Google Cloud is the hyperscaler that thinks most like an AI company: Vertex AI, TPUs, GKE with excellent GPU support, and the A3 (H100), A3 Ultra (H200) and A4 (B200) machine families. For teams inside the Google ecosystem, or teams that want TPUs, it is the obvious home.
packet.ai is a GPU cloud that competes with exactly one part of that: the NVIDIA GPU-hour. On that line it is three to four times cheaper, sells a single card rather than a whole VM shape, and does not need a reservation. This comparison sets out both sides plainly.
Platform overview: packet.ai vs Google Cloud
What Google Cloud offers
Google Cloud prices GPUs as part of a VM. Representative us-central1 on-demand figures: a3-highgpu-1g (1× H100 80GB) around $11.06/hr as a whole VM, with trackers showing H100 from about $14.19 per GPU-hour on-demand and spot from $1.52; a2-ultragpu-1g (1× A100 80GB) about $5.07/hr; a4-highgpu-8g (8× B200) normalised to about $16.11 per GPU-hour, available mainly through reservations, Spot or Flex-start rather than plain on-demand. Committed-use discounts and Spot cut these substantially.
- Whole-VM pricing bundles vCPU, RAM and local SSD with the GPU
- Quotas and, for A4/B200, reservation or Flex-start scheduling required
- Egress roughly $0.08–0.12/GB depending on destination and volume
- TPU v5e/v6e/Ironwood as an alternative to NVIDIA for training and inference; Vertex AI and GKE integration
What packet.ai delivers
Google Cloud prices GPUs bundled into a VM shape, so the bill includes vCPU, RAM and local SSD whether the workload needs them or not, and the highest-end card requires a reservation, Spot bid or Flex-start scheduling to get at all. packet.ai sells the GPU by itself, on demand, hourly.
Normalised to a per-GPU basis, the gap is the widest of any comparison on this list: B200 at $3.75/hr Dynamic against roughly $16.11/hr on Google's a4 shape, a 77% difference. A100 80GB at $1.43/hr Dedicated is 72% below the $5.07/hr a2-ultragpu-1g VM. Even Google's interruptible Spot H100 at around $1.52/hr is a dollar more than packet.ai's non-interruptible A100.
- Dynamic: scheduler-enforced multi-tenant, no reservation, no Flex-start queue. B200 starting at $3.75/hr, 77% below Google's normalised a4 rate.
- Dedicated: a whole card, single-tenant, 99% SLA, no bundled VM overhead. RTX 4090 at $0.39/hr, L40S at $0.92/hr, A100 80GB at $1.43/hr, B200 at $6.99/hr.
What Google has that packet.ai does not: TPUs at a genuinely different price-performance point for training and large-batch inference, Vertex AI for the full ML lifecycle, and GKE's mature accelerator support. If those are why you are on Google Cloud, the NVIDIA GPU rate is a secondary line item. If you just need an NVIDIA card without a reservation queue or bundled VM cost, packet.ai is built for that.
Billing is hourly with monthly commits up to 20% off. There are no platform fees and no ingress charges; egress is $0.04/GB. Up to 2 TB of local NVMe per node is included. Dynamic instances are SSH-ready in under five minutes, Dedicated in five to ten. Capacity is live in the US (California, Virginia, Texas, Oregon) and Europe (Frankfurt, Amsterdam, Paris, London, Dublin). H100 SXM, H200 and RTX 5090 are on the notify list.
Against Google Cloud, packet.ai's B200 at $3.75/hr Dynamic is roughly 77% below GCP's normalised A4 rate, and its A100 80GB at $1.43/hr is 72% below a2-ultragpu-1g. There is no reservation, no quota request, and egress is $0.04/GB. What packet.ai does not have is TPUs, Vertex AI or GKE.
packet.ai vs Google Cloud at a glance
Published starting rates in USD per GPU-hour, on-demand unless noted. Percentages compare the first price in each cell.
