# FluidStack vs Verda (formerly Takomo)

FluidStack records 154,620 monthly visits with +7.3% month-over-month growth and Paid, Contact for Pricing pricing. Verda (formerly Takomo) records 0 monthly visits with 0% growth and Contact for Pricing pricing.

| Metric | [FluidStack](https://openaitools.app/tool/fluidstack) | [Verda (formerly Takomo)](https://openaitools.app/tool/takomo) |
| :--- | :--- | :--- |
| Website URL | https://fluidstack.io | https://www.takomo.ai |
| Categories | AI Developer Tools, AI Models, Large Language Models (LLMs) | No-Code&Low-Code, AI Workflow, AI Models, AI App Builder |
| 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 | 4.4 |
| Saved Count | 1 | 7 |
| 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)

## Verda (formerly Takomo)

Verda (previously presented as Takomo and linked to the DataCrunch brand) sells cloud infrastructure shaped around AI workloads. The portfolio spans compute, storage, networking and, more recently, a managed inference layer. Available accelerators include the newest NVIDIA data-center parts such as GB300 NVL72, B300 SXM6, B200 SXM6, RTX PRO 6000, H200, H100 and A100, alongside dedicated CPU instances with high clock speeds and NVMe block storage.

For developers, the platform emphasises self-service access: on-demand GPU instances can be provisioned without sales negotiations, instant clusters scale across multiple racks with InfiniBand or RoCE interconnect, and serverless containers autoscale for inference and batch processing. A newer Inference offering exposes frontier open-weight models through an OpenAI-compatible endpoint billed per token, with cached input pricing, so existing code that targets the OpenAI API can switch base URL and run unchanged.

The company highlights European ownership and data-center control, renewable energy, and a compliance posture that includes SOC 2 Type II, ISO 27001 and GDPR alignment, with confidential computing options for hardware-attested inference and fine-tuning. An in-house AI Lab contributes to open-source projects such as SGLang and vLLM, and publishes GPU benchmarks for prospective customers.

**Pricing & Plans:** Pricing is usage-based and published per GPU instance type, storage and inference tokens; enterprise and reserved-capacity deals are arranged by contacting sales.

- **NVIDIA GPU instances on demand:** Provision current data-center accelerators from RTX PRO 6000 up to GB300 NVL72 with NVLink v5, without contracts or negotiations.
- **Instant clusters for large training:** Self-service clusters with InfiniBand or RoCE interconnect span from a single tray to multiple racks for distributed training.
- **Serverless containers:** Auto-scaling GPU containers handle inference and batch jobs without managing the underlying cluster.
- **OpenAI-compatible inference API:** Call frontier open-weight models such as Kimi K3.1 Pro through a single endpoint with per-token pricing and cached input discounts.
- **Managed storage and networking:** NVMe block volumes, POSIX shared file systems, S3-compatible object storage and an OCI container registry round out the platform.

**Strengths:** Broad range of current NVIDIA accelerators available self-service; Vertical integration from owned data centers to managed services; OpenAI-compatible inference endpoint reduces code changes; Strong European compliance and renewable-energy positioning

**Limitations:** Public pricing detail varies by instance type and can require sales contact; Inference offering is newer, so model catalog breadth may lag larger clouds; Regional capacity availability depends on specific data-center locations

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

## Frequently Asked Questions

### How do FluidStack and Verda (formerly Takomo) compare in monthly traffic?

FluidStack has 154,620 monthly visits, while Verda (formerly Takomo) 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%; Verda (formerly Takomo) 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. Verda (formerly Takomo): Contact for Pricing. Pricing is usage-based and published per GPU instance type, storage and inference tokens; enterprise and reserved-capacity deals are arranged by contacting sales.

## 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 | 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) |
| 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 | [RunComfy — Cloud ComfyUI with Fast GPUs & Serverless API](https://openaitools.app/tool/runcomfy) | 364,336 | -8.6% | Contact for Pricing | Run native ComfyUI in the cloud with zero setup, fast GPUs, and one-click serverless APIs | [.md](https://openaitools.app/tool/runcomfy.md) |

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Source: https://openaitools.app/compare/fluidstack-vs-takomo

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