
根据功能和用途,为你挑选 12 款 Cerebrium 的替代工具。
| 排名 | 工具和标语 | 官方域名 | 月访问量 | 月度增长 (MoM) |
|---|
| 定价模式 |
|---|
| 主要市场 |
|---|
| 评分 |
|---|
| 比较 |
|---|
| ● | Cerebrium | cerebrium.ai | 2.7万 | -2.78万 (-50.8%) | 免费试用,付费 | 美国 | 4.3 | 概览 |
| 1 | RunPod | runpod.io | 288.1万 | 16.15万 (+5.9%) | 付费 | 美国 | 4.7 | 比较 |
| 2 | Modal | modal.com | 119.6万 | 27.72万 (+30.2%) | 免费增值,定价请咨询 | 美国 | 4.8 | 比较 |
| 3 | Nebius | nebius.com | 114.57万 | 11.08万 (+10.7%) | 付费,定价请咨询 | 美国 | 4.5 | 比较 |
| 4 | Lightning AI | lightning.ai | 27.26万 | -3.81万 (-12.3%) | 免费增值,付费,定价请咨询 | 美国 | 4.7 | 比较 |
| 5 | Together AI | together.ai | 164.59万 | 64.77万 (+64.9%) | 免费增值,付费 | 美国 | 4.7 | 比较 |
| 6 | Thunder Compute | thundercompute.com | 10.21万 | -4.17万 (-29%) | Paid (per-minute instances, per-second sandboxes) | 美国 | 4.4 | 比较 |
| 7 | Salad | salad.com | 71.35万 | 6.75万 (+10.4%) | 免费试用,付费,定价请咨询 | 美国 | 4.3 | 比较 |
| 8 | Vast.ai | vast.ai | 125.76万 | 3.42万 (+2.8%) | 付费 | 美国 | 4.6 | 比较 |
| 9 | DeepInfra | deepinfra.com | 45.78万 | -4.45万 (-8.9%) | 付费 | 美国 | 4.6 | 比较 |
| 10 | FluidStack | fluidstack.io | 15.46万 | 1.05万 (+7.3%) | 付费,定价请咨询 | 美国 | 4.4 | 比较 |
| 11 | Novita AI | novita.ai | 27.14万 | -3.15万 (-10.4%) | 付费 | 美国 | 4.4 | 比较 |
| 12 | General Compute | generalcompute.com | 1.38万 | 924 (+7.2%) | 免费试用,定价请咨询 | 美国 | 4.2 | 比较 |
面向 AI 与数据负载的无服务器云基础设施
Nebius is an AI-focused cloud platform that gives teams non-virtualized NVIDIA GPU clusters, managed MLOps tooling, and a serverless inference layer so they can move from experiment to production on one stack.
Nebius is an AI-focused cloud platform that gives teams non-virtualized NVIDIA GPU clusters, managed MLOps tooling, and a serverless inference layer so they can move from experiment to production on one stack.
由 PyTorch Lightning 创建团队打造的 AI 云平台

Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all backed by in-house systems research.
Together AI is the AI Native Cloud — a full-stack platform for running, fine-tuning and training open-source models. It spans serverless inference APIs, dedicated endpoints, GPU clusters, post-training and storage, all backed by in-house systems research.

低成本云 GPU:面向 AI 工作负载的按分钟实例与按秒计费沙箱。
低成本云 GPU:面向 AI 工作负载的按分钟实例与按秒计费沙箱。
编排全球闲置消费级 GPU 的分布式 GPU 云
从全球市场按秒租用云 GPU

FluidStack builds and operates large-scale compute infrastructure for AI, including gigawatt-class data centers and GPU clusters used for training and inference. The company designs its own power, cooling and data center systems, onshores manufacturing and deploys solar generation to supply the power its clusters need. Rather than a self-serve cloud console, FluidStack works with AI labs and enterprises to provision large reserved clusters, with orchestration tooling for the ML frameworks and schedulers those teams already use.
FluidStack builds and operates large-scale compute infrastructure for AI, including gigawatt-class data centers and GPU clusters used for training and inference. The company designs its own power, cooling and data center systems, onshores manufacturing and deploys solar generation to supply the power its clusters need. Rather than a self-serve cloud console, FluidStack works with AI labs and enterprises to provision large reserved clusters, with orchestration tooling for the ML frameworks and schedulers those teams already use.
General Compute is an inference cloud built around alternative AI silicon rather than standard GPU fleets. The company buys racks of accelerators, racks them in colocation data centres, and handles the cooling, power, and networking itself. On top of that hardware it runs a disaggregated inference stack that splits each request: prompt processing runs on NVIDIA GPUs for prefill, while token generation runs on faster decode chips such as Cerebras, SambaNova, Positron AI, and d-Matrix. Customers get bare metal access or a tokens-per-second SLA.
General Compute is an inference cloud built around alternative AI silicon rather than standard GPU fleets. The company buys racks of accelerators, racks them in colocation data centres, and handles the cooling, power, and networking itself. On top of that hardware it runs a disaggregated inference stack that splits each request: prompt processing runs on NVIDIA GPUs for prefill, while token generation runs on faster decode chips such as Cerebras, SambaNova, Positron AI, and d-Matrix. Customers get bare metal access or a tokens-per-second SLA.