# FluidStack vs Mercor

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. Mercor records 14,164,192 monthly visits with +34% growth and Contact for Pricing pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [Mercor](https://openaitools.app/tool/mercor-com) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://mercor.com |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Recruiting, AI Jobs, AI Interview Assistant, AI Resume Checker |
| Pricing Model | Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 154,620 | 14,164,192 |
| MoM Growth Rate | +7.3% | +34% |
| Primary Market | United States | United States |
| Direct Visits | 47,869 | 7,518,232 |
| Review Score | 4.4 | 4.4 |
| Saved Count | 1 | 3 |
| 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)

## Mercor

Mercor started as an AI recruiting marketplace that used machine learning to match candidates with remote jobs faster than traditional hiring. The platform interviews and vets applicants automatically, then routes matched professionals to short-term and long-term engagements that range from software and data work to specialist expert tasks.

Over time Mercor expanded well beyond recruiting. It now positions itself around "organizing human intelligence to power the AI economy," running interfaces for frontier research, AI benchmarks and AI-agent training at scale. Its enterprise arm builds industry benchmarks with partner organizations, sells human evaluation capacity to frontier labs, and lets businesses turn their own workflow data into a new revenue stream through anonymized licensing.

Mercor reports hundreds of thousands of roles created, an average contracted expert rate around $90 per hour and millions in daily payouts. It has raised significant venture funding and works with leading AI labs and large enterprises, serving both experts looking for flexible paid work and companies that need human data, evaluations or AI-ready talent.

**Pricing & Plans:** Free to sign up for experts; Mercor takes no fee from talent. Enterprises and AI labs pay custom, quote-based rates for data, benchmarking and evaluation services — contact sales for details.

- **AI-powered talent matching:** Machine-learning models screen and match candidates to remote roles in hours rather than weeks, based on skills, experience and availability.
- **Automated expert vetting:** AI-driven interviews and assessments validate professional expertise before experts are surfaced to hiring partners.
- **Paid expert engagements:** Experts work on flexible, remote projects with rates reported to average around $90 per hour and fast, recurring payouts.
- **Industry benchmark building:** Mercor collaborates with enterprises to design benchmarks such as legal, accounting and software-engineering evaluations for AI agents.
- **Enterprise data monetization:** Businesses can license anonymized workflow data to Mercor, with automated masking of sensitive identifiers and full control over what is shared.

**Strengths:** Fast, AI-driven matching that shortens time-to-hire dramatically; High reported pay rates and quick, frequent payouts for experts; Broad scope beyond recruiting — benchmarks, evals and data monetization; Works with major AI labs and enterprises, signaling strong credibility

**Limitations:** Enterprise pricing is quote-only, so costs are not transparent upfront; Work is project-based and may be irregular for some experts; Availability depends on demand from AI labs and enterprise partners

[Profile (Markdown)](https://openaitools.app/tool/mercor-com.md)

## Frequently Asked Questions

### How do FluidStack and Mercor compare in monthly traffic?

FluidStack has 154,620 monthly visits, while Mercor has 14,164,192. 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%; Mercor has +34%.

### 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. Mercor: Contact for Pricing. Free to sign up for experts; Mercor takes no fee from talent. Enterprises and AI labs pay custom, quote-based rates for data, benchmarking and evaluation services — contact sales for details.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Workable](https://openaitools.app/tool/workable) | 10,780,627 | +5.5% | Free Trial, Paid | Offers robust general HR and recruiting tools with native AI agents, but lacks Mercor's specialized expert network. | [.md](https://openaitools.app/tool/workable.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 | Offers flexible on-demand GPU rentals and serverless endpoints, but lacks FluidStack's ultra-scale enterprise GPU clusters. | [.md](https://openaitools.app/tool/runpod.md) |
| 4 | [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) |
| 5 | [Vast.ai](https://openaitools.app/tool/vast-ai) | 1,257,554 | +2.8% | Paid | Delivers peer-to-peer GPU rental marketplaces for lower-cost experimentation, though with less enterprise infrastructure predictability. | [.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 | Provides serverless Python container execution for cloud jobs, but lacks raw multi-node cluster provisioning controls. | [.md](https://openaitools.app/tool/modal-com.md) |
| 7 | [Nebius](https://openaitools.app/tool/nebius) | 1,145,711 | +10.7% | Paid, Contact for Pricing | Provides dedicated NVIDIA GPU clusters and managed cloud infrastructure, competing directly on enterprise-grade training workloads. | [.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 | Utilizes proprietary wafer-scale processors for extreme training speeds, differing from standard NVIDIA GPU cluster setups. | [.md](https://openaitools.app/tool/cerebras.md) |
| 10 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Supplies enterprise AI training data and expert model evaluations, operating on the infrastructure side of AI labs. | [.md](https://openaitools.app/tool/scale.md) |

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Source: https://openaitools.app/compare/fluidstack-vs-mercor-com

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