How to Compare Cloud Providers for Your Project: Compute, Storage, Egress, GPUs and Regions

Start by deciding which category your project actually needs, because the candidate list changes completely depending on the answer. A general-purpose cloud (AWS, Google Cloud, Hetzner, DigitalOcean) fits web apps, databases and containers; a GPU rental marketplace fits training and inference; an LLM API fits teams that don't want to manage GPUs at all. GetDeploying tracks 116 providers across 136 countries, with 5,422 GPU prices, so the site is built for narrowing that list rather than browsing it. Once you know your category, compare candidates on four quantifiable axes — compute, storage, egress and region — then verify GPU availability and price terms before committing.

Step 1: Classify your workload before comparing anything

Mixing these up is the most common filtering mistake. Comparing a $5 VPS against an H100 rental tells you nothing, and an LLM API price per token isn't comparable to a GPU hourly rate unless you do the throughput math yourself.

Step 2: Compare on the dimensions that actually move your bill

Compute

For non-GPU workloads, compare the instance price at the size you'll actually run, not the entry tier. Note whether the listed price is on-demand, reserved, spot or quote-only — GetDeploying's GPU methodology states a clear preference order: in-stock first, then on-demand ahead of reserved, spot and quote-only terms, taking the cheapest listing that fits. Apply the same discipline to your own comparison.

Storage and egress

Object storage is usually priced per GB stored plus per GB retrieved, and egress is where bills surprise people. GetDeploying compares object storage and data egress across providers, which is the right place to check whether a cheap compute price is offset by expensive outbound traffic.

Price terms and units

• Prices are shown in USD, converted at daily reference rates where a provider publishes in another currency.
• A month means 720 hours — use the same convention so monthly estimates are comparable.
• Affiliate links are marked on the site, and the site states commissions never affect ordering. Treat marked links as commercial, not as a ranking signal.

Step 3: Check regions and latency

Confirm the provider has a datacenter in or near your users' region before you fall in love with the price. A provider with no node in your target country means every request crosses a border, which shows up as latency and possibly as egress cost. GetDeploying's region coverage spans 136 countries, so filter by region first, then compare price within that filtered set.

Step 4: Verify GPU availability, not just the headline price

The cheapest listed GPU price is often not purchasable. GetDeploying labels listings that aren't straightforwardly available: sold out, waitlist, spot instance, a reservation term, or On request where no price is published. When nothing better exists, the site shows the next best offer with that label rather than hiding it.

Current examples from the site (per GPU per hour, cheapest listing shown):

Two caveats when reading this table. First, the provider count is how many companies list the model on any terms, so a high count doesn't mean 57 providers have it in stock today. Second, the "in-demand" list is ranked by visits to each model's page over the last 90 days, then by how many providers list it — it is a popularity ranking, not a cheapest-six ranking.

On price direction: cloud GPU rental prices are up 10% over the year according to the site's trend data, with a recent move of +2.3% over 4 weeks. If you're budgeting for a long rental, check the trend page rather than assuming today's rate holds.

Step 5: Narrow to two or three with side-by-side comparison

GetDeploying supports comparing any two of the 116 clouds on pricing, regions, egress, GPUs and managed services. Use it to reduce your shortlist, then decide on the things a comparison table can't show: managed service depth, deployment model (containers vs VMs vs bare metal), and how much operational work you're willing to take on.

Popular pairings on the site suggest the natural fault lines: AWS vs Google Cloud (hyperscaler feature depth), DigitalOcean vs Hetzner (developer-friendly VPS vs low-cost VPS), Hetzner vs Hostinger and Hostinger vs Vercel (budget hosting vs deployment platform), Fal.ai vs Replicate (hosted model inference).

Common sticking points

• Comparing list prices across terms. A spot price and a reserved price aren't the same product. Normalize to on-demand first, then evaluate whether you can tolerate interruption for the discount.
• Ignoring egress until the first invoice. Compute is the visible number; egress is the one that scales with success.
• Trusting a GPU price without checking the label. Sold out, waitlist and on-request listings are not capacity you can rent today.
• Forgetting the 720-hour convention. A "monthly" figure computed at 730 or 744 hours won't match the provider's own math.
• Assuming a comparison site's ordering is neutral. Check whether links are marked as affiliate and read the stated methodology before treating rank as a recommendation.

What to do next

1. Classify your workload using the table in Step 1.
2. Filter providers by region.
3. Compare compute, storage and egress at your real usage size, normalizing price terms.
4. For GPUs, check the availability label before the price.
5. Run two or three side-by-side comparisons, then pick based on managed services and deployment fit — not on the lowest single number.