Compute Comparison

AtmosCompute

Specialist Cloud

AtmosCompute provides on-demand H100, A100, and L40S GPU instances for AI and ML workloads with flexible pay-as-you-go billing and straightforward pricing that makes it easy to estimate costs for training and inference jobs. Fast provisioning and a simple interface lower the barrier to entry for startups and small teams exploring GPU compute for the first time. A no-frills on-demand GPU cloud for teams that want quick access to professional NVIDIA hardware without enterprise complexity.

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Cheapest On-Demand

$0.600/hr

Cheapest Spot

GPU Listings

4

Billing

Pay-as-you-go

Performance Benchmarks

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Provider Info

Headquarters

United States

Founded

2023

Regions

US

Min Commitment

None

Support

Standard

Strengths

  • Simple pricing
  • Flexible billing
  • Fast provisioning

Limitations

  • Smaller provider — limited scale vs hyperscalers
  • Fewer regions than major cloud providers
  • Less mature ecosystem and fewer integrations

Best For

StartupsShort training runsInference workloads

Full GPU Catalog

GPU ModelvRAMOn-DemandSpotAvailabilityRegion
RTX 409024 GB$0.600HighUS
L40S48 GB$1.09HighUS
A100 80GB80 GB$1.39MedUS
H100 80GB80 GB$2.48MedUS

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AtmosCompute GPU pricing overview

AtmosCompute is a specialist GPU cloud provider headquartered in United States. AtmosCompute provides on-demand H100, A100, and L40S GPU instances for AI and ML workloads with flexible pay-as-you-go billing and straightforward pricing that makes it easy to estimate costs for training and inference jobs. Fast provisioning and a simple interface lower the barrier to entry for startups and small teams exploring GPU compute for the first time. A no-frills on-demand GPU cloud for teams that want quick access to professional NVIDIA hardware without enterprise complexity. Billing is Pay-as-you-go with a minimum commitment of None. Available regions include US. On-demand GPU instances can be provisioned in minutes with no upfront cost, making AtmosCompute suitable for both short-duration experiments and sustained production workloads.

AtmosCompute vs other GPU providers

AtmosCompute 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: Simple pricing; Flexible billing; Fast provisioning. Use the side-by-side comparison tool above to see AtmosCompute pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 4 AtmosCompute listings alongside 94+ providers in a single sortable view.

Best use cases for AtmosCompute

AtmosCompute is best suited for: Startups, Short training runs, Inference workloads. Support tiers range from Standard, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 4 active GPU listings on AtmosCompute, covering H100 80GB, A100 80GB, L40S, RTX 4090. For workloads requiring the highest single-GPU throughput, H100 SXM5 instances with NVLink interconnect deliver the best performance per dollar at scale. For cost-sensitive fine-tuning or inference of models up to 13B parameters, A100 40GB or RTX 4090 instances typically offer the best value.

AtmosCompute billing model and cost structure

AtmosCompute uses Pay-as-you-go pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot pricing is not currently available on this provider — all instances are on-demand. Reserved instance pricing, where available, can reduce costs by 30–60% for predictable long-running workloads. 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 AtmosCompute

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 — the 3,350 GB/s HBM3 bandwidth and 989 TFLOPS FP16 throughput make them 2–2.5× faster than A100 for transformer workloads. For inference of 7B–13B models in FP16 or BF16, A100 40GB offers the best cost-per-token on most providers. RTX 4090 instances are ideal 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 AtmosCompute pricing data is collected

Prices shown are sourced from AtmosCompute'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 retained for 90 days 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 14+ other providers. The cheapest GPU cloud guide covers the break-even analysis between self-hosted and managed inference at different request volumes.

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On-demand from $0.600/hr — 4 GPU configurations available. Pay-as-you-go billing.

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