Table of Contents
Paperspace had a clean run as the "Heroku for GPUs" from roughly 2019 to 2024 — flexible Gradient notebooks, sane pricing, decent UX. Since the DigitalOcean acquisition in mid-2023 and the subsequent pricing changes, a steady stream of customers has been looking for alternatives. This guide covers the strongest options across three deployment shapes.
For steady production inference, the strongest Paperspace replacement is a dedicated GPU rental like GigaGPU — fixed monthly, no usage cliff, full root. For spiky on-demand workloads, RunPod or Modal. For managed notebooks, Lightning AI Studio or Hyperbolic. None matches Paperspace's pre-DO Gradient experience exactly, but every one of them is more cost-predictable today.
Why people are leaving Paperspace
The recurring complaints we hear from customers who migrated to dedicated hardware:
- Prices changed mid-cycle. Multiple GPU SKUs had per-hour rates raised significantly post-acquisition.
- Capacity shortages on flagship cards. A100/H100 availability is intermittent during peak hours.
- Long-running jobs are expensive. Gradient's hourly billing is fine for notebooks but punitive for production inference at 24/7 runtime.
- Limited region selection. EU data residency is non-trivial.
- Documentation has stagnated since the DO transition; community feels less responsive.
None of these are deal-breakers for every workload, but if any of them describes your situation, an alternative is worth evaluating.
Dedicated alternatives — own the box
GigaGPU
Bare-metal dedicated GPU servers in the UK. Fixed monthly pricing, no per-hour billing, full root access. RTX 3050 at £79/mo through RTX 6000 Pro 96 GB at £1,099. Best for steady-state production inference and long fine-tuning runs. Catalogue.
Hetzner GPU
German hosting. Limited GPU SKU availability, focused on RTX 4000 SFF Ada and RTX 6000 Ada. Excellent value when in-stock; long lead times when not.
OVH GPU
French hosting, larger range. T4, V100, A100. Pricing is competitive but provisioning is slower than dedicated US hosts.
Lambda Labs Reserved
Reserved GPU instances on Lambda. H100 SXM clusters available. Higher price point; worth it for serious training. The on-demand tier is closer to RunPod.
Serverless alternatives — pay-per-second
RunPod
The biggest serverless GPU player. Multiple region/SKU combinations including RTX 4090, A100, H100. Cold start 5–60 s. Per-second pricing. Best Paperspace replacement if your workload is genuinely intermittent. See our RunPod alternatives for the further breakdown.
Modal
Python-first serverless. Decorator-based deployment (@modal.function). Best for ML engineers who want code-as-config and do not want to think about containers. Pricier than RunPod per second.
Replicate
Model-marketplace + serverless. Strong for off-the-shelf inference (SDXL, FLUX, Whisper). Less good if you need custom models.
Banana / Cerebrium
Smaller serverless players, both with reasonable Python integrations. Worth shortlisting if RunPod / Modal do not fit.
Managed notebook alternatives
If what you actually wanted from Paperspace was the Gradient notebook experience, the closest replacements:
- Lightning AI Studio — Jupyter-style notebook with shareable Studios. GPU-backed, similar pricing model to Gradient.
- Hyperbolic — newer, focused on ML notebooks with a generous free tier.
- Google Colab Pro+ — still the cheapest if you are OK with Google ecosystem.
- Kaggle Notebooks — free, time-limited GPUs. Fine for experimentation.
Side-by-side feature matrix
| Provider | Pricing model | EU presence | Cold start | Data control |
|---|---|---|---|---|
| Paperspace | Per-hour / per-month | Limited | n/a (always-on) | Decent |
| GigaGPU | Fixed monthly | UK / EU | None — bare metal | Full root |
| RunPod | Per-second + per-hour | Multi-region | 5–60 s | Good |
| Modal | Per-second | US / EU | 5–30 s | Good |
| Replicate | Per-second | US-centric | 5–60 s | Limited |
| Lightning AI | Per-hour | US / EU | n/a | Decent |
| Hetzner | Fixed monthly | EU only | None | Full root |
| Lambda Reserved | Per-month commit | US-centric | None | Full root |
Which alternative for which workload
- Production inference, steady traffic → GigaGPU dedicated, Hetzner, or Lambda Reserved.
- Long fine-tuning runs → GigaGPU dedicated or Lambda Reserved. Per-second billing for a 5-day training is ruinous.
- Spiky inference → RunPod or Modal.
- Off-the-shelf model serving → Replicate or Together AI.
- Notebook / research → Lightning AI Studio or Hyperbolic.
- Free tier / experimentation → Colab Pro+ or Kaggle.
Bottom line
Paperspace replaced one shape of GPU hosting (managed notebooks + flexible billing) and the alternatives have specialised. Pick by your traffic shape: steady → dedicated, spiky → serverless, learning → notebook. If you are moving production inference workloads, dedicated is almost always the right answer for cost predictability and data control.
For more comparisons see RunPod alternatives, Together AI alternatives, and Vast.ai alternatives.
