# FluidStack vs Trelent

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. Trelent records 0 monthly visits with 0% growth and Contact for Pricing pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [Trelent](https://openaitools.app/tool/trelent) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://www.trelent.com |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | AI Developer Tools, AI Consulting, Large Language Models (LLMs), AI Workflow |
| Pricing Model | Paid, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 154,620 | 0 |
| MoM Growth Rate | +7.3% | 0% |
| Primary Market | United States | Global |
| Direct Visits | 47,869 | 0 |
| Review Score | 4.4 | 4.1 |
| Saved Count | 1 | 1 |
| 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)

## Trelent

Trelent is a private AI platform aimed at organisations whose most valuable workflows are blocked by data sensitivity. Instead of pointing staff at a public chatbot, Trelent deploys its stack inside the customer's own environment, so confidential documents and records never leave the organisation's perimeter.

The platform is built in three layers. Data ingestion connects file stores and databases and converts them into AI-ready context; intelligent search retrieves by meaning rather than keyword matching; and agent orchestration chains multi-step workflows so each step gets the right context at the right time. On top of this foundation Trelent also operates a forward-deployed engineering team that builds specific automations alongside customers.

Typical engagements start with a short discovery phase, then a paid validation stage of two to four months while ROI is proven, after which pricing shifts to an outcome metric such as clients onboarded or cases resolved. Published use cases include client onboarding, financial statement screening, legal document review and compliance monitoring.

**Pricing & Plans:** Contact for pricing. Trelent runs a two-stage model: a 2-4 month validation engagement at roughly $10k-$25k per month depending on scope, followed by outcome-based pricing tied to an agreed business metric such as clients onboarded or issues resolved.

- **Private-by-default deployment:** The whole stack runs inside your own environment, so sensitive data never leaves your perimeter and compliance requirements are met from day one.
- **Data ingestion:** Connect file stores and databases and have Trelent convert them into structured, AI-ready context rather than a loose pile of documents.
- **Intelligent semantic search:** Search across your data by meaning instead of literal keywords, so answers surface even when terminology differs between documents.
- **Agent orchestration:** Chain multi-step AI workflows that pull the right context at the right moment instead of relying on a single shallow prompt.
- **Forward-deployed engineering:** Trelent's own engineers build and migrate the automations with your team, shortening time to a first working system to days or weeks.

**Strengths:** Stack is self-hosted, which suits regulated industries with strict data-residency or confidentiality rules; Blends platform software with hands-on engineering help from a forward-deployed team; Pricing model aligns incentives by shifting to outcome-based metrics once ROI is proven; Three-layer architecture (ingestion, semantic search, orchestration) is designed for multi-step workflows rather than one-off prompts

**Limitations:** No self-serve signup or transparent public price list; you must book a call to get engaged; Reported entry retainer of $10k-$25k per month puts it out of reach for small teams; Requires committing to a 2-4 month validation phase before outcome-based terms kick in

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

## Frequently Asked Questions

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

FluidStack has 154,620 monthly visits, while Trelent has 0. 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%; Trelent has 0%.

### 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. Trelent: Contact for Pricing. Contact for pricing. Trelent runs a two-stage model: a 2-4 month validation engagement at roughly $10k-$25k per month depending on scope, followed by outcome-based pricing tied to an agreed business metric such as clients onboarded or issues resolved.

## 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 | 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) |
| 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 | Provides dedicated NVIDIA GPU clusters and managed cloud infrastructure, competing directly on enterprise-grade training workloads. | [.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-trelent

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