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Connect each candidate to a current official product and your criterion. An old tutorial, public rate and actual launch availability are different facts.
GPU-cloud research often starts with someone else’s ranking but ends with a specific resource decision. Maintain a short, verifiable list of product, inclusion basis, constraints and next test rather than transferring another project’s conditions.
Current to 1 November 2026, this method does not rank the entire market, use affiliate links or attribute unmeasured results to providers.
Define the selection unit first
Specify a compute instance, dedicated physical node, owned-code execution or ready model API. Their responsibilities and billing differ. One price column combining them gives little useful guidance.
Add mandatory memory, region, context, concurrency and team-capability requirements. Keep preferences separate. This excludes unsuitable offers without attempting to offset fundamental constraints through promotional discounts.
Check currency with four examples
| Candidate | Evidence of format | Do not assume |
|---|---|---|
| Runpod Pods | Documented GPU environment | Findings also apply to Serverless |
| Hetzner GEX | Dedicated GPU-server catalogue | It accepts your private cards |
| TensorDock | Platform and host network | Every host provides the same configuration |
| CUDO | Former-platform status | Retained documentation means open public on-demand |
These are teaching examples, not four winners. CUDO’s current website describes infrastructure; assess former and current products separately. Review any changed offering similarly.
Preserve evidence and open questions
For each candidate, record date, official URL, product, configuration and inclusion criterion. Identify availability, account, commitment, storage and function questions. Do not fill gaps from an old shortlist’s assumptions.
Retain location or tariff context for limited claims. Finding a device in a catalogue does not establish capacity on your date. This keeps the list a decision tool rather than a logo collection.
Compare only a few suitable options
A hypothetical team retains two candidates after constraint checks. It runs one model with matched settings and requests, measuring quality, latency, recovery, full expense and operational effort.
Findings apply to tested conditions. Expansion may need a new network or multi-device test. If evidence is insufficient, name the next experiment rather than produce a precise ranking from incompatible numbers.
Include your own resource
Add existing GPU equipment as another option with an honest estimate covering power, cooling, availability and support. Cloud retail prices do not forecast owner payment.
Read the pilot checklist and purchase-versus-rental comparison.