| Category | packet.aiYOU | Google Cloud |
|---|---|---|
| B200 (180–192GB) | $3.75/hr Dynamic · $6.99/hr Dedicated−77% | ~$16.11/hr per GPU (a4-highgpu-8g, reservation/Flex-start) |
| A100 80GB | $1.43/hr Dedicated−72% | ~$5.07/hr (a2-ultragpu-1g VM) |
| L40S (48GB) | $0.92/hr Dedicated | Not offered (L4 24GB instead) |
| RTX 6000 Pro (96GB) | $0.66/hr Dynamic | Not offered |
| RTX 4090 (24GB) | $0.39/hr Dedicated | Not offered |
| H100 (80GB) | Launching soon (notify list) | ~$11.06/hr (a3-highgpu-1g VM) · spot from ~$1.52 |
| Unit of purchase | 1 GPU, hourly | VM shape (GPU + vCPU + RAM + SSD) |
| Access | Self-serve, no quota approval | Quotas; A4 mainly via reservation or Flex-start |
| Discount model | Monthly up to 20% off; clusters ~30% below retail | Committed-use discounts, Spot, Flex-start |
| Egress | $0.04/GB, no ingress fee | ~$0.08–0.12/GB |
| Alternative accelerators | None (NVIDIA only) | TPU v5e, v6e Trillium, Ironwood |
| Adjacent platform | Object storage, Token Factory (waitlist), Pixel Factory | Vertex AI, GKE, BigQuery, Cloud Storage |
Detailed comparison
Per-GPU cost
Normalise Google's VM prices to the GPU and the gap is stark: about $16.11 per B200-hour on a4 versus $3.75 on packet.ai Dynamic, about $5.07 per A100 80GB-hour versus $1.43. Even Google's Spot H100 at roughly $1.52/hr, which is genuinely cheap, is interruptible and quota-gated; packet.ai's on-demand A100 is a dollar less than that and stays up.
Committed-use discounts of one or three years bring GCP down materially, and for a company already spending millions with Google, negotiated rates change everything. On list price, though, there is no card where GCP is close.
Reservations, quotas and Flex-start
Getting a B200 on Google Cloud today generally means a reservation, Spot capacity, or Flex-start (a queued scheduling mode that finds you capacity within a window). That is fine for planned training and poor for 'I need a card now'. packet.ai's Dynamic B200 is SSH-ready in under five minutes, with no quota conversation.
For A100 and H100 the friction is lower but still there: GPU quotas per region, per family, and the whole-VM shape means you pay for the CPU and RAM Google decided go with the card.
TPUs and the platform
This is where Google Cloud is not really comparable. TPUs offer a different price-performance curve for training and large-batch inference, Vertex AI wraps the whole ML lifecycle, and GKE's GPU and TPU support is the most mature managed Kubernetes for accelerators. If those are the reasons you are on GCP, the NVIDIA GPU price is a secondary concern.
packet.ai has none of that. It has NVIDIA cards, an API, a CLI, object storage and a per-token API on a waitlist. Teams that want the Google platform but not the GPU bill sometimes run the control plane on GCP and the GPU-hours on packet.ai, moving weights and results over an egress line that costs $0.04/GB on the way back.
Who should choose which
Choose packet.ai if
- You want one or a few GPUs on-demand without a quota increase or reservation
- GPU-hours are your primary cost and the 60–77% rate difference matters at your scale
- You do not need TPUs, hyperscaler compliance controls, or the GCP service mesh
- Egress cost from GCP has become a constraint and you want $0.04/GB
Choose Google Cloud if
- You need TPUs (v5 or v6) for JAX or large-scale transformer training
- Your workload requires hyperscaler compliance controls (FedRAMP, HIPAA, ISO, SOC)
- You are running multi-thousand-GPU training jobs that need the A3 or A4 cluster at hyperscaler scale
- Your data is already in GCS and the data gravity makes GCP the path of least resistance
Conclusion
Google Cloud is an AI platform with GPUs attached; packet.ai is GPUs. On the NVIDIA GPU-hour packet.ai is three to four times cheaper at list, with none of the reservation friction. On TPUs, Vertex and GKE, Google is alone. Choose by which of those you are actually buying.
Same silicon. Smarter economics.
Every rate on this page is a published starting price you can deploy against today. Dynamic launches in under five minutes; no credit card to start.
Additional resources
- packet.ai GPU pricing: the full Dynamic, Dedicated and Clusters rate card.
- packet.ai documentation: API, CLI and platform guides.
- How intelligent scheduling works: why Dynamic keeps peak performance at a lower price.
- Clusters and wholesale quotes: multi-node InfiniBand capacity at ~30% below retail.
- Rent GPUs on packet.ai: specs, pricing and workload fit for every card.