Northflank is a deployment platform. You push to Git, it builds a container, and it runs your services, databases, cron jobs and preview environments on managed infrastructure or inside your own AWS, GCP or Azure account. GPUs are one resource type you can attach to that.
Runpod is GPU infrastructure. Pods for dedicated instances, Serverless for inference that scales to zero, Clusters for multi-node training, all running standard Docker images.
Both will get a model onto a GPU. They are answering different questions, and the pricing structures reflect that.
| Capability | Northflank | Runpod |
|---|---|---|
| Built around | Deploying and operating applications | GPU compute |
| Deployment unit | Git repo or container image | Any standard Docker image |
| GPU models published | 5 | 21 NVIDIA cards, plus AMD MI300X |
| Databases, cron, pipelines | Yes, part of the platform | Not offered, bring your own |
| Bring your own cloud | Yes, into AWS, GCP or Azure | Not offered |
| vCPU and RAM | Billed separately from the GPU | Included in the GPU rate |
| Egress | $0.06/GB | No egress or ingress fees |
| Scale to zero for inference | Via autoscaling on services | Serverless, sub-200ms cold starts via FlashBoot |
| Multi-node training | Not a published product | Clusters, self-serve up to 64 GPUs |
| Free tier | Sandbox with always-on compute | Not offered |
Northflank capabilities and rates read from northflank.com/pricing on 9 September 2026 and may change. Runpod rates pull live.
What each company is actually built around
Northflank has been running since 2019 and its center of gravity is the application, not the accelerator. Buildpacks, custom Dockerfiles, GitHub and GitLab integration, secret management, RBAC, preview environments per pull request, managed databases. GPUs arrived into a platform that already did all of that.
That is a real advantage if your AI workload is a feature inside a product rather than the product itself. A team shipping a web app with an inference endpoint attached can run both on one platform, with one deployment pipeline and one bill.
Runpod assumes the GPU is the point. There is no managed Postgres, no cron scheduler, no preview environments. What there is instead is a much wider catalog of accelerators, a Community Cloud tier below the standard rate, and multi-node clusters you can provision without talking to anyone.
The hourly rate is not the whole rate
This is the difference that changes the arithmetic, and it is easy to miss.
Northflank prices the GPU separately from the CPU and memory it runs beside. Their published rates are $0.01667 per vCPU per hour and $0.00833 per GB of memory per hour, on top of the GPU.
Runpod includes vCPU and RAM in the GPU rate. An H100 PCIe on Secure Cloud comes with 188GB of RAM and 16 vCPUs at $2.89/hr, with nothing further to add.
Worked through on an H100:
| Component | Northflank | Runpod, H100 PCIe Secure Cloud |
|---|---|---|
| GPU | $2.74/hr | $2.89/hr |
| 16 vCPUs | $0.27/hr | Included |
| 188GB RAM | $1.57/hr | Included |
| Comparable total | ~$4.58/hr | $2.89/hr |
Northflank GPU rate $2.74/hr, vCPU $0.01667/hr, memory $0.00833/GB/hr, read 9 September 2026. The CPU and memory figures above assume matching Runpod’s bundled 16 vCPUs and 188GB of RAM, which is an illustration rather than a required configuration – most workloads attach considerably less. Runpod rate pulls live.
Read that as a structural point, not a headline number. Very few workloads need 188GB of RAM beside a single H100. The honest version is that Northflank’s $2.74 and Runpod’s H100 PCIe rate are not the same measurement, and any comparison has to add a compute plan to Northflank’s side before the two are the same thing. On a lean plan the gap narrows sharply.
GPU catalog
Northflank publishes five GPUs: L4 24GB at $0.80/hr, A100 40GB at $1.42, A100 80GB at $1.76, H100 80GB at $2.74, and RTX PRO 6000 96GB at $3.00, all read 9 September 2026.
Runpod publishes 21 NVIDIA cards from the RTX A5000 up to the B300, plus AMD MI300X at $2.39/hr on Secure Cloud, verified 31 August 2026. That includes B200, H200, H100 in PCIe, SXM and NVL variants, the RTX 40 and 50 series, and the L40 family.
The practical consequence is fit. If your model runs comfortably on an L4 or an A100, Northflank covers it. If you need a B200 for a large training run, an H200 for long-context inference, or an AMD card, Northflank does not publish one.
Where Northflank wins
You want one platform for the whole application. If the GPU workload sits alongside a web service, a database and a queue, running them in one place with one pipeline is worth real money in operational time. Runpod does not offer that and is not trying to.
You need to run in your own cloud account. Northflank’s bring-your-own-cloud deploys into your AWS, GCP or Azure account, which matters for data residency, committed-spend agreements you have already signed, and security teams that require workloads inside a known VPC. Runpod is a hosted service only.
You want a free tier to try it on. Northflank’s Sandbox includes always-on compute, two free services, a database and two cron jobs. Runpod has no free tier.
Your workload is CPU-heavy with an occasional GPU. Northflank’s separate billing works in your favor when the accelerator is the smaller part of the job.
Where Runpod wins
Hardware choice. 21 NVIDIA cards against five, plus AMD. Being able to move a workload from an A5000 to an H200 without changing provider is the whole argument.
No egress fees. Northflank charges $0.06/GB for network egress. Runpod charges nothing for ingress or egress. On a workload shipping model weights, generated images or video out to users, that line item decides real invoices.
Multi-node training. Runpod Clusters provision self-serve up to 64 GPUs with shared storage. Northflank does not publish a distributed training product.
Inference that scales to zero. Runpod Serverless bills per second of active worker time with sub-200ms cold starts via FlashBoot. Northflank autoscales services, which is a different mechanism aimed at a different traffic shape.
A lower-cost tier. Community Cloud sits below Secure Cloud pricing, starting at $0.16/hr for an RTX A5000. Northflank has no equivalent.
Compliance and support
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. Enterprise agreements add a 99.99% uptime SLA, dedicated capacity and tailored terms. See runpod.io/legal/compliance for current certification details.
Northflank publishes an Enterprise tier carrying 24/7 support, an SLA, VPC deployment and white-label options. Their specific certifications were not verified for this article, so check them directly if compliance is a deciding factor.
Which one should you choose
Choose Northflank if your GPU work is one part of a larger application you also need to deploy and operate, if you need to run inside your own cloud account, or if managed databases and pipelines alongside your model would save your team more than hardware flexibility would.
Choose Runpod if the GPU is the workload, if you need a card Northflank does not carry, if you move enough data that egress fees matter, if you need multi-node training without a sales conversation, or if a lower-cost tier is worth more than an integrated platform.
The honest summary: these are not the same product. Northflank is competing with Render and Railway and happens to offer GPUs. Runpod is competing on the accelerators themselves. Decide which of those two problems is actually yours before comparing any hourly rate, because the rates are not measuring the same thing.
Frequently asked questions
Is Northflank cheaper than Runpod?
Their published H100 rate of $2.74/hr is lower than Runpod’s H100 PCIe Secure Cloud rate, read 9 September 2026. But Northflank bills vCPU and memory separately at $0.01667/hr and $0.00833/GB/hr, while Runpod includes them, so the two figures are not measuring the same thing. Add the compute plan your workload actually needs to Northflank’s side before comparing.
Does Northflank support multi-node GPU training?
Northflank does not publish a distributed training product. Runpod Clusters provision self-serve up to 64 GPUs with shared storage and 1600 to 3200 Gbps between nodes.
Can I run Northflank in my own AWS account?
Yes. Bring your own cloud deploys Northflank workloads into your own AWS, GCP or Azure account, across what they describe as 600 BYOC regions. Runpod is a hosted service and does not offer this.
Which has more GPU options?
Runpod publishes 21 NVIDIA cards plus AMD MI300X. Northflank publishes five: L4, A100 40GB, A100 80GB, H100 80GB and RTX PRO 6000.
Does either charge for egress?
Northflank charges $0.06/GB for network egress, with ingress free and cross-zone transfer at $0.02/GB. Runpod charges no ingress or egress fees.
Do I need to write a Dockerfile for either?
Neither requires one. Northflank can build from a Git repo using buildpacks. Runpod offers prebuilt templates and the Hub for deploying open-source models, and accepts any Docker image you already have.
Get started
If the GPU is the part of the job that matters, the fastest way to compare is to run the same container on both. Runpod bills by the second with no minimum and no quota request. See current pricing or deploy a Pod.
