GPU Cloud Infrastructure
VESSL AI vs Paperspace (DigitalOcean) for persistent GPU workspaces and team collaboration
8 min read
Quick Answer: The best overall choice for persistent GPU workspaces and serious team collaboration is VESSL AI. If your priority is low-friction, notebook-style individual workspaces with simple UI, Paperspace (DigitalOcean) is often a stronger fit. For small teams that mostly need occasional shared access and basic persistence, consider Paperspace + Git/cloud storage as a lightweight baseline.
Persistent GPU workspaces sound simple—“give my team a box that never dies”—but under load they turn into a capacity and reliability problem. Quotas, spot preemptions, and regional outages will eventually hit you. The real question isn’t just “whose notebook UI is nicer?” It’s “who keeps your work running when the underlying cloud falls over, and who lets your team scale without babysitting jobs?”
VESSL and Paperspace answer that in very different ways.
At-a-Glance Comparison
| Rank | Option | Best For | Primary Strength | Watch Out For |
|---|---|---|---|---|
| 1 | VESSL AI | Teams running multi-GPU or multi-cloud workloads that must stay up | Unified multi-cloud GPU control plane with auto failover and storage | Less “playground” feel than classic notebook-centric tools |
| 2 | Paperspace (DigitalOcean) | Individuals and small teams that want simple, persistent GPU desktops/notebooks | Easy persistent “machine” model and familiar dev UX | Tied to a single provider; no built-in multi-cloud failover; scaling beyond a few users gets manual |
| 3 | Paperspace + Git/Cloud Storage | Budget-conscious teams with infrequent collaboration | Minimal setup; use existing Git/S3/GDrive for “persistence” | No real orchestration, access control, or HA; collaboration is mostly manual process |
Comparison Criteria
We evaluated each option against the following criteria to ensure a fair comparison:
-
Persistent GPU workspaces:
How reliably you can keep environments alive across reboots, preemptions, and provider issues—without losing data or spending hours reconfiguring. -
Team collaboration & control:
How well the platform supports multi-user projects: shared storage, role-based access, project-level isolation, monitoring, and “who owns what” clarity. -
Scalability & reliability under real constraints:
What happens when you go from 1 to 10–100 GPUs, or when a region/provider fails. Can you move work, fail over, and keep shipping, or do you stall on quotas and outages?
Detailed Breakdown
1. VESSL AI (Best overall for multi-user, multi-cloud persistent workspaces)
VESSL AI ranks as the top choice because it treats persistent workspaces as an orchestration problem, not just a VM problem—unifying GPUs across providers and adding auto failover, shared storage, and multi-cluster visibility.
What it does well:
-
Multi-cloud, persistent compute that survives provider issues
VESSL AI is a GPU liquidity and orchestration layer, not a single-cloud “GPU IDE.” You get:- Access to A100, H100, H200, B200, GB200, B300 across multiple providers through one platform.
- Three operational modes (Spot, On-Demand, Reserved) so you can match cost and risk:
- Spot for experimentation and non-critical notebooks that can be preempted.
- On-Demand with Auto Failover for production-like sessions that must stay up even when a provider or region fails.
- Reserved capacity for teams that need guaranteed GPU pools with dedicated support.
- Auto Failover: if a cloud provider blips, VESSL can seamlessly switch to another provider/region and keep workloads running rather than leaving your “persistent” machine stuck.
-
Team-ready storage and shared workspaces
Persistent workspaces aren’t persistent if you lose data when the GPU changes. VESSL solves this as a first-class primitive:- Cluster Storage: shared, high-performance file system across jobs and users. Your workspace data lives here, not inside one fragile VM.
- Object Storage: lower-cost storage for datasets, checkpoints, and artifacts, accessible across projects and runs.
- Researchers can treat GPU sessions as “throwaway” compute and keep state in shared storage—this is how you get fire-and-forget behavior instead of “never shut down that one machine.”
-
Collaboration built for teams, not just individuals
VESSL’s core unit is the team and project, not the lone developer:- Unified Web Console for visual cluster and workspace management.
- CLI (
vessl run) for native workflows, CI/CD integration, and scripted submissions. - Per-project visibility into runs, logs, metrics—everyone sees the same timeline instead of guessing what’s running on whose machine.
- Enterprise-ready features like SOC 2 Type II and ISO 27001, plus talk-to-sales support for SLAs, onboarding, and custom integrations.
-
Scales from 1 to 100 GPUs without new tooling
The same platform that runs your “single GPU notebook” also:- Scales to multi-node, multi-GPU jobs for LLM post-training, Physical AI, and AI-for-Science.
- Survives regional outages via Multi-Cluster: one unified view even when you’re spanning providers and locations.
- Keeps monitoring and “job wrangling” overhead low—BAIR researchers explicitly credit VESSL with letting them run more “fire-and-forget” experiments and spend less time chasing stalled jobs.
