloads.

The drawback is that CoreWeave is focused on enterprise and large-scale customers. Minimum spend requirements, the need for Kubernetes expertise, and a sales-driven onboarding process make it less accessible for smaller teams or individual developers. If you just need a single GPU for inference, CoreWeave is overkill.

Pricing: A100 (40GB) at ~$2.21/hr, H100 at ~$2.06/hr (reserved pricing available).

Best for: Enterprises and well-funded startups running large-scale training or multi-GPU inference clusters.

5. AWS EC2 GPU Instances

Amazon Web Services offers the broadest selection of GPU instances in the cloud through EC2. From the budget-oriented G4dn instances with T4 GPUs to the massive P5 instances with H100s and UltraCluster interconnects, AWS has an option for virtually every workload. The deep integration with S3, SageMaker, Lambda, and the rest of the AWS ecosystem makes it the default choice for many enterprise teams.

The pricing, however, is by far the highest on this list. A single A10G instance (g5.xlarge) starts at $1.21/hr, and A100 instances (p4d.24xlarge) run $32.77/hr for an 8-GPU node. Even with reserved instances and savings plans, AWS GPU pricing is typically 3–7x more expensive than alternatives for equivalent compute power. Factor in egress fees ($0.09/GB), EBS storage costs, and load balancer charges, and the true cost climbs even higher.

AWS makes sense when you're already deeply invested in the AWS ecosystem, need compliance certifications (HIPAA, SOC2, FedRAMP), or require access to the latest hardware with enterprise SLAs. For cost-sensitive inference workloads, though, AWS is rarely the right choice.

Pricing: A10G at $1.21/hr, A100 at $3.67/hr (per GPU, on-demand).

Best for: Enterprises with existing AWS commitments and strict compliance requirements.

6. Google Cloud GPU (Vertex AI)

Google Cloud offers GPU compute through both Compute Engine (bare instances) and Vertex AI (managed ML platform). Their TPU offerings are unique to GCP and provide excellent performance for JAX and TensorFlow workloads. GPU options include T4, L4, A100, and H100, with Vertex AI providing additional tooling for model serving, experiment tracking, and pipeline orchestration.

GCP's GPU pricing is comparable to AWS — A100 (40GB) instances run about $3.67/hr on-demand. Where Google differentiates is with Vertex AI's managed inference endpoints, which handle auto-scaling, model versioning, and A/B testing out of the box. If you're building on TensorFlow or JAX and want a fully managed pipeline from training to serving, GCP's integration is hard to beat.

The downsides mirror AWS: high cost, complex pricing with multiple SKUs for compute, storage, and networking, and a steep learning curve for the full GCP ML stack. Spot pricing (preemptible VMs) can reduce costs significantly but introduces reliability concerns similar to Vast.ai's interruptible instances.

Pricing: A100 (40GB) at ~$3.67/hr, L4 at ~$0.70/hr (on-demand).

Best for: Teams heavily invested in TensorFlow/JAX or those who need Vertex AI's managed MLOps pipeline.

7. Modal

Modal takes a fundamentally different approach to GPU cloud by offering a serverless compute platform with Python-native APIs. Instead of managing instances, you write decorated Python functions and Modal handles provisioning, scaling, and teardown automatically. Cold starts are typically 1–5 seconds, and you can scale from zero to hundreds of GPUs seamlessly.

The developer experience is arguably the best in the space. Modal's @app.function decorator system lets you define GPU requirements, container images, and scaling policies right in your Python code. The platform handles container caching, model weight storage, and request routing. For teams that want to move fast without learning Kubernetes or writing Dockerfiles, Modal is compelling.

The cost, however, scales steeply. Modal charges per-second for GPU time, and the per-second rates are higher than hourly providers. An A100 runs about $4.53/hr equivalent, and their T4 at $0.59/hr. For bursty workloads with significant idle time, the scale-to-zero model can save money. For consistent 24/7 inference, you'll pay significantly more than dedicated GPU providers.

Pricing: T4 at $0.59/hr, A100 (40GB) at ~$4.53/hr (per-second billing).

Best for: Developers who want the fastest path from code to deployed inference with minimal infrastructure management.

RunPod Alternatives Comparison Table

How to Choose the Right RunPod Alternative

The best RunPod alternative depends on your specific workload, budget, and reliability requirements. Here's a decision framework to guide your choice:

Ultimately, many teams use multiple providers for different workloads. You might train on Lambda, run production inference on VectorLay, and prototype on Modal. The key is matching each provider's strengths to your specific needs rather than trying to find one platform that does everything.

Frequently Asked Questions