Jarvislabs and Runpod both rent NVIDIA GPUs by the hour and both run serverless inference on top. The differences that matter are billing granularity, how much hardware you can choose from, and what each publishes about compliance.
Short version: Jarvislabs prices its headline on-demand rates aggressively and keeps the product surface small and legible. Runpod bills per second rather than per minute, offers a much wider GPU catalog across 31 global regions, and publishes the compliance documentation an enterprise security review asks for.
At a glance
Jarvislabs figures were read from their pricing, serverless and supported-countries pages on September 8, 2026. Rates move. Check their pricing page and ours before budgeting against either.
Where Jarvislabs is genuinely strong
Being straight about this first makes the rest worth reading.
The headline rates are competitive. On-demand, they publish H100 SXM at $2.69/hr, A100 80GB at $1.49/hr, RTX Pro 6000 Blackwell at $1.89/hr and L4 at $0.44/hr. Those are real numbers on a public rate card, no sales call required.
The product is legible. Eight GPU types, one pricing page, an INR and USD toggle, and a clear ladder from VMs to templates to serverless endpoints. If you find large catalogs paralyzing, a short menu is a feature.
They are strong in India and price for it. The currency toggle, the India-specific pages and the university program are not decoration. If your team, your billing and your users are all in India, that focus is worth weighing.
They publish fast start times. A template boots in 1.8 seconds and a VM in under 90.
Per second against per minute
This is the difference that shows up on every invoice.
Jarvislabs bills per minute, on both VMs and serverless worker runtime. Runpod bills per second, with no minimum.
On a long training run the difference rounds away. On short, bursty inference it does not. A serverless worker that handles a request in eleven seconds bills a full minute on per-minute pricing and eleven seconds on per-second. Repeat that across a day of traffic and you are paying for time no GPU spent working.
The same applies to experimentation. Stopping a pod after seven minutes of a failed run costs seven minutes here, not eight.
Where this does not matter: steady multi-hour training, or any workload where a machine stays up for days. If that is all you run, billing granularity is a rounding error and you should decide on other grounds.
GPU choice
Jarvislabs lists eight GPU types. Runpod's catalog runs to dozens across Pods and Serverless.
That matters less than it sounds if the card you want is on both lists. It matters a great deal if it is not. Runpod carries three distinct H100 variants – SXM, PCIe and NVL – which have different memory bandwidth and interconnect characteristics, and which you can select individually. It also carries B300, L40S, AMD MI300X, and a long tail of consumer cards for smaller jobs where an H100 is expensive overkill.
The practical version: if you are fine-tuning a 7B model, an RTX 4090 at {{gpu:rtx-4090}}/hr on Secure Cloud or {{gpu:rtx-4090:community}}/hr on Community Cloud will do the job, and neither platform's H100 tier is the right answer. A catalog that includes the cheap end of the range lets you make that choice. Our guide to choosing a GPU covers the sizing question properly.
Current Runpod rates on comparable hardware: H200 at {{gpu:h200}}/hr, H100 SXM at {{gpu:h100-sxm}}/hr, RTX Pro 6000 at {{gpu:rtx-pro-6000}}/hr, A100 PCIe at {{gpu:a100-pcie}}/hr and L4 at {{gpu:l4}}/hr.
Serverless inference
Both platforms run it, and the shapes differ.
Jarvislabs serverless asks you to pick a supported framework – vLLM, SGLang or Ollama – and point it at a model. You configure minimum and maximum workers and choose whether to keep them warm or scale to zero. The endpoint is OpenAI-compatible. Worker runtime bills by the minute, storage separately.
Runpod Serverless takes any container, so the framework list is whatever you can package. There is an official vLLM worker if you want the same short path, Flash if you would rather deploy a Python function with no Dockerfile at all, and Public Endpoints if you want a hosted model API instead of running your own worker. Cold starts are sub-200ms on FlashBoot-optimized workers.
The honest split: if your model is a standard LLM served by one of three frameworks, Jarvislabs gets you there with less to think about. If your inference does anything unusual – custom preprocessing, a non-LLM model, a multi-stage pipeline – a container you control is the difference between shipping and rewriting.
Compliance, and why it is the sharpest difference
Jarvislabs does not publish compliance certifications on its site.
Runpod is SOC 2 Type II certified and HIPAA and GDPR compliant, with SOC 2 reports, BAAs and DPAs available for security review through the Runpod Trust Center. Secure Cloud adds network isolation for workloads with stricter compliance needs. Coverage can vary by region and deployment model, so check requirements for your workload. See runpod.io/legal/compliance for current certification details.
If you are a solo developer fine-tuning a model, this changes nothing about your day. If you are the person who has to answer a vendor security questionnaire before the invoice clears, it is the whole conversation.
Geography, stated carefully
Jarvislabs accepts sign-ups from 62 countries across 6 continents. That is who can open an account, which is not the same as where the compute runs, and they do not publish a region count.
Runpod operates across 31 global regions, which is a statement about where workloads execute. The two numbers answer different questions, so do not read 62 against 31. If data residency matters to you, ask both vendors where the GPU physically is.
Which to choose
Choose Jarvislabs if your workloads are long-running rather than bursty, the eight GPU types cover what you need, you value a small product surface, and you have no compliance documentation requirement. The India focus is a genuine advantage if that is your market.
Choose Runpod if you run short or spiky inference where per-second billing compounds, you want hardware outside a curated list, you need a container rather than a supported framework, you are scaling to multi-node training, or someone is going to ask for a SOC 2 report.
Run both if you are unsure. Both bill by usage with no commitment, so an afternoon on each with your actual workload will settle it faster than any comparison table, including this one.
FAQ
Is Jarvislabs cheaper than Runpod?
On some headline on-demand rates, yes. But list price and total cost diverge once billing granularity, idle time and the size of the catalog come in: per-minute billing on short inference calls, and being pushed onto a larger card than your model needs, both cost more than the hourly rate suggests. Price your actual workload rather than comparing rate cards.
Does Jarvislabs have serverless inference?
Yes. It supports vLLM, SGLang and Ollama, exposes an OpenAI-compatible endpoint, and lets you set minimum and maximum workers with the option to scale to zero. Worker runtime bills by the minute, with storage billed separately.
Is Jarvislabs SOC 2 certified?
They do not publish any compliance certification on their website, so we cannot say either way. If you need SOC 2, HIPAA or GDPR documentation, ask them directly before committing.
What GPUs does Jarvislabs offer?
Eight types as of September 8, 2026: H200 SXM, H100 SXM, RTX Pro 6000 Blackwell, A100 80GB, A100 40GB, A30, L4, and B200, with configurations up to 8 GPUs per instance.
Can I move a workload from Jarvislabs to Runpod?
Usually with little friction. Both run standard containers against NVIDIA GPUs, so a working Docker image moves over largely unchanged. What needs redoing is anything written against platform-specific tooling, such as a Jarvislabs template or CLI workflow.
Which is better for fine-tuning?
Either handles a single-GPU fine-tune. The difference shows up at the edges: Runpod for a wider card selection when you want to right-size the job, and for multi-node work through Clusters. Our multi-GPU training guide covers the distributed setup.
Get started
The fastest way to compare them is to run the same job on both for an hour. Runpod bills per second with no minimum and pods deploy in under 30 seconds, so the test costs very little. See current pricing or deploy a Pod.
