Northflank Alternatives for GPU Inference: 6 Compared

Northflank is a capable GPU platform-as-a-service, but "inference" is not one workload, and the right provider changes with how you serve traffic. This guide compares six Northflank alternatives for GPU inference workloads across serving model, GPU price, how compute is billed, and whether endpoints scale to zero. If you are also weighing the hyperscalers, our alternatives to AWS, GCP and Azure for AI covers that axis.

Why teams evaluate Northflank alternatives

Northflank is a genuine GPU PaaS. It handles GPU attachment, CUDA driver setup, and cluster provisioning so your container gets immediate GPU access "without infrastructure setup," and it runs everything from "LLM training and fine-tuning, real-time inference APIs" to computer vision and notebooks. It also supports GPU time-slicing to "run multiple workloads on single GPUs" and share expensive H100s across development, testing, and small inference jobs.

The friction shows up in pricing. Northflank bills GPU by the second - an H100 80GB is listed at "$2.74/hour, including allocated CPU, RAM, and ephemeral storage" - but third-party analysis notes that vCPU and memory are in practice "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." For a VP Engineering modelling cost at scale, that unbundling is the trigger to look wider. Our LLM inference cost at scale breakdown goes deeper on where the real money goes.

How to choose

Four axes decide the shortlist:

• Inference operating model. Do you want dedicated GPUs you manage, per-second serverless containers, or a per-token API where the provider owns the serving stack? See serverless GPU vs dedicated tradeoffs.

• Cost predictability. Bundled compute (GPU price includes vCPU + RAM) is easier to forecast than unbundled rates you assemble per plan.

• Lock-in. Per-token APIs are fastest to ship but hardest to leave; dedicated GPUs keep your serving stack portable.

• Compliance and BYOC. Regulated teams need data residency and bring-your-own-cloud; see EU data residency for serverless GPU inference.

Alternatives at a glance

Modal

What it's for: per-second serverless GPU compute with autoscaling. Modal lists "Nvidia H100 SXM5 $0.001097/ sec" (about $3.95/hour), with a GPU ladder spanning B300, B200, H200 SXM, and H100 SXM5.

Where it stops: it is a compute primitive, not a tuned serving stack - latency and cold-start behaviour are yours to optimise.

Best for: teams that want granular billing and broad GPU choice. See our Modal alternatives and Modal vs RunPod on cost and cold starts.

Replicate

What it's for: managed model deployments. Replicate prices the "Nvidia H100 GPU gpu-h100 $0.001525/sec $5.49/hr" with 13 CPU cores and 144 GB RAM bundled in.

Where it stops: the convenience premium shows in the headline H100 rate.

Best for: image and model-catalog serving; see Replicate alternatives for production serving.

RunPod

What it's for: the widest hardware menu at the lowest price. RunPod "publishes 25 NVIDIA cards from the RTX A4000 up to the B300, plus AMD MI300X at $2.39/hr and MI350X on Secure Cloud. vCPU and RAM are included in the rate." Three products cover different shapes of work: Pods for dedicated instances, Serverless that "scales to zero between requests, with sub-200ms cold starts via FlashBoot," and Clusters for multi-node training.

Where it stops: more assembly than a fully managed endpoint.

Best for: cost-led teams that want control. See multi-node LLM inference on serverless clusters.

DigitalOcean

What it's for: GPU Droplets. On-demand HGX H100 is "$4.41/GPU/hr and HGX H200 is $4.47," with 5TiB of scratch NVMe and per-second billing at a 5-minute minimum. DigitalOcean also offers "serverless inference endpoints with autoscaling" and managed deployment workflows.

Where it stops: the 5-minute minimum and droplet model suit steadier traffic more than spiky inference.

Best for: teams already standardised on DigitalOcean.

Together AI

What it's for: both dedicated and per-token serving. Dedicated on-demand "NVIDIA HGX H100 | $5.49" sits alongside serverless inference, where "most teams start with serverless inference and move to dedicated endpoints at scale."

Where it stops: per-token pricing is convenient but opaque under heavy, predictable load.

Best for: open-model API consumers; see Together AI alternatives.

Fireworks AI

What it's for: serverless per-token inference. Fireworks lets you "get started in seconds with per token pricing, zero setup and no cold starts," across Standard, Priority, and Fast serving paths.

Where it stops: you serve the provider's stack, not your own container.

Best for: fastest time-to-API; see Fireworks AI alternatives.

Benchmark evidence

List prices do not tell you how a platform behaves under load. Cerebrium's own measurements give the shape. In our benchmarks - 256 input tokens on 1xH100 at batch size 1, measured as total roundtrip to the platform - vLLM delivered "the lowest TTFT of 123ms," while for throughput "SGLang was a clear winner achieving a throughput of 460 tokens per second on a batch size of 64." On cold starts, we measured roughly "~10-15s to load the model into GPU memory" for a TensorRT-LLM Llama 3 8B engine. And in production, DistilLabs reported that with Cerebrium "we reduced their inference costs by 50%, and increased accuracy from 83% to 92%, while keeping latency and reliability consistent at production scale." That is the bar a Northflank alternative should clear.

Which alternative fits which workload

For the lowest GPU price and the broadest hardware, RunPod. For granular per-second serverless, Modal. For a per-token API with zero setup, Fireworks AI or Together AI. For droplet-style GPUs inside an existing DigitalOcean footprint, DigitalOcean. For catalog-style managed deploys, Replicate. For measured low-latency production inference with bundled, predictable pricing, Cerebrium.