# Actcast vs FluidStack

Actcast records 585 monthly visits with -59.9% month-over-month growth and Contact for Pricing pricing. FluidStack records 154,620 monthly visits with +7.3% growth and Paid, Contact for Pricing pricing.

| Metric | [Actcast](https://openaitools.app/tool/actcast-io) | [FluidStack](https://openaitools.app/tool/fluidstack) |
| :--- | :--- | :--- |
| Website URL | https://actcast.io | https://fluidstack.io |
| Categories | AI Developer Tools, AI Image Recognition, AI Models | AI Developer Tools, AI Models, Large Language Models (LLMs) |
| Pricing Model | Contact for Pricing | Paid, Contact for Pricing |
| Monthly Visits | 585 | 154,620 |
| MoM Growth Rate | -59.9% | +7.3% |
| Primary Market | Japan | United States |
| Direct Visits | 200 | 47,869 |
| Review Score | 4.1 | 4.4 |
| Saved Count | 0 | 1 |
| Supported Platforms | Website | Website |

## Actcast

Actcast is an IoT platform built around the idea of "intelligent sensing for everyone." Rather than streaming raw video to a cloud for processing, Actcast runs deep learning inference directly on inexpensive edge devices, typically Raspberry Pi hardware, and forwards only the meaningful events and metadata to the cloud. That design keeps bandwidth and latency low while sidestepping many of the privacy concerns of continuous video upload.

The platform is organized around Acts, which are packaged sensing applications that can be installed onto registered devices. Users set up and manage devices and groups through the Actcast console, choose from a catalog of supported hardware, and connect the resulting detections to downstream systems through APIs and webhooks. This makes it practical to build things like counted footfall, occupancy monitoring, or notification triggers without writing a full CV pipeline from scratch.

Actcast targets developers and companies that want to add ambient sensing to physical spaces — retail, offices, factories and smart-building deployments. Because capabilities are delivered as installable Acts and managed remotely, teams can iterate on which signals they care about over time instead of committing to a one-off hardware integration.

**Pricing & Plans:** Actcast does not publish a self-serve price list; pricing is arranged through a contact/demo conversation, typically taking into account the number of devices, the Acts you deploy and the scale of your deployment.

- **Edge deep-learning inference:** Runs trained models on local edge devices such as Raspberry Pi so events are detected on-site and only metadata is sent onward.
- **Installable Acts:** Sensing applications are packaged as Acts that can be published, discovered and installed onto registered devices.
- **Device and group management:** A central console lets you register, organize and monitor devices and groups across one or many locations.
- **Webhook and API integration:** Detected events can be pushed to your own services through webhooks and APIs, making it easy to wire Actcast into existing applications.
- **Supported device program:** A curated list of supported hardware keeps deployments predictable, so you know which cameras and boards work out of the box.

**Strengths:** Keeps video and inference on the edge, reducing bandwidth, latency and privacy exposure.; Acts model means you can deploy useful sensing without a full ML team.; Central console makes managing many remote devices and groups manageable.; Clean webhook/API path for connecting detections to existing software.

**Limitations:** Pricing requires a sales conversation, so budgeting is not instant.; Best experience depends on supported hardware, which constrains DIY builds.; Console-based tooling has a learning curve for teams new to edge deployments.

[Profile (Markdown)](https://openaitools.app/tool/actcast-io.md)

## 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)

## Frequently Asked Questions

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

Actcast has 585 monthly visits, while FluidStack has 154,620. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Actcast has a month-over-month growth rate of -59.9%; FluidStack has +7.3%.

### How does pricing compare?

Actcast: Contact for Pricing. Actcast does not publish a self-serve price list; pricing is arranged through a contact/demo conversation, typically taking into account the number of devices, the Acts you deploy and the scale of your deployment. 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.

## 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 | 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) |
| 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 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Build, train, and deploy computer vision for the physical world | [.md](https://openaitools.app/tool/roboflow.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 | [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) |

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Source: https://openaitools.app/compare/actcast-io-vs-fluidstack

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