# FluidStack vs General Compute

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. General Compute records 13,828 monthly visits with +7.2% growth and Free Trial, Contact for Pricing pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [General Compute](https://openaitools.app/tool/general-compute) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://www.generalcompute.com |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI API, AI Models, Large Language Models (LLMs), AI Developer Tools |
| Pricing Model | Paid, Contact for Pricing | Free Trial, Contact for Pricing |
| Monthly Visits | 154,620 | 13,828 |
| MoM Growth Rate | +7.3% | +7.2% |
| Primary Market | United States | United States |
| Direct Visits | 47,869 | 6,732 |
| Review Score | 4.4 | 4.2 |
| 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)

## General Compute

General Compute describes itself as the inference cloud for alternative chips. Rather than reselling standard GPU instances, the company acquires hardware directly, places it in colocation data centres, and takes responsibility for everything around the silicon: cooling sized per chip, power delivery, fast links between racks, and the software layer that makes heterogeneous hardware usable. Customers then buy either bare metal or a managed endpoint with a tokens-per-second SLA, priced per contract according to term, volume, and site.

Its central technical idea is disaggregated inference. Prompt prefill is compute-heavy but relatively short, so it runs on NVIDIA GPUs. Token decode is latency-bound, so it runs on faster-purpose silicon, including Cerebras CS-3 wafer-scale systems, SambaNova, Positron AI, and d-Matrix accelerators. Splitting the two stages lets each rack serve more users while each user still receives tokens quickly; General Compute reports up to 2.8x the throughput per rack at a given per-user speed, and up to 16.2x end-to-end gains in its open benchmarks.

The company has raised a $100M Series A, works with chip vendors and colocation partners, and reports more than 30 MW of capacity, with deployments scheduled to go live from Q1 2027. It positions itself mainly for latency-sensitive workloads, especially agentic coding tools and other applications where many sequential model calls make per-token speed the real bottleneck.

**Pricing & Plans:** Pricing is contract-based and requires a call; it is quoted per engagement according to term, volume, and site rather than published as self-serve rates. Indications of free trial availability exist, but no public price list is shown.

- **Disaggregated prefill and decode:** Prompt processing runs on NVIDIA GPUs while token generation runs on faster ASICs from Cerebras, SambaNova, Positron AI, and d-Matrix.
- **Bare metal or SLA endpoint:** Buy racks of GPUs for prefill and ASICs for decode, with General Compute handling cooling, power, networking, and the orchestration software.
- **Per-chip specifications:** Inspect each accelerator in an interactive 3D view with its SRAM, power draw, cooling type, and the workloads it suits best.
- **Software for non-standard silicon:** The platform abstracts bespoke hardware so models run without customers having to learn a new chip toolchain.
- **Throughput SLA and failover:** Multi-provider redundancy and automatic failover for production inference, backed by contracted throughput guarantees.

**Strengths:** Purpose-built for very low token latency rather than generic GPU rental; Handles cooling, power, networking, and orchestration so customers don't have to; Mix of GPU prefill and ASIC decode can improve throughput per rack; Well funded ($100M Series A) with named chip and partner ecosystem

**Limitations:** Pricing is contract-based rather than published per-token rates, which slows down self-serve evaluation; No instant public signup for compute; access begins with a sales conversation; Newest deployments are scheduled from Q1 2027, so capacity is in ramp-up; Workloads need to fit the disaggregated prefill/decode model to get the full speed benefit

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

## Frequently Asked Questions

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

FluidStack has 154,620 monthly visits, while General Compute has 13,828. 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%; General Compute has +7.2%.

### 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. General Compute: Free Trial, Contact for Pricing. Pricing is contract-based and requires a call; it is quoted per engagement according to term, volume, and site rather than published as self-serve rates. Indications of free trial availability exist, but no public price list is shown.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Groq](https://openaitools.app/tool/groq) | 3,987,936 | +10% | Paid | Ultra-fast AI inference on the LPU and LPX platform | [.md](https://openaitools.app/tool/groq.md) |
| 2 | [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) |
| 3 | [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) |
| 4 | [Together AI](https://openaitools.app/tool/together-ai) | 1,645,930 | +64.9% | Freemium, Paid | 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 b | [.md](https://openaitools.app/tool/together-ai.md) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [Cerebras](https://openaitools.app/tool/cerebras) | 701,013 | -12.6% | Contact for Pricing | The world's fastest AI inference, powered by the world's biggest chip | [.md](https://openaitools.app/tool/cerebras.md) |
| 10 | [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) |

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

More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)