GPU Cloud Providers Comparison 2026: BHK Cloud vs Vast.ai vs RunPod vs TensorDock vs Lambda Labs

Why GPU Cloud Pricing Is Hard to Compare

GPU cloud pricing looks simple at first glance: providers quote a per-hour rate for a GPU instance. But the real cost of running GPU workloads in the cloud depends on storage pricing, data transfer fees, instance availability, minimum commitment terms, and support quality. A $0.29/hr GPU can easily cost more than a $0.45/hr one once you factor in storage, bandwidth, and idle-time charges.

In this comparison, we break down five GPU cloud providers across the dimensions that matter for real workloads: pricing, GPU selection, storage costs, geographic regions, support quality, and hidden fees. We are transparent about where BHK Cloud fits — and where it doesn't.

Provider Overview

Detailed Pricing Comparison

Entry-Level GPU Instances (RTX 3090 / A4000 class)

For fine-tuning small models, running inference, or prototyping, the RTX 3090 / A4000 class hits the sweet spot of 24 GB VRAM at a reasonable price. Here's what each provider charges:

At $0.15/hr, BHK Cloud is the cheapest option for RTX 3090-class GPUs. Vast.ai's marketplace can sometimes beat this price, but availability is inconsistent and quality varies by host. RunPod and TensorDock are roughly 2× the price. Lambda Labs doesn't offer RTX 3090 — their cheapest GPU is the A6000 at $0.50/hr.

High-End GPU Instances (A100 / H100 class)

For training large models or running production inference at scale, the A100 (80 GB) and H100 are the standard choices:

For A100 workloads, Vast.ai's marketplace offers the lowest prices if you can find a reliable host. BHK Cloud at $0.95/hr is competitive with TensorDock and significantly cheaper than RunPod and Lambda Labs. H100 pricing follows a similar pattern, with BHK Cloud at $1.49/hr.

Storage Costs: The Hidden Multiplier

GPU compute is only half the equation. AI workloads consume massive amounts of storage — datasets, model checkpoints, containers, and logs. Storage pricing varies dramatically across providers:

This is where the pricing gap widens dramatically. At 10 TB of storage, BHK Cloud costs $24.90/month — less than 3% of what Lambda Labs charges ($1,024/month). RunPod's network volumes at $0.07/GB look cheap per-GB but add up to $716/month at 10 TB. If your workload is storage-heavy (datasets, model repositories, experiment tracking), storage pricing dominates your total cost.

Data Transfer and Bandwidth

Moving data in and out of GPU instances is another cost that isn't always obvious from the headline pricing:

• BHK Cloud: Free ingress, 1 TB/month free egress, then $0.01/GB. No intra-region transfer fees.
• Vast.ai: Depends on host. Most hosts include basic bandwidth, but some charge for high-volume transfers.
• RunPod: Free ingress, egress included in instance pricing. No separate bandwidth charges.
• TensorDock: Free ingress, egress varies by host. Typically 1–5 TB included.
• Lambda Labs: Free ingress, $0.05/GB egress after 1 TB free. Inter-region transfer charged.

Instance Availability and Startup Time

Price means nothing if you can't get an instance when you need it. GPU availability has been a persistent challenge across the industry since 2023:

• BHK Cloud: Frankfurt data center, dedicated capacity. Instances start in under 60 seconds. RTX 3090 and A100 available on-demand. H100 may have waitlist during peak demand.
• Vast.ai: Largest marketplace — thousands of hosts. Availability varies by GPU type and bid price. Can take minutes to hours to secure a spot instance at low bids.
• RunPod: Secure Cloud instances are on-demand but frequently sold out for popular GPU types (A100, H100). Community Cloud has better availability but lower reliability.
• TensorDock: Marketplace model means availability fluctuates. RTX 3090 and 4090 generally available; A100 and H100 can be scarce.
• Lambda Labs: On-demand instances are frequently sold out. Reservations recommended 1–2 weeks in advance for A100/H100. This is the biggest complaint in Lambda user reviews.

Support and SLA

For production workloads, support quality is a critical differentiator:

• BHK Cloud: Managed support included. Email + Telegram response within 4 hours. European business hours with on-call for critical issues. Custom SLA available for enterprise customers.
• Vast.ai: Community support (Discord, Reddit). No SLA. Hosts are independent operators — support quality varies. Good for experimentation, risky for production.
• RunPod: Ticket-based support. Premium support available for enterprise plans. Serverless workers have built-in auto-scaling that reduces support burden.
• TensorDock: Email + Discord support. Response times of 24–48 hours typical. No SLA for standard plans.
• Lambda Labs: Email + phone support. Enterprise SLA available. Generally well-regarded support, but slower response during GPU shortages.

Minimum Commitments and Billing

• BHK Cloud: Pay-as-you-go, per-minute billing. No minimum commitment. Prepaid credits available at 10% discount.
• Vast.ai: Pay-as-you-go via credit system. Minimum deposit $5. No long-term commitments.
• RunPod: Pay-as-you-go for Secure Cloud. Reserved instances available at 30–50% discount with 1-month commitment.
• TensorDock: Pay-as-you-go. Reserved instances at 30% discount with 1-month minimum.
• Lambda Labs: On-demand and reserved pricing. Reserved instances require 1–3 year commitment for best pricing (up to 40% discount).

Who Should Use Which Provider?

Choose BHK Cloud if:

• You need the lowest GPU pricing in the EU (Frankfurt-based)
• Storage costs matter — $2.49/TB is 10–40× cheaper than competitors
• You want managed support without enterprise pricing
• Your workloads are in Europe and need GDPR compliance
• You need predictable, on-demand availability without bidding

Choose Vast.ai if:

• You need the absolute lowest GPU price and are willing to bid
• You need exotic GPU types (GTX 1080 Ti, RTX 2080 Ti, etc.)
• You need GPUs in specific geographic locations worldwide
• You are doing experimentation, not production

Choose RunPod if:

• You need serverless GPU inference (their strongest feature)
• You want a mix of managed and community cloud options
• You need L40S or other NVIDIA data center GPUs
• You value the ecosystem (templates, CLI, API)

Choose TensorDock if:

• You need GPUs in Asia-Pacific regions
• You want reserved pricing without long-term lock-in
• You need Windows GPU instances (rarely offered elsewhere)

Choose Lambda Labs if:

• You need H100 and GH200 clusters for large-scale training
• You are a US-based enterprise with compliance requirements
• You value brand reputation and established infrastructure
• You can commit to 1–3 year reservations

Bottom Line: The Real Cost of GPU Cloud

The headline GPU price is a starting point, not the full picture. For a typical ML team running a mix of training and inference workloads with 5 TB of storage, here's the actual monthly cost comparison:

BHK Cloud's total cost at $120/month is 4–7× cheaper than the alternatives for this workload profile. The gap widens with storage-intensive workloads.

Last updated: August 01, 2026. Pricing data is based on publicly available information as of this date. Providers may change pricing — always verify current rates before making decisions.