Tradeoffs & Limitations:
- Less of a “personal dev box” vibe
VESSL is opinionated about separating compute from state. Compared to something like Paperspace “machines”:- You won’t treat a GPU instance as your long-lived desktop; you treat it as a disposable runtime attached to persistent storage.
- That’s a win for reliability and team workflows, but it’s different from the “I have my own box and I never rebuild it” mental model.
- If all you want is a single, always-on GPU VM that you SSH into like a personal workstation, VESSL may feel like more orchestration than you strictly need.
Decision Trigger: Choose VESSL AI if you want persistent GPU workspaces that survive provider issues, shared storage that outlives any single machine, and a multi-user control plane that scales from 1 to 100 GPUs without turning your team into full-time cluster operators.
2. Paperspace (DigitalOcean) (Best for simple, persistent individual workspaces)
Paperspace (DigitalOcean) is the strongest fit if your main goal is straightforward, persistent GPU “machines” or notebooks for individuals or small teams, all on a single cloud backend.
What it does well:
-
Familiar “my machine in the cloud” model
Paperspace popularized the idea of a cloud GPU desktop:- You spin up a persistent VM (Gradient Notebook or Core VM), install your tools once, and come back to the same environment later.
- For solo developers, this is intuitive: your conda env, your dotfiles, your code—all living inside one always-on instance.
- Great for GPU-accelerated dev environments, demo servers, and long-running notebooks.
-
Low-friction UI for notebooks and experiments
Especially with Gradient:- You get browser-based Jupyter-like notebooks, templates, and quick-start projects.
- Good fit for prototyping, teaching, or teams where each person needs their own workspace more than they need deep collaboration features.
- If you’re new to GPUs, the Paperspace UX is approachable.
Tradeoffs & Limitations:
-
Single-provider, limited failover story
Because Paperspace is effectively one provider’s GPU fleet:- If that provider hits capacity issues, regional outages, or SKU shortages, you wait.
- There’s no built-in multi-cloud failover or multi-cluster abstraction—you can’t seamlessly redirect workloads to a different vendor without changing tools.
- Persistent machines are persistent only as long as that region and VM stay healthy.
-
Collaboration is mostly “share the machine”
For real teams, this gets messy:- You often end up with one “big box” that everyone SSHes into, or each person has their own machine with slightly different environments.
- No unified cross-provider view, limited project-level isolation, and less emphasis on team-wide observability and run history.
- Scaling to dozens of users becomes manual GPU assignment and calendar coordination.
Decision Trigger: Choose Paperspace (DigitalOcean) if your priority is simple, persistent GPU workspaces for individuals, you’re comfortable staying on a single provider, and you don’t yet need multi-cloud failover or a strong team control plane.
3. Paperspace + Git/Cloud Storage (Best for low-budget, lightweight collaboration)
Paperspace + Git/Cloud Storage stands out when your team is small, budgets are tight, and you can lean on existing tools (GitHub, S3, GCS, Drive) for persistence and collaboration.
What it does well:
-
Extremely simple setup
- Each person has a Paperspace GPU VM or notebook.
- Code is versioned in Git; datasets and checkpoints live in S3/MinIO/Drive or a shared bucket.
- Collaboration happens in PRs and shared folders, not in the GPU platform itself.
- Works fine for 2–3 people doing research or side projects.
-
Reuses tools your team already knows
- No new mental model for collaboration.
- CI, code review, and documentation live in the same places as your non-GPU projects.
Tradeoffs & Limitations:
- No real orchestration or high availability
- Each GPU VM is a snowflake; if it goes down, you rebuild manually.
- Outages, quota issues, and preemptions are your problem to handle.
- There is no Auto Failover, Multi-Cluster, or unified monitoring: it’s just VMs plus good habits.
Decision Trigger: Choose Paperspace + Git/Cloud Storage if you primarily need cheap, basic access to persistent GPU machines and are willing to handle collaboration, reliability, and orchestration with process and scripts instead of a dedicated control plane.
Final Verdict
If you care about persistent GPU workspaces only in the “my personal dev box” sense, Paperspace’s notebook and VM model is comfortable and fast to adopt. For a single developer or a very small team, it’s hard to argue with that simplicity.
But the moment your team needs:
- multi-GPU or multi-node training,
- guaranteed capacity during peak demand,
- protection against provider or regional failures, or
- a clean way to share data, track runs, and avoid “who’s using which box?” chaos,
you’re in orchestration territory, not just VM territory.
That’s where VESSL AI is the better long-term choice: it unifies GPUs across providers, builds persistence on top of shared storage instead of fragile machines, and gives teams a control surface—Web Console and CLI—that scales from 1 to 100 GPUs with Auto Failover, Multi-Cluster, and enterprise-ready security and support.
Use Paperspace when you want a simple GPU sandbox. Use VESSL AI when your team needs to stop chasing GPUs and start shipping AI on a platform designed for persistent, collaborative, and reliable workloads.