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最佳 Thunder Compute 替代方案與競爭對手
依功能與用途，為你挑選 12 款 Thunder Compute 的替代工具。
替代方案總數
12
免費／免費增值方案
6
造訪次數最多的替代方案
RunPod
最大流量變化
Modal
全部 12 個 Thunder Compute 替代方案
瀏覽所有 AI 開發者工具工具 →Thunder Compute 與競爭對手比較
| 排名 | 工具與標語 | 官方網域 | 每月造訪次數 | 月增幅 |
|---|
依功能與用途，為你挑選 12 款 Thunder Compute 的替代工具。
| 排名 | 工具與標語 | 官方網域 | 每月造訪次數 | 月增幅 |
|---|
| 定價模式 |
|---|
| 主要市場 |
|---|
| 評分 |
|---|
| 比較 |
|---|
| ● | Thunder Compute | thundercompute.com | 10.21萬 | -4.17萬 (-29%) | Paid (per-minute instances, per-second sandboxes) | 美國 | 4.4 | 簡介 |
| 1 | RunPod | runpod.io | 288.1萬 | 16.15萬 (+5.9%) | 付費 | 美國 | 4.7 | 比較 |
| 2 | Lightning AI | lightning.ai | 27.26萬 | -3.81萬 (-12.3%) | 免費增值、付費、需聯絡報價 | 美國 | 4.7 | 比較 |
| 3 | Modal | modal.com | 119.6萬 | 27.72萬 (+30.2%) | 免費增值、需聯絡報價 | 美國 | 4.8 | 比較 |
| 4 | Vast.ai | vast.ai | 125.76萬 | 3.42萬 (+2.8%) | 付費 | 美國 | 4.6 | 比較 |
| 5 | Nebius | nebius.com | 114.57萬 | 11.08萬 (+10.7%) | 付費、需聯絡報價 | 美國 | 4.5 | 比較 |
| 6 | FluidStack | fluidstack.io | 15.46萬 | 1.05萬 (+7.3%) | 付費、需聯絡報價 | 美國 | 4.4 | 比較 |
| 7 | Massed Compute | massedcompute.com | 1.18萬 | -2.1萬 (-64.1%) | 付費、需聯絡報價 | 美國 | 4.2 | 比較 |
| 8 | Cerebrium | cerebrium.ai | 2.7萬 | -2.78萬 (-50.8%) | 免費試用、付費 | 美國 | 4.3 | 比較 |
| 9 | Cirrascale | cirrascale.com | 2.72萬 | 4046 (+17.5%) | 付費 | 美國 | 4.2 | 比較 |
| 10 | Novita AI | novita.ai | 27.14萬 | -3.15萬 (-10.4%) | 付費 | 美國 | 4.4 | 比較 |
| 11 | Salad | salad.com | 71.35萬 | 6.75萬 (+10.4%) | 免費試用、付費、需聯絡報價 | 美國 | 4.3 | 比較 |
| 12 | General Compute | generalcompute.com | 1.38萬 | 924 (+7.2%) | 免費試用、需聯絡報價 | 美國 | 4.2 | 比較 |
免費 / 開源由 PyTorch Lightning 創建團隊打造的 AI 雲端平台
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.
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.
Massed Compute is a cloud GPU provider offering on-demand NVIDIA instances, single-tenant bare metal servers, large InfiniBand node clusters, and a programmatic REST API and MCP server. It targets AI training, inference, machine learning, data analytics, and VFX rendering workloads.
Massed Compute is a cloud GPU provider offering on-demand NVIDIA instances, single-tenant bare metal servers, large InfiniBand node clusters, and a programmatic REST API and MCP server. It targets AI training, inference, machine learning, data analytics, and VFX rendering workloads.
為訓練與推論工作負載打造的私有 AI 雲
免費 / 開源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.
免費 / 開源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.
