Jarvislabs vs CoreWeave
Compare CoreWeave and Jarvislabs for self-service GPU instances, committed capacity, and training and inference workloads.
The Verdict: Jarvislabs vs CoreWeave
The main distinction is the operating model. Jarvislabs offers root-access VMs, Templates, Serverless, and Managed Endpoints, plus committed capacity and H200 SXM InfiniBand clusters. CoreWeave offers managed Kubernetes and HPC networking for large deployments. Compare orchestration, GPU topology, storage, support terms, and the full capacity quote rather than choosing by company size.
Choose Jarvislabs if:
- +You want root-access VMs without adopting a managed Kubernetes operating model
- +You want self-service inference or team-maintained model serving configurations
- +You need H200 SXM InfiniBand clusters or committed capacity
Choose CoreWeave if:
- +Managed Kubernetes for GPU workloads
- +InfiniBand and RoCE networking options
- +Large-scale cluster infrastructure
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.
See Jarvislabs capacity options and cluster capabilities. Compare with CoreWeave's cluster documentation.
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.
CoreWeave rates are per-GPU equivalents of whole 8-GPU nodes: H100 $49.24/hr, H200 $50.44/hr, and A100 80GB $21.60/hr. They are not single-GPU purchase prices; node resources and contracted rates differ.
| GPU | Jarvislabs | CoreWeave | GPU rate difference |
|---|---|---|---|
| H100 80GB | $2.69/hrSXM | $6.16/hrHGX, 8-GPU node | 56% less |
| H200 | $3.99/hr | $6.31/hr | 37% less |
| A100 80GB | $1.49/hr | $2.70/hr | 45% less |
Rates checked: September 5, 2026. CoreWeave 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 | CoreWeave |
|---|---|---|
| Instance billing | Per-minute | per hour |
| Persistent Storage | Available (billed separately) | Yes |
| Inference options | Serverless (beta), Managed Endpoints, or your own VM stack | Kubernetes-based serving and separate inference products |
| 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 | Managed Kubernetes with HPC interconnects |
| Community Cloud | No (dedicated only) | No |
CoreWeave: Strengths and Considerations
Strengths
- +Managed Kubernetes for GPU workloads
- +InfiniBand and RoCE networking options
- +Large-scale cluster infrastructure
Considerations
- -Compare whole-node costs with the GPU quantity you need
- -Kubernetes deployments require a compatible operating workflow
- -Capacity plans and contracted pricing need separate evaluation
Frequently Asked Questions
Does Jarvislabs require Kubernetes?
No. You can launch a VM or Template, or deploy inference through Serverless and Managed Endpoints. A Jarvislabs VM can join a Kubernetes cluster you operate; Jarvislabs does not operate that control plane. CoreWeave provides a managed Kubernetes service.
How do the H100 prices compare?
Jarvislabs H100 SXM is $2.69/GPU-hour for self-service instances. The listed CoreWeave HGX H100 node is $49.24/hour for eight GPUs, approximately $6.16 per GPU-hour. Compare complete node resources and negotiated capacity terms; the per-GPU figure is not a single-GPU CoreWeave offer.
Do both providers offer H200 capacity?
Yes. Jarvislabs offers H200 SXM instances and InfiniBand clusters. CoreWeave lists HGX H200 nodes. Confirm GPU count, interconnect, region, and availability for the deployment you need.
Which is better for a growing AI company?
Evaluate the workload and the operations your team wants to own. Jarvislabs supports self-service instances, managed inference workflows, and capacity discussions as demand grows. CoreWeave is an option for teams seeking managed Kubernetes and cluster infrastructure.
Do both providers support multi-node training?
Yes. Jarvislabs supports H200 SXM InfiniBand clusters with shared storage, confirmed through sales. CoreWeave supports distributed workloads through managed Kubernetes and HPC networking. Compare the supported topology, storage, capacity, and contract for your training run.
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