# FluidStack vs Thunder Compute

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. Thunder Compute records 102,147 monthly visits with -29% growth and Paid (per-minute instances, per-second sandboxes) pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [Thunder Compute](https://openaitools.app/tool/thunder-compute) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://thundercompute.com |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI Models, Large Language Models (LLMs) |
| Pricing Model | Paid, Contact for Pricing | Paid (per-minute instances, per-second sandboxes) |
| Monthly Visits | 154,620 | 102,147 |
| MoM Growth Rate | +7.3% | -29% |
| Primary Market | United States | United States |
| Direct Visits | 47,869 | 40,743 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 1 | 0 |
| Supported Platforms | Website | Website |

## FluidStack

FluidStack is an AI compute provider that builds the physical layer underneath frontier model work: data centers, power, cooling and very large GPU clusters. Its public positioning is deliberately infrastructural rather than product-led — it describes building gigawatt-scale compute, bringing new sites online in months, and operating tens of millions of accelerators reliably. For teams training or serving large models, that translates into reserved, single-tenant-class capacity that is provisioned and operated for you rather than an instant, self-serve VM catalogue.

A distinguishing theme is the pairing of power and compute. FluidStack writes about redesigning compute and power infrastructure from first principles, developing off-grid solar to supply clusters, and using closed-loop cooling that fills tanks once and recirculates water indefinitely. It also emphasises building in the United States with onshored manufacturing, framing infrastructure ownership as a strategic question about which values shape the technology being built. For AI organisations, these are increasingly relevant because energy availability, not chip supply alone, is the binding constraint on scaling.

If you need a small number of GPUs for a day, FluidStack is probably not the fastest route; capacity is engaged at enterprise scale through a sales process. If you are planning a multi-month training run or a production inference fleet and want dedicated, purpose-built infrastructure with managed operations, it is a strong candidate to evaluate alongside the major hyperscalers and specialised GPU clouds.

**Pricing & Plans:** Enterprise and contact-for-pricing. FluidStack does not publish per-GPU-hour rates or a self-serve sign-up on its website; capacity is scoped and quoted through a direct sales conversation based on cluster size, duration and hardware. Catalogue listings classify it as a paid product requiring contact for pricing.

- **Datacenter-scale compute built in-house:** FluidStack designs and builds large AI data centers, including high-density campuses with significant per-site power capacity, rather than reselling capacity from other clouds.
- **GPU clusters for training and inference:** Clusters are architected for large-scale model training and inference, with high-bandwidth interconnect and orchestration suitable for distributed workloads.
- **Power and cooling engineered together:** The company develops off-grid solar and recirculating closed-loop cooling, treating power and water efficiency as core engineering problems.
- **Managed operations and reliability:** Smart hands, cluster provisioning and operations support let research teams focus on models while FluidStack runs the physical and orchestration layers.
- **US-built and onshored supply chain:** Data centers are built in the United States with onshored manufacturing of compute and power infrastructure, a deliberate supply-chain and sovereignty choice.

**Strengths:** Purpose-built AI infrastructure rather than generic cloud resale; Power, cooling and data center design handled in-house, which matters at scale; Managed provisioning and operations for large training and inference clusters; Explicit positioning on US-built, onshored infrastructure

**Limitations:** No published self-serve pricing or instant sign-up; access requires a sales conversation; Enterprise-scale focus means very small individual workloads may not be a fit; Capacity is concentrated in US locations, which may matter for latency-sensitive or data-residency-bound teams

[Profile (Markdown)](https://openaitools.app/tool/fluidstack.md)

## Thunder Compute

Thunder Compute is a cloud GPU provider that markets itself as the GPU sandbox company. Rather than a single generic VM offering, it splits its product into two shapes. Instances are long-lived GPU machines for hands-on development and training, available with RTX A6000, L40, A100 80GB, or H100 SXM GPUs, configurable across GPU count, vCPU, RAM, and disk, and billable per minute only while running. Sandboxes are isolated, short-lived Ubuntu microVMs designed for agent workloads: reinforcement-learning environments, evaluation sweeps, and CI jobs that need a GPU for a few minutes and can then disappear.

The company leans on price and developer ergonomics. Its published rates list H100 SXM instances at $3.20 per GPU-hour, A100 80GB at $1.09, L40 at $0.79, and RTX A6000 at $0.35, with per-second sandbox pricing of $2.95 per GPU-hour for H100 and $1.95 for A100. Storage is metered per GB-month with monthly snapshot pricing, and there are no data egress fees. Thunder Compute says it keeps costs down by scheduling workloads efficiently and reducing idle time, and passes those savings on.

The workflow is designed to be scriptable rather than click-heavy. A CLI handles instance creation and connection, a VS Code extension supports in-editor iteration, and an MCP server plus REST API let agents and internal platforms provision GPUs programmatically. Snapshots preserve disk state between sessions, and neither instances nor sandboxes can be preempted. The company raised a $13 million Series A in 2026 to expand the platform, and documents the stack through guides, quickstarts, and API references.

**Pricing & Plans:** Thunder Compute bills instances per minute while running and sandboxes per second. Published instance rates: RTX A6000 $0.35/GPU-hour, L40 $0.79, A100 80GB $1.09, H100 SXM $3.20, with extra vCPUs at $0.04/vCPU-hour. Sandbox rates are $2.95/GPU-hour for H100 SXM and $1.95/GPU-hour for A100 80GB plus vCPU, memory, and storage components. Instance disk storage is $0.219/GB per month (first 100 GB per GPU included), snapshots are $0.05/GB per month, and data egress is free. Grilled specifically: rates change over time, so verify on the pricing page.

- **Persistent GPU instances:** Launch RTX A6000, L40, A100 80GB, or H100 SXM machines, often ready to SSH into within minutes, and pay only while they run.
- **Per-second GPU sandboxes:** Spin up isolated Ubuntu VMs with or without a GPU for RL rollouts, evals, and CI, billed per second and scaled to zero between runs.
- **Developer-first tooling:** A CLI, VS Code extension, MCP server, and REST API let you drive infrastructure from a terminal, an editor, an AI agent, or your own services.
- **Snapshots and persistent storage:** Save an instance disk as a snapshot and restore it later, so you can pause experiments without leaving machines running.
- **No preemption, no egress fees:** Instances and sandboxes are never preempted, and outbound data transfer is not billed.

**Strengths:** Aggressive pricing versus hyperscalers: published H100 rates around $3.20/GPU-hour against $6+ at major clouds.; Two billing models that match two real workloads: per-minute persistent instances and per-second ephemeral sandboxes.; No egress fees and no preemption, which removes two common sources of surprise cost and interruption.; Real developer surface area: CLI, VS Code extension, MCP server, REST API, and templates.

**Limitations:** GPU availability can fluctuate, so a particular type may be sold out when you try to launch it.; Sandbox access is requested through sales rather than being fully self-serve at sign-up.; The platform's GPU catalog is narrower than the largest hyperscalers, which offer a wider range of accelerator families.

[Profile (Markdown)](https://openaitools.app/tool/thunder-compute.md)

## Frequently Asked Questions

### How do FluidStack and Thunder Compute compare in monthly traffic?

FluidStack has 154,620 monthly visits, while Thunder Compute has 102,147. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

FluidStack has a month-over-month growth rate of +7.3%; Thunder Compute has -29%.

### How does pricing compare?

FluidStack: Paid, Contact for Pricing. Enterprise and contact-for-pricing. FluidStack does not publish per-GPU-hour rates or a self-serve sign-up on its website; capacity is scoped and quoted through a direct sales conversation based on cluster size, duration and hardware. Catalogue listings classify it as a paid product requiring contact for pricing. Thunder Compute: Paid (per-minute instances, per-second sandboxes). Thunder Compute bills instances per minute while running and sandboxes per second. Published instance rates: RTX A6000 $0.35/GPU-hour, L40 $0.79, A100 80GB $1.09, H100 SXM $3.20, with extra vCPUs at $0.04/vCPU-hour. Sandbox rates are $2.95/GPU-hour for H100 SXM and $1.95/GPU-hour for A100 80GB plus vCPU, memory, and storage components. Instance disk storage is $0.219/GB per month (first 100 GB per GPU included), snapshots are $0.05/GB per month, and data egress is free. Grilled specifically: rates change over time, so verify on the pricing page.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | Delivers developer-first serverless endpoints optimized for generative media models rather than general-purpose infrastructure. | [.md](https://openaitools.app/tool/fal-ai.md) |
| 2 | [RunPod](https://openaitools.app/tool/runpod) | 2,881,043 | +5.9% | Paid | The AI developer cloud for GPU pods, serverless inference and clustering | [.md](https://openaitools.app/tool/runpod.md) |
| 3 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | Focuses on optimized serverless inference APIs and managed model hosting rather than raw cluster rentals. | [.md](https://openaitools.app/tool/together-ai.md) |
| 4 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Rent cloud GPUs by the second from a global marketplace | [.md](https://openaitools.app/tool/vast-ai.md) |
| 5 | [Modal](https://openaitools.app/tool/modal-com) | 1,196,021 | +30.2% | Freemium, Contact for Pricing | Serverless cloud infrastructure for AI and data workloads | [.md](https://openaitools.app/tool/modal-com.md) |
| 6 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | An AI-native cloud built from silicon to API for training, tuning, and serving models | [.md](https://openaitools.app/tool/nebius.md) |
| 7 | [Salad](https://openaitools.app/tool/salad-gpu-cloud) | 713,519 | +10.4% | Free Trial, Paid, Contact for Pricing | Distributed GPU cloud that orchestrates the world's idle consumer GPUs | [.md](https://openaitools.app/tool/salad-gpu-cloud.md) |
| 8 | [Cerebras](https://openaitools.app/tool/cerebras) | 701,013 | -12.6% | Contact for Pricing | Utilizes proprietary wafer-scale processors for extreme training speeds, differing from standard NVIDIA GPU cluster setups. | [.md](https://openaitools.app/tool/cerebras.md) |
| 9 | [DeepInfra](https://openaitools.app/tool/deep-infra) | 457,810 | -8.9% | Paid | Specializes in cost-effective serverless API inference for open-weights models instead of raw infrastructure rentals. | [.md](https://openaitools.app/tool/deep-infra.md) |
| 10 | [Lightning AI](https://openaitools.app/tool/lightning-ai) | 272,562 | -12.3% | Freemium, Paid, Contact for Pricing | The AI cloud built by the creators of PyTorch Lightning | [.md](https://openaitools.app/tool/lightning-ai.md) |

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Source: https://openaitools.app/compare/fluidstack-vs-thunder-compute

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