Jarvislabs vs Lambda
Compare Lambda and Jarvislabs for self-service GPU instances, committed capacity, and training and inference workloads.
The Verdict: Jarvislabs vs Lambda
Jarvislabs offers H100 SXM at $2.69/GPU-hour; Lambda lists its single-GPU H100 SXM at $4.29/GPU-hour. Both support self-service instances and multi-node training options. Jarvislabs also offers Templates, Serverless, and Managed Endpoints, while Lambda offers Lambda Stack and reserved InfiniBand clusters. Compare the full instance specification and the training or serving workflow you need.
Choose Jarvislabs if:
- +You want H100 SXM instances plus a choice of VMs and preconfigured Templates
- +You need Serverless or Managed Endpoints as well as training infrastructure
- +You want committed capacity or H200 SXM InfiniBand clusters
Choose Lambda if:
- +Lambda Stack for deep learning environments
- +Self-service GPU instances, including B200
- +Reserved InfiniBand clusters
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 Lambda'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.
Lambda H100 uses the 1-GPU SXM rate of $4.29/hr, matching the Jarvislabs SXM variant. Lambda also lists PCIe at $3.29/hr and lower per-GPU SXM rates for larger instances. CPU, RAM, and storage allocations differ.
| GPU | Jarvislabs | Lambda | GPU rate difference |
|---|---|---|---|
| H100 80GB | $2.69/hrSXM | $4.29/hrSXM, 1 GPU | 37% less |
| A100 40GB | $0.89/hr | $1.99/hr | 55% less |
Rates checked: September 5, 2026. Lambda 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 | Lambda |
|---|---|---|
| Instance billing | Per-minute | per minute |
| Persistent Storage | Available (billed separately) | Yes |
| Inference options | Serverless (beta), Managed Endpoints, or your own VM stack | Run your own serving stack on instances |
| GPUs per instance | Up to 8; varies by GPU and region | Varies by instance configuration |
| GPU instance locations | Europe, India | US, Europe, Asia |
| Multi-node capacity | H200 SXM InfiniBand clusters; contact sales | Reserved InfiniBand clusters |
| Community Cloud | No (dedicated only) | No |
Lambda: Strengths and Considerations
Strengths
- +Lambda Stack for deep learning environments
- +Self-service GPU instances, including B200
- +Reserved InfiniBand clusters
Considerations
- -On-demand inventory is first-come access
- -Instance and cluster prices use different configurations and terms
- -Compare the serving software you must operate yourself
Frequently Asked Questions
Is Lambda cheaper than Jarvislabs?
At the listed single-GPU on-demand rates, Jarvislabs H100 SXM is $2.69/hr and Lambda H100 SXM is $4.29/hr. Lambda's $3.29/hr H100 is PCIe. Lambda has other rates for larger instances and reserved clusters; compare CPU, RAM, storage, GPU count, and commitment terms as well as the GPU-hour rate.
Can I get H100s immediately?
Self-service capacity is subject to inventory on both platforms. Check the selected region and GPU count before planning a run. For a scheduled project, discuss capacity and start dates with the provider.
Which is better for LLM training?
Jarvislabs offers Templates for prepared environments, VMs for custom stacks, and H200 SXM InfiniBand clusters for multi-node training. Lambda offers Lambda Stack and InfiniBand clusters. Select based on GPU memory, topology, data placement, and the software environment your training job requires.
Does Jarvislabs offer more than training instances?
Yes. Serverless provides OpenAI-compatible inference with autoscaling and scale-to-zero, while Managed Endpoints use serving configurations maintained by the Jarvislabs team for supported models. The CLI and Python SDK automate instance workflows.
How should I choose a deployment region?
Choose by workload location, data residency, latency, and available GPUs. Jarvislabs has GPU instances in Europe and India; features and quantities vary by region. Serverless, network filesystems, and VM networking currently have narrower regional availability. Confirm the complete deployment before moving 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