Northflank is a deployment platform that added GPUs. It builds from Git, runs your services, databases and cron jobs, and can deploy into your own AWS, GCP or Azure account. If your AI workload is a feature inside a product you also need to host, that is a strong fit.
Teams outgrow it in two specific ways. The GPU catalog is five cards, so a workload that needs a B200, an H200 or anything in the RTX 40 or 50 series has nowhere to go. And vCPU and memory are billed separately from the GPU, at $0.01667 per vCPU-hour and $0.00833 per GB-hour, which makes the headline GPU rate incomplete until you attach a compute plan.
Five alternatives below, plus what each is actually for. Competitor prices carry the date they were verified. Runpod rates pull live.
What to check before you switch
Whether the hourly rate is the whole rate. Northflank bills CPU and memory as separate line items. Most platforms here bundle them. Convert both sides to the same thing before comparing anything.
Where the platform stops. Serving and training are different problems, and several options here do exactly one of them. Replatforming later is a cost nobody puts on a pricing page.
Whether you need the surrounding platform at all. Northflank’s databases, pipelines and preview environments are the reason to be there. If you are not using them, you are paying a platform tax on a GPU.
Egress. Northflank charges $0.06/GB. On a workload shipping images, video or model weights out to users, that decides real invoices.
Bring your own cloud. If your workload has to run inside your own AWS, GCP or Azure account, that requirement eliminates most of this list immediately.
1. Runpod
Runpod is the strongest option if the GPU is the workload rather than a component of one.
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. vCPU and RAM are included in the rate, so the number on the pricing page is the number. There are no ingress or egress fees.
Three products cover different shapes of work. Pods are dedicated instances for development and long-running jobs, billed per second. Serverless runs inference that scales to zero between requests, with sub-200ms cold starts via FlashBoot. Clusters provision multi-node training self-serve up to 64 GPUs with shared storage. All three run standard Docker images, so moving between them is a configuration change rather than a migration.
Community Cloud sits below Secure Cloud pricing, starting at {{gpu:rtx-a5000:community}}/hr for an RTX A5000. Runpod is SOC 2 Type II certified and HIPAA and GDPR compliant, with SOC 2 reports, BAAs and DPAs available through the Runpod Trust Center. Coverage can vary by region and deployment model, so check requirements for your workload.
Where it stops: no managed databases, no CDN, no cron scheduler, no bring-your-own-cloud. If the platform around the GPU is what you wanted from Northflank, Runpod does not replace it.
Best for: teams whose bottleneck is hardware choice, egress cost or per-second billing.
Full detail in our Runpod vs Northflank comparison.
2. Modal
The closest match to Northflank on developer experience, and the best answer if you liked the abstraction but need more hardware.
You decorate a Python function, run one command, and it executes on a GPU. Container images are declared in Python rather than built from a Dockerfile. Queues, scheduled functions and web endpoints are primitives you call. Modal publishes 11 GPUs including B200 and H200, and advertises scaling from 1 to 64 GPUs for training.
Their base H100 SXM5 rate is $3.95/hr, verified 18 August 2026. Two multipliers decide whether that is the rate you pay: region pinning costs 1.5x to 1.75x base, and guaranteed non-preemptible execution costs 3x base. A base-rate H100 becomes $11.85/hr at the 3x tier. There is also a $250/month Team plan above compute.
Where it stops: the SDK is the product and the lock-in. A workload built on Modal decorators and Modal volumes does not lift elsewhere without a rewrite.
Best for: Python-first teams who want to stop thinking about containers entirely.
3. DigitalOcean
The nearest thing on this list to Northflank’s actual shape: a general-purpose cloud where GPUs are one product among many.
Managed databases, Kubernetes, object storage, load balancers and an app platform, all on one bill. GPU Droplets publish four NVIDIA cards plus L40S and a deeper AMD line covering MI300X, MI325X, MI350X and MI355X. Every GPU Droplet bundles 5TiB of scratch NVMe and 10,000 to 15,000 GiB of transfer.
On-demand HGX H100 is $4.41/GPU/hr and HGX H200 is $4.47, verified 9 September 2026. Billing is per second with a 5-minute minimum, and powered-off Droplets are still billed until destroyed.
Where it stops: the NVIDIA catalog is thin between the RTX 6000 Ada at $1.57 and the H100 at $4.41, with nothing in the RTX 40 or 50 series. Multi-node routes through sales.
Best for: teams who want the whole cloud in one place and are willing to pay more per GPU-hour for it.
4. Baseten
Production inference operated for you rather than orchestrated by you, and the only option here that runs inside your own cloud.
You pay for the minutes a model spends deploying, scaling or predicting rather than for idle capacity, which suits spiky traffic. Models are packaged with Truss, their open-source format. Self-hosted and hybrid deployments with data residency control are available on the Enterprise tier, and they are SOC 2 Type II certified and HIPAA compliant.
An H100 is $6.50/hr and an A100 80GB is $4.00/hr, converted from their per-minute rates, verified 18 August 2026.
Where it stops: if you left Northflank because you wanted to stop packaging models, Truss is a different format rather than no format. Rates are the highest on this list.
Best for: teams with a data residency requirement, and anyone who values not paying for idle capacity.
5. Replicate
The shortest path from nothing to a working model. A large public catalog of community-packaged models callable through one API request, with no container work at all.
Their H100 is $5.49/hr and A100 80GB is $5.04/hr, verified 18 August 2026. The cheapest GPU is a T4 at $0.81/hr, a 2018 card.
Where it stops: every multi-GPU configuration requires a committed spend contract. One GPU is self-serve; two is a sales call. If scaling past a single card is why you are leaving Northflank, Replicate reproduces that limit.
Best for: calling community models through an API, not sustained training on your own image.
Frequently asked questions
What is the cheapest Northflank alternative for GPUs?
It depends on the card, and on whether the platform bundles vCPU and RAM. Northflank’s $2.74/hr H100 excludes both. Runpod includes them and publishes rates on the pricing page, with Community Cloud below Secure Cloud. Compare the total for the configuration you actually need, not the headline rate.
Which alternative supports bring your own cloud?
Baseten offers self-hosted and hybrid deployments with data residency control on its Enterprise tier. Northflank itself deploys into your AWS, GCP or Azure account. Runpod, Modal and Replicate are hosted services only.
Which has the widest GPU selection?
Runpod, with 21 NVIDIA cards plus AMD MI300X. Modal publishes 11, DigitalOcean four NVIDIA plus four AMD, and Northflank five.
Do any of these charge for egress?
Northflank charges $0.06/GB. Runpod charges no ingress or egress fees. DigitalOcean bundles a transfer allowance per Droplet. Check the others directly, as their published terms vary.
Can I run multi-node training on these?
Runpod Clusters provision self-serve up to 64 GPUs. Modal advertises 1 to 64 GPUs. DigitalOcean routes multi-node through Bare Metal GPUs and sales. Replicate requires a committed contract for any multi-GPU configuration. Northflank does not publish a distributed training product.
Why do teams leave Northflank for GPU work?
Two reasons recur: the five-card catalog does not cover the hardware they need, and the separate CPU and memory billing makes the real cost hard to predict against platforms that bundle.
Get started
The fastest way to compare is to run the same container on two of them. Runpod bills by the second with no minimum and no quota request. See current pricing or deploy a Pod.
