LLM Hangar / Compare / BYOC LLM platforms
Bring-your-own-cloud LLM platforms compared (2026)
"Bring your own cloud" has come to mean several different things. Some platforms create the GPU instance inside your AWS or GCP account and leave you the owner. Some run your workload on their hardware but let you pick a region. Some only offer the first option after a conversation with sales. Before comparing prices, it helps to agree on a test.
The payload test
Two vendors in this space have published the same definition. Railway's developer guide to BYOC and Northflank's survey of BYOC options both draw the line at one question: can the vendor see the request payload? In real BYOC the answer is no, because the request goes to a process running in your account and the vendor only ever sees metadata. For an LLM endpoint the payload is the prompt and the response, which is usually the reason a team wanted its own cloud in the first place. So that is the first column below.
Six platforms, seven questions
Everything in this table is as published on each vendor's site on 22 August 2026. "Not stated" means we could not find it in public pages; it does not mean the feature is absent.
| Platform | Runs in your account | Vendor can read payloads | Cluster setup required | Self-serve signup | EU pinning | Audit log | Published price |
|---|---|---|---|---|---|---|---|
| Northflank (BYOC) | Yes: AWS, GCP, Azure and other clouds via its BYOC mode | No, by its own definition: workloads run in your cluster | Yes: you connect a cluster and node pools, then deploy images | Yes, pay as you go | Via the region of your own cloud | Yes, per its compliance pages | Pay as you go; GPUs listed at $0.80 to $3.00 per hour; the BYOC guide itself shows no price |
| Tensorfuse | Yes: serverless GPUs in your AWS account | Not stated | Not stated: the platform manages the cluster for you | Yes, with a free tier | Via your AWS region; not stated as a feature | Not stated | Free, $249 and $799 per month |
| BentoML / Modular (BYOC) | Yes: control plane with the vendor, data plane in your cloud | Not stated | Not stated | No: demo-gated | Not stated | Not stated | Not stated |
| Together AI | Enterprise only: VPC deployments arranged through sales. Serverless runs on Together's infrastructure | Serverless: yes, the request is handled on Together's infrastructure. VPC: not stated | No | Serverless yes; VPC no | EU regions for enterprise VPC deployments, per its docs | Not stated | Per token on serverless; dedicated endpoints per GPU hour |
| Baseten | Enterprise only: self-hosted and VPC options on the Enterprise plan | Hosted plans: yes, inference runs on Baseten's infrastructure. VPC: not stated | No | Hosted yes; VPC no | Other regions "via sales", per its pricing page | Not stated | Per GPU minute; H100 listed at $0.10833 per minute |
| LLM Hangar | Yes: AWS, Nebius and RunPod, in your own account | No: prompts travel from your client to the endpoint on your instance and never pass through our servers | No: no Kubernetes, no Terraform, no command line | Yes, 24-hour trial | One checkbox pins every resource to EU member-state regions; no surcharge | Every plan, including the trial | $39 per month (Lab); GPU hours billed by your provider |
Is BYOC self-hosting?
A recent Hacker News thread asked exactly this, and the honest answer is "the half that matters". Self-hosting is usually shorthand for two things: the workload runs on infrastructure you control, and nobody else handles your data. BYOC delivers both for the data plane. The GPU instance, the disk holding the weights, the network and the endpoint are created in your account and belong to you. What you do not operate is the control plane: the service that provisions those resources, watches the boot, enforces budget caps and records what happened.
So the right question for any BYOC vendor is what its control plane holds. For LLM Hangar the list is short: your account details, deployment metadata (which model, which GPU shape, which region, the cost estimate you confirmed), the credential that lets us act in your account, stored envelope-encrypted, and the timestamped audit log of every action we took. It never holds prompts, responses, model weights or endpoint traffic, and it could not, because none of that passes through it. If you cancel, the instance and the endpoint keep running in your account until you delete them, and the audit log is yours to export. How each credential is scoped is described on the security page.
When to choose each
Northflank is the strongest general-purpose option. If you run web services, jobs and GPU workloads side by side across several clouds and you are comfortable managing clusters and container images, it does far more than an inference-only tool. The price of that breadth is setup: it is a platform you operate, not an endpoint you receive.
Tensorfuse fits teams that want serverless GPU functions inside their AWS account and can budget a flat monthly fee for the platform. It is AWS-first; if your cloud is elsewhere, look further down the list.
BentoML (now under the Modular handbook) is built for organisations standardising an inference platform across many models and teams, with the procurement process that implies. If you have a platform team and a purchasing cycle, it belongs on the shortlist. If you want to try something this afternoon, it does not.
Together AI and Baseten are excellent hosted inference services. Their BYOC and VPC options exist for enterprise customers; for everyone else the product is their cloud, not yours. If per-token serverless pricing and instant scale are what you need, they are the better choice, and we say so on the Together AI and Baseten comparison pages.
LLM Hangar is for a private OpenAI-compatible endpoint in your own AWS, Nebius or RunPod account without DevOps: pick a model, pick a GPU shape and region, set a hard budget cap, confirm. The platform is $39 per month and the GPU bill is your provider's. It is deliberately narrow: one model per deployment on a shape that fits it, not a serverless fleet.
Questions people ask
Is bring your own cloud the same as self-hosting?
Partly. With BYOC the data plane (the GPU instance, the model, the endpoint) runs in your own cloud account, which is the part self-hosting is about. A vendor still runs the control plane that provisions and monitors it. What the control plane holds is the question to ask: for LLM Hangar it is account data, deployment metadata and the audit log, never prompts or responses.
Is BYOC only for enterprise tiers?
On Together AI and Baseten, deployments in your own VPC are arranged through enterprise sales. Northflank and Tensorfuse are self-serve. LLM Hangar is self-serve at $39 per month with a 24-hour trial.
Can I use my existing AWS credits or reserved capacity?
When the GPU instance is created in your own account, your provider bills you for it directly, so credits, reserved capacity, savings plans and negotiated rates apply as they would to any other instance. This is true of LLM Hangar and of any platform that genuinely provisions into your account.
Do BYOC platforms support GPUs?
All of the platforms compared here run GPU workloads. The differences are whether you manage a cluster yourself, whether signup is self-serve, whether EU pinning costs extra, and whether the vendor can see request payloads.