Deep-learning-in-cloud: A Curated Index of GPU Rental and MLOps Vendors

What the repository actually contains

This is a list, not a library. The repository has no primary language recorded and no published releases, and the README is the product: four markdown tables covering cloud GPU vendors, web app deployment hosts, MLOps platforms, and a perks or free credits section. The framing in the opening lines is explicit about the audience. It addresses someone whose laptop is not powerful enough to run their models and points them at cloud GPUs instead. That is a beginner or small-team framing. A researcher with an existing cluster allocation is not the target reader. The value proposition is breadth of names in one file: Google Colaboratory and Kaggle Kernels appear as free options, while paid vendors such as RunPod, Lambda, Vast.ai, TensorDock, Paperspace, Genesis Cloud, DataCrunch, Jarvis Labs, puzl.ee and Dataoorts sit in the same table. The README also links out to sections on deploying a model as a web app and on MLOps platforms for what it calls the complete machine learning lifecycle.

How the vendor tables are structured and what the columns mean

Each row follows the same four-column shape: cloud vendor, website, pricing, and free trial or free credits. The pricing column is inconsistent by design. Some cells carry a concrete figure, such as DataCrunch at a stated V100 rate of $0.69/h, Genesis Cloud at a stated 1080Ti rate of $0.30/hour, or Jarvis Labs at a stated RTX 5000 rate of $0.49/hr. Others carry only the words pay as you go, and many carry a link labelled pricing with no number at all. The free credits column is similarly mixed: Alibaba Cloud, Google Cloud Platform and Ace Cloud are listed with $300 credits, Azure and IBM Cloud with $200, while Cirrascale, Cloudalize, Exoscale and several others show a dash. That dash is the honest part of the file. It signals an unknown, not a zero. The MLOps table repeats the same four-column structure for platforms such as Akira.ai, Algo and Dataiku, and the deployment table does the same for Digital Ocean, Glitch, Heroku, PythonAnywhere, Render, Streamlit For Teams and Vercel.

The mechanism is a hand-maintained markdown file

There is no ingestion pipeline, no API call to a vendor pricing endpoint, and no schema. A contributor edits the README and opens a pull request. That means the freshness of any given cell depends on whether a human noticed a price change. The repository has topics for artificial-intelligence, cloud-gpus, deep-learning, mlops and related terms, which is how it is likely to be found, but topics do not keep a table current. The last push recorded is 2026-06-22, which tells you the file has been touched recently without telling you which rows were touched or whether the numbers in them were rechecked. If you fork it to keep an internal copy for your team, you inherit the maintenance burden in full: every vendor you actually use needs its own recheck, and the file gives you no way to mark a row as verified on a date. Adding a verified-on column would be the obvious fix, and its absence is the clearest sign that this is a personal list rather than a maintained dataset.

Getting the list and using it in a workflow

The repository is consumed by cloning or downloading it. The standard command is git clone https://github.com/zszazi/Deep-learning-in-cloud.git, which places the README on your machine under the default branch, master. From there you read README.md directly or render it. The README also uses in-page anchors, for example the link to the deploy section is written as README.md#deploy-your-model-as-a-web-app, and the MLOps link points at README.md#mlops-platforms. Those anchors are the only navigational structure the project offers, so if you fork it and rename a heading, you break the links that point at it. There is no package to install from a registry, no configuration file, and no CLI. If you want the list inside a docs site, you convert the tables yourself. Nothing in the repository supplies a build step for that.

Where the list is the wrong tool

A static table cannot answer the questions that decide a GPU rental. It does not tell you whether capacity exists in your region right now, whether the quoted instance is preemptible or on-demand, what egress charges apply, or how long provisioning takes. The README states prices for a handful of vendors and leaves the rest to a link, so a reader who needs to compare ten vendors on cost has to open ten pricing pages anyway. The free credits column has a second failure mode: a credit figure without a stated expiry or eligibility rule is not actionable. A $300 credit that requires a new account and a business verification is a different offer from one that applies to any signup. The repository does not distinguish between them. There is also a staleness risk specific to this format. GPU rental prices moved repeatedly over the period this file has existed, and a table cell that was correct when written can be wrong by the time a reader acts on it. Treat every number in it as a pointer to a page you still have to open.

What a live alternative does differently

The closest functional alternative is a pricing aggregator that queries vendor APIs or scrapes pricing pages on a schedule, such as the GPU comparison tools that let you filter by card, region and hourly rate. The difference in approach is fundamental. This repository is a curated snapshot with human judgement about which vendors are worth naming, and it includes free tiers and credit programmes that a pure price scraper often ignores. An aggregator is a live feed with no editorial filter, and it typically covers only the vendors that expose machine-readable pricing. Neither replaces the other. If you want to know that a Colab-style free tier exists on AMD ROCm hardware, the curated list is where you would find a pointer to it. If you want to know the current cheapest H100 across twenty providers, a live aggregator will beat a markdown cell every time. A reasonable workflow is to use this repository to build your shortlist and a live source to price it.

Licence, maintenance and what you take on by forking

The repository is MIT licensed. That permits reuse, modification and redistribution provided the copyright notice and permission notice are retained, but this is a summary of the identifier and not legal advice; read the LICENSE file in the repository for the actual terms. The practical consequence for a team is that you can copy the tables into internal documentation without asking anyone. The cost you take on is maintenance. Because there are no releases, there is no version to pin to and no changelog to read when a row changes. If you fork, you own the accuracy of every cell you keep, and you have no upstream signal telling you when a vendor's pricing page has moved. The repository's own structure gives you no tooling to automate that check, so the work is manual by definition.

Editorial conclusion

Adopt this repository if you are at the discovery stage and need a single markdown file to skim GPU vendors, web app hosts and MLOps platforms before you start pricing instances yourself. Do not adopt it as a source of truth for cost, capacity or regional availability: the README itself carries entries with a dash where a price or trial credit should be, and several rows point at vendor homepages rather than a pricing page. Verify first by opening the linked pricing URL for any two vendors you are considering, then check whether the licence and repository contents are still being updated, since the last push recorded is 2026-06-22 and no releases have been published.

Official sources

1. Issues
2. License: MIT
3. README
4. zszazi/Deep-learning-in-cloud on GitHub