# DeepInfra vs FluidStack

DeepInfra records 457,810 monthly visits with -8.9% month-over-month growth and Paid pricing. FluidStack records 154,620 monthly visits with +7.3% growth and Paid, Contact for Pricing pricing.

| Metric | [DeepInfra](https://openaitools.app/tool/deep-infra) | [FluidStack](https://openaitools.app/tool/fluidstack) |
| :--- | :--- | :--- |
| Website URL | https://deepinfra.com | https://fluidstack.io |
| Categories | AI Models, AI API, Large Language Models (LLMs), AI Text Generator | AI Developer Tools, AI Models, Large Language Models (LLMs) |
| Pricing Model | Paid | Paid, Contact for Pricing |
| Monthly Visits | 457,810 | 154,620 |
| MoM Growth Rate | -8.9% | +7.3% |
| Primary Market | United States | United States |
| Direct Visits | 237,817 | 47,869 |
| Review Score | 4.6 | 4.4 |
| Saved Count | 0 | 1 |
| Supported Platforms | Website | Website |

## DeepInfra

DeepInfra positions itself as an inference cloud rather than a model lab: the product is fast, cheap, reliable access to models other people trained. The catalog spans more than 200 models grouped by task (text generation, text-to-image, text-to-music, text-to-speech, text-to-video, automatic speech recognition, embeddings, rerankers, zero-shot image classification) and by family, so you can pick a Claude, DeepSeek, Flux, Gemini, GLM, Kimi, Llama, Mistral, Nemotron or Qwen model and call it through the same API. Pricing is transparent and usage-based, quoted per million tokens with separate input, output and cached-input rates, and many models carry promotional discounts.

Beyond plain model hosting, DeepInfra runs its own inference-optimized hardware in US-based data centers and layers managed options on top: DeepStart for deploying your own models, DeepCluster for GPU clusters and on-demand GPU rental billed per instance-hour, plus a chat playground for quick testing. The company emphasizes security and privacy — a zero-retention policy for inputs and outputs, SOC 2 and ISO 27001 certifications — which matters for teams handling sensitive data. A large funding round (announced as a $107M Series B) and reported 25x growth in token volume signal an actively scaling business.

Practically, DeepInfra is aimed at engineering teams who want OpenAI-compatible style API calls without managing infrastructure, at startups that need cheap high-volume inference, and at organizations that want to rent GPUs for fine-tuning or custom serving on the same platform they use for hosted models.

**Pricing & Plans:** Paid, pay-as-you-go. Model inference is billed per million tokens with separate input, output and cached-input rates, and many models show temporary percentage discounts. On-demand GPUs are billed per instance-hour. No long-term contracts; enterprise support and custom arrangements are available via contact sales.

- **200+ Model Catalog:** Access text, image, speech, music and video models from multiple families through one consistent API, with task- and family-based browsing.
- **Transparent Token Pricing:** Per-million-token rates with input, output and cached-input breakdowns shown up front, including current discounts on many models.
- **On-Demand GPU