RunPod vs Lambda Labs vs VectorLay: GPU Cloud Comparison (2026)
RunPod, Lambda Labs, and VectorLay take fundamentally different approaches to GPU cloud computing. RunPod operates a marketplace with serverless options, Lambda Labs runs its own data centers for ML workloads, and VectorLay aggregates distributed GPU nodes with built-in failover. Here's how they compare on price, features, and reliability.
TL;DR
- →RunPod: Best GPU marketplace with serverless support. Wide selection of GPUs, community templates. Good for experimentation and serverless inference.
- →Lambda Labs: Own data centers, ML-focused. Best for training clusters and teams that need NVLink H100s. Limited consumer GPU options.
- →VectorLay: Distributed network with automatic failover. Cheapest consumer GPUs (RTX 4090 at $0.49/hr). Best for always-on inference at the lowest cost.
Provider Overviews
RunPod
RunPod started as a GPU marketplace and has grown into one of the most popular platforms for ML practitioners. They offer two main products: on-demand GPU pods (virtual machines with GPU access) and serverless endpoints (pay-per-request inference).
Their strength is variety. RunPod lists GPUs from consumer cards like the RTX 3090 all the way up to H100s, sourced from a mix of their own infrastructure and community providers. Community templates make it easy to spin up popular frameworks. The serverless product is well-designed, with autoscaling and support for custom containers.
The tradeoff: pricing varies depending on the provider behind each GPU, and there's no built-in failover for on-demand pods. If the underlying host goes down, your pod goes down with it.
Lambda Labs
Lambda Labs takes the opposite approach: they own and operate their own data centers. This gives them full control over hardware, networking, and reliability. Their focus has shifted heavily toward large-scale training clusters, particularly multi-node H100 setups with high-bandwidth InfiniBand networking.
Lambda's on-demand cloud offers A100s and H100s at competitive prices ($1.29/hr for A100 40GB, $2.49/hr for H100). However, they don't offer consumer GPUs like RTX 3090s or 4090s. Availability can be an issue since capacity is limited to their own data centers, and popular GPU types frequently sell out.
Lambda is best suited for teams doing serious training work that need guaranteed high-bandwidth interconnects and enterprise-grade reliability.
VectorLay
VectorLay takes a distributed approach, aggregating GPU capacity from nodes across a network and presenting it as a unified platform. Each deployment runs inside a hardware-isolated VM with dedicated GPU passthrough via VFIO, so workloads are securely isolated at the hypervisor level.
The key differentiator is automatic failover. If a node goes offline, VectorLay automatically reschedules the workload to another available node without manual intervention. This is built into the platform at the orchestration layer, not bolted on as an afterthought.
VectorLay focuses on consumer GPUs (RTX 3090, RTX 4090) at the lowest prices in the market, making it the most cost-effective option for inference workloads that fit within 24GB of VRAM. They also offer data center GPUs like A100s and H100s for larger models.
Pricing Comparison
Here's a direct comparison of on-demand GPU pricing across all three providers:
| GPU | VectorLay | RunPod | Lambda Labs |
|---|---|---|---|
| RTX 4090 (24GB) | $0.49/hr | $0.74/hr | N/A |
| RTX 3090 (24GB) | $0.29/hr | $0.44/hr | N/A |
| A100 40GB | $1.64/hr | $1.64/hr | $1.29/hr |
| H100 80GB | $2.49/hr | $3.49/hr | $2.49/hr |
Prices as of February 2026. On-demand pricing shown. RunPod community cloud pricing may vary. Lambda Labs pricing for reserved instances may be lower.
VectorLay has the lowest prices for consumer GPUs by a significant margin. For data center GPUs, Lambda Labs edges ahead on A100 pricing, while VectorLay and Lambda are tied on H100s. RunPod's H100 pricing is the highest of the three, though they offer the broadest GPU selection overall.
Feature Comparison
| Feature | VectorLay | RunPod | Lambda Labs |
|---|---|---|---|
| Automatic Failover | |||
| Serverless Endpoints | |||
| Consumer GPUs (3090/4090) | |||
| Data Center GPUs (A100/H100) | |||
| Billing Granularity | Per-minute | Per-second | Per-hour |
| Egress Fees | None | None | None |
| Workload Isolation | VM + VFIO passthrough | Container | VM |
| Multi-node Training |
Best For
RunPod: Experimentation and Serverless
Lambda Labs: Training and Enterprise ML
VectorLay: Cost-Effective Inference
Annual Cost Scenarios
Let's put the pricing differences in perspective with real-world scenarios:
Scenario 1: Single RTX 4090, 24/7 Inference
Running a 7B-13B model for a production API. Needs one GPU running around the clock.
Scenario 2: Two H100s, 24/7 Training + Inference
Running a 70B model or fine-tuning jobs. Needs two H100 80GB GPUs.
Scenario 3: Four RTX 3090s, 24/7 Multi-Model Serving
Serving multiple smaller models (Whisper, Stable Diffusion, a 7B LLM, an embedding model).
Try VectorLay
Deploy your first GPU workload in minutes. No credit card required for your first deployment. RTX 4090s from $0.49/hr with automatic failover included.
Prices accurate as of February 2026. Cloud GPU pricing changes frequently. Always verify current rates on each provider's website. This comparison uses on-demand pricing. RunPod community cloud prices may vary by provider. Lambda Labs offers reserved pricing that may be lower than on-demand rates shown. VectorLay is our own product and we have done our best to present all providers fairly.