Runcrate

Runcrate GPU pricing overview

Runcrate is a specialist GPU cloud provider headquartered in US. Runcrate is a GPU cloud founded in 2024 offering on-demand instances with per-second billing and no minimum commitment — the meter stops the moment you stop the instance. The platform aggregates GPU capacity across eight global regions spanning North America, Europe, and Asia, covering 14 GPU models from RTX A6000 and A100 through H100 and RTX PRO 6000. Full root access via SSH, Docker support, and custom images are included on every instance. An OpenAI-compatible inference API serves open-source models billed per token, and reserved or dedicated capacity is available for teams with monthly volume commitments. Billing is Per-second on-demand with a minimum commitment of None. Available regions include US, EU, APAC. GPU instances can be provisioned on-demand, making Runcrate suitable for both short-duration experiments and sustained production workloads.

Runcrate vs other GPU providers

Runcrate competes with providers including Lambda Labs, CoreWeave, RunPod, Paperspace, Vast.ai, and the major hyperscalers (AWS, Google Cloud, Azure) for GPU compute workloads spanning LLM training, fine-tuning, and inference serving. Key differentiators include: Per-second billing — no minimum commitment; OpenAI-compatible inference API; 14 GPU models including H100 and RTX PRO 6000. Use the side-by-side comparison tool above to see Runcrate pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 22 Runcrate listings alongside 91+ providers in a single sortable view.

Best use cases for Runcrate

Runcrate is best suited for: Teams needing flexible per-second GPU billing, Open-source model inference via API, Multi-region GPU workloads, Cost-sensitive on-demand training and fine-tuning. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 22 active GPU listings on Runcrate, covering B300 288GB, B200 192GB, H200 141GB, GH200 96GB and more. H100 SXM5 instances with NVLink interconnect are commonly used for large-scale training workloads. A100 40GB and RTX 4090 instances are frequently used for fine-tuning and inference of smaller models.

Runcrate billing model and cost structure

Runcrate uses Per-second on-demand pricing. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing may be available for predictable long-running workloads — check the provider's pricing page for current committed-use rates. Always compare the effective hourly rate including egress, storage, and networking costs when evaluating total cost of ownership across providers.

Choosing the right GPU on Runcrate

GPU selection depends on model size, precision, and whether your workload is compute-bound or memory-bandwidth-bound. For LLM training above 30B parameters, H100 80GB SXM5 instances with NVLink are the standard choice — high HBM3 bandwidth and FP16 throughput make them well-suited for large transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB is a common choice on many providers. RTX 4090 instances are widely used for fine-tuning, prototyping, and quantized inference (INT4/INT8) of models up to 70B. Read the H100 vs A100 guide or the GPU benchmarks for ML guide for a full breakdown.

How Runcrate pricing data is collected

Prices shown are sourced from Runcrate's public pricing API or pricing page and refreshed every 15 minutes. On-demand rates reflect the current list price for a single GPU instance in the cheapest available region. Spot prices, where available, reflect interruptible instance rates at the time of the last snapshot. All prices are in USD per hour. Daily snapshots are kept in our history archive and visualised in the GPU price history charts — useful for identifying seasonal pricing patterns and evaluating whether current rates are above or below the 30-day average.

Evaluating managed LLM inference APIs as an alternative to self-hosted GPU compute? Compare live LLM token prices across OpenAI, Anthropic, Google, Groq, and many other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.