Table of Contents
Paperspace (acquired by DigitalOcean) offers GPU machines + ML workflow tools. For teams already on DigitalOcean or wanting integrated workflow tooling, it's a sensible choice. For pure dedicated GPU at lowest cost, specialist providers win.
Paperspace wins for: DigitalOcean-aligned shops, integrated ML workflow tools (Gradient), notebooks + GPU + deployment in one platform. Self-hosted dedicated (e.g., GigaGPU) wins for: lowest cost per consumer GPU, predictable monthly, full control. Paperspace is good middle ground; specialist hosts undercut on price.
Comparison
| Aspect | Paperspace | Specialist dedicated |
|---|---|---|
| Pricing | Hourly + monthly | Monthly fixed |
| ML workflow tooling | Integrated (Gradient) | You compose |
| Notebook integration | Native | You configure |
| Cost (RTX 4090 24/7) | ~£500/mo | ~£280/mo |
| Best for | DO-aligned, integrated ML platform | Cost-anchored production |
When each
- Paperspace: DO ecosystem alignment, want integrated notebook + GPU + deployment, ML workflow tooling matters
- Specialist dedicated: cost is the deciding factor, you compose your own ML stack, simple monthly billing
Verdict
Paperspace fills a real niche: integrated ML workflow + GPU. For pure cost-anchored deployments, specialist dedicated GPU providers undercut by 40-50%. Choice depends on whether you value the integrated platform or the cost saving. Both are credible; the right answer is workload-dependent.
Bottom line
Paperspace for integrated ML; specialists for cost. See Paperspace alternatives.
