Jarvislabs vs Paperspace
Compare Paperspace and Jarvislabs for self-service GPU instances, committed capacity, and training and inference workloads.
The Verdict: Jarvislabs vs Paperspace
Paperspace combines Machines, Gradient Notebooks, and Deployments. Jarvislabs offers preconfigured Templates, root-access VMs, Serverless, and Managed Endpoints, with a path to committed capacity and H200 SXM InfiniBand clusters. Compare the development environment, serving workflow, GPU configuration, and complete cost for your project.
Choose Jarvislabs if:
- +You want preconfigured Templates or full root access through VMs
- +You need autoscaling inference or Managed Endpoints for supported models
- +You want a path to committed capacity and H200 SXM InfiniBand clusters
Choose Paperspace if:
- +Managed Gradient Notebooks
- +Gradient Deployments for container serving
- +Paperspace Machines within the DigitalOcean portfolio
What you can build on Jarvislabs
VMs and Templates
Use a VM with full root access for your own kernel and Docker stack, or start with a preconfigured Template. GPU quantities depend on the model and region, with up to 8 GPUs per instance.
Explore VMsServerless inference
Deploy an OpenAI-compatible API with vLLM, SGLang, or Ollama. Workers autoscale and can scale to zero. GPU runtime is billed per worker-minute; retained storage continues billing. Currently in beta.
Explore ServerlessManaged Endpoints
Deploy a supported model with the GPU, serving image, precision, and parallelism selected and maintained by the Jarvislabs team.
How Managed Endpoints workPersistent filesystems
Keep datasets, checkpoints, and weights outside an instance’s lifecycle. A filesystem can attach to multiple instances in your account within its region. Storage is billed separately.
Explore filesystemsVM networking
Connect VMs privately with a VPC, control public ingress with Security Groups, and retain a public address with a Reserved IP. Reserved IPs are billed separately; cross-cloud private connectivity is not currently offered.
Explore networking capabilitiesCLI and Python SDK
Automate availability checks, launches, file transfers, training scripts, logs, and pause/resume workflows through the jl CLI and Python SDK.
Explore developer toolsGPU instances are available in Europe and India, with GPU types and quantities varying by region. Serverless, network filesystems, and VPC, Security Group, and Reserved IP features currently run in India regions. Choose deployment location by latency, data residency, and available features, wherever your team is based.
From self-service instances to committed capacity
Launch self-service instances with up to 8 GPUs per instance, or talk to Jarvislabs about committed capacity and H200 SXM InfiniBand clusters with shared storage for multi-node training. Share your GPU count, workload, preferred region, start date, and duration so sales can confirm a supported configuration, availability, and terms.
GPU Pricing Comparison
On-demand compute rates in USD per GPU-hour. These are not Serverless or cluster quotes. Compare CPU, RAM, storage, networking, taxes, and commitment terms for the total deployment cost.
Paperspace hourly Machine rates; H100 $5.95/hr and A100 80GB $3.18/hr. Storage and other add-ons are separate. Notebook subscriptions and free machine eligibility are different products.
| GPU | Jarvislabs | Paperspace | GPU rate difference |
|---|---|---|---|
| H100 80GB | $2.69/hrSXM | $5.95/hr | 55% less |
| A100 80GB | $1.49/hr | $3.18/hr | 53% less |
Rates checked: September 5, 2026. Paperspace pricing source. See Jarvislabs pricing for applicable rates and plans. A lower GPU-hour rate is not a measured performance or total-cost advantage.
Feature Comparison
| Feature | Jarvislabs | Paperspace |
|---|---|---|
| Instance billing | Per-minute | per hour |
| Persistent Storage | Available (billed separately) | Yes |
| Inference options | Serverless (beta), Managed Endpoints, or your own VM stack | Gradient Deployments with autoscaling |
| GPUs per instance | Up to 8; varies by GPU and region | Varies by instance configuration |
| GPU instance locations | Europe, India | US, Europe |
| Multi-node capacity | H200 SXM InfiniBand clusters; contact sales | Multi-GPU Machines; confirm multi-node requirements |
| Community Cloud | No (dedicated only) | No |
Paperspace: Strengths and Considerations
Strengths
- +Managed Gradient Notebooks
- +Gradient Deployments for container serving
- +Paperspace Machines within the DigitalOcean portfolio
Considerations
- -Notebook plan eligibility differs from Machine pricing
- -Storage and public IPs can keep billing while compute is stopped
- -Verify GPU availability for the selected region and product
Frequently Asked Questions
How do instance prices compare?
Jarvislabs H100 SXM is $2.69/hr and A100 80GB is $1.49/hr. Paperspace lists H100 at $5.95/hr and A100 80GB at $3.18/hr. These are compute rates; include storage, networking add-ons, and any subscription or commitment terms.
Does Paperspace have a free tier?
Paperspace lists limited free notebook machine options tied to subscription eligibility. These are distinct from paid GPU Machine rates. Jarvislabs provides paid self-service GPU instances; compare the GPU and environment your work actually requires.
Which is better for getting started?
Jarvislabs Templates provide prepared environments with tools such as JupyterLab and SSH. VMs provide root access for a custom stack. Paperspace Gradient Notebooks are an alternative for a notebook-centred workflow. Choose the environment that matches how you develop.
Can I deploy inference on Jarvislabs?
Yes. Serverless offers autoscaling workers and an OpenAI-compatible API; Managed Endpoints provide maintained serving configurations for supported models. Paperspace documents container-based Gradient Deployments with autoscaling. Compare model compatibility and operational controls.
What keeps billing when compute stops?
Jarvislabs stops on-demand GPU charges when an instance is paused, while retained storage continues billing. Paperspace also stops Machine compute billing when powered off, with storage, IPs, and other retained resources billed separately. Delete unneeded resources after preserving your data.
Plan your workload
Start with self-service GPU instances, or discuss committed capacity and H200 SXM InfiniBand clusters with our team.
Get startedProduct comparison reviewed: September 5, 2026 · View all comparisons