Dev tools · alternatives · 2026
24 alternatives to Dstack – an open-source engine for running GPU workloads
dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters.
Dstack is an open-source engine designed to run GPU workloads efficiently. It is intended for developers and teams who need to execute computationally intensive tasks on graphics processors. The tool simplifies the… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Dstack – an open-source engine for running GPU workloads launched in 2024; newer entries below may have overtaken it.
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dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters.
2020 · dstack.ai · its alternatives →
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dstack Sky▲111Get GPUs at competitive rates from a pool of providers
2024 · dstack.ai · its alternatives →
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- 4DA
Dear HN, I am Riwaj, the cofounder of dstack.ai (https://github.com/dstackai). A few months ago, we built an online service that allows users to publish data visualizations from Python or R. The idea was to build a tool that did not require additional programming or front-end development for publishing data visualizations. Such a code can be invoked from either Jupyter notebook, RMarkdown, Python, or R scripts. Once the data is pushed, it can be accessed via a browser. Open-sourcing dstack: During our customer discovery phase, we realized that dstack.ai should integrate a lot…
2020 · its alternatives →
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Cerebrium▲300If you haven’t noticed, GPUs are scarce right now. The global boom in inference has demand growing faster than supply can keep up, and at Cerebrium ...
2024 · cerebrium.ai · its alternatives →
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Greetings HN! This is Doruk from Oblivus, and I'm excited to announce the launch of our platform, Oblivus Cloud. After more than a year of beta testing, we're excited to offer you a platform where you can deploy affordable and scalable GPU virtual machines in as little as 30 seconds! https://oblivus.com/cloud - What sets Oblivus Cloud apart? At the start of our journey, we had two primary goals in mind: to democratize High-Performance Computing and make it as straightforward as possible. We understand that maintaining GPU servers through major cloud service providers can be…
2023 · oblivus.com · its alternatives →
- 8TC
Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…
2022 · tensordock.com · its alternatives →
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An on-premises, bare-metal solution for deploying GPU-powered applications in containers - emergingstack/es-dev-stack
2016 · github.com · its alternatives →
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We developed a tool to trick your computer into thinking it’s attached to a GPU which actually sits across a network. This allows you to switch the number or type of GPUs you’re using with a single command.
2024 · thundercompute.com · its alternatives →
- 11WM
Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…
2024 · glhf.chat · its alternatives →
- 12CG
vEdge is a GPU cloud compute platform making computing affordable and accessible.
2021 · gpu.land · its alternatives →
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Host LLMs across devices sharing GPU to make your AI go brrr
Oct 2025 · github.com · its alternatives →
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Hi everyone — looking for feedback on a new infrastructure project we launched called vMetal. It's a bare metal management platform for GPU clusters that handles machine discovery, PXE booting, and lifecycle management, without the OpenStack complexity. Built around Kubernetes-native workflows so you can hand it off to teams or drop it into an existing platform. A lot of the infra platforms used for this today were designed 20 years ago (VMware, OpenStack, NVIDIA BCM, MAAS, etc.), while newer tools usually solve only a small piece of the stack. Neither were built with modern GPU cluster ops…
Mar 2026 · vmetal.ai · its alternatives →
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Simple scheduler for running jobs on GPUs. Contribute to ExpectationMax/simple_gpu_scheduler development by creating an account on GitHub.
2019 · github.com · its alternatives →
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Runs anything you can put into a Docker container. Improve your workflow with polyglot notebooks, automatic versioning and real-time collaboration. Save time and money with on-demand provisioning, including GPU support. Use it for ML, data science, runnable tutorials, for your classroom or scientifi
2019 · nextjournal.com · its alternatives →
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Hi HN, we are Ed, Zach, and Ronald, creators of Shadeform (https://www.shadeform.ai/), a GPU marketplace to see live availability and prices across the GPU market, as well as to deploy and reserve on-demand instances. We have aggregated 8+ GPU providers into a single platform and API, so you can easily provision instances like A100s and H100s where they are available. From our experience working at AWS and Azure, we believe that cloud could evolve from all-encompassing hyperscalers (AWS, Azure, GCP) to specialized clouds for high-performance use cases. After the launch of…
2023 · shadeform.ai · its alternatives →
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2024 · shadeup.dev · its alternatives →
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RunInfra▲156Describe the AI model you need and get an optimized AI
Jul 2026 · runinfra.ai · its alternatives →
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The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%. This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems…
Apr 2026 · systalyze.com · its alternatives →
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gpu share, free, runsnack
Aug 2026 · runsnack.com · its alternatives →
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Hi. :) I’m Andrey, the creator of dstack. I started this project while I was working at JetBrains where I helped the PyCharm team to improve support for Jupyter notebooks. As I was in close contact with many ML devs (who used PyCharm) I was able to see their struggle with running ML workflows. Unlike traditional dev workflows, ML workflows are difficult to run on a local machine (due to the lack of memory, more CPUs/GPUs, etc). This is why people often have to use remote machines (e.g. via SSH), or adopt one of the end-to-end MLOps platforms. Using remote machines is not difficult but…
2022 · github.com · its alternatives →
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We built installable software for Windows & Linux that makes any remote Nvidia GPU accessible to, and shareable across, any number of remote clients running local applications, all over standard networking.
2022 · github.com · its alternatives →
- 24
Compare GPU rental prices across cloud providers
5d ago · gpu.context.dev · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →