Google Cloud

Google Cloud GPU pricing overview

Google Cloud is a hyperscaler GPU cloud provider headquartered in Sunnyvale, CA. Google Cloud provides A100 and H100 GPU instances via Compute Engine and Vertex AI, with sustained use discounts and committed use contracts that can significantly cut hourly GPU rental costs. TPU v4 and v5 accelerators are also available for TensorFlow and JAX workloads, giving teams a unique alternative to NVIDIA hardware. Spanning 30+ regions, it is the top choice for ML pipelines deeply integrated with the TensorFlow and Google ecosystem. Billing is On-demand, Committed Use (1yr/3yr), Spot/Preemptible with a minimum commitment of None (on-demand). Available regions include us-central1, us-east4, europe-west4, asia-east1 and 1 more. On-demand GPU instances can be provisioned in minutes with no upfront cost, making Google Cloud suitable for both short-duration experiments and sustained production workloads.

Google Cloud vs other GPU providers

Google Cloud 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: Sustained use discounts; Vertex AI integration; TPU availability. Use the side-by-side comparison tool above to see Google Cloud pricing against any other provider across shared GPU models. For a broader market view, the live GPU prices table shows all 8 Google Cloud listings alongside 94+ providers in a single sortable view.

Best use cases for Google Cloud

Google Cloud is best suited for: ML training pipelines, TensorFlow workloads, Teams using GCP services. Support tiers range from Basic → Premium, making it viable for both individual researchers and enterprise teams with SLA requirements. There are currently 8 active GPU listings on Google Cloud, covering H100 80GB, A100 80GB, A100 40GB, A10G and more. 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.

Google Cloud billing model and cost structure

Google Cloud uses On-demand, Committed Use (1yr/3yr), Spot/Preemptible pricing. On-demand instances are billed per second or per hour depending on the instance type, with no termination fees. Spot (interruptible) instances are available from $0.15/hr — typically 40–70% cheaper than on-demand rates, suitable for fault-tolerant training jobs with checkpointing. 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 Google Cloud

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 Google Cloud pricing data is collected

Prices shown are sourced from Google Cloud'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.