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Lambda is the reserved-capacity GPU cloud most ML engineers know. They offer mid-range NVIDIA GPUs on demand and reserved. GigaGPU rents the same RTX 5060 Ti as a dedicated bare-metal monthly server. Same silicon, different billing.
For RTX 5060 Ti-class workloads, GigaGPU dedicated at £119/mo wins above ~5 hours/day of GPU-time. Below that, Lambda on-demand is cheaper. For 24/7 inference, GigaGPU saves ~50%. For multi-week experiments, Lambda Reserved competes.
Current rates
- Lambda RTX 5060 Ti class on-demand: ~$0.30/hr
- GigaGPU RTX 5060 Ti 16 GB dedicated: £119/month flat
- Lambda 1-year reserved equivalent: ~$140/mo (about £110)
Break-even hours per day
Against Lambda on-demand at $0.30/hr (~£0.24/hr):
- £119 / £0.24 = ~705 hours/month break-even
- = ~23 hours/day
- Below 23h/day, Lambda is cheaper
- Above 23h/day (effectively always-on), GigaGPU is cheaper
The break-even is closer than for the 4090 because the 5060 Ti is closer to Lambda’s per-hour rate.
By workload
- 24/7 inference — GigaGPU. Slightly cheaper, no cold start, EU residency.
- Business-hours only — Lambda on-demand. Idle hours actually save money.
- Long fine-tuning runs — GigaGPU. Predictable bill regardless of run length.
- 1-year reserved capacity — Lambda Reserved is meaningfully cheaper than GigaGPU monthly.
- UK / EU data residency — GigaGPU only.
Verdict
For a steady-state inference deployment, GigaGPU dedicated at £119/mo is the right answer. For occasional or business-hours-only workloads, Lambda on-demand. For genuinely steady 1-year commitments, Lambda Reserved is competitive on price but costs you data residency.
Bottom line
The 5060 Ti is the cheapest tier where the "always-on dedicated" vs "per-hour cloud" calculation is genuinely close. Pick by traffic shape and residency. See serverless vs dedicated for the broader analysis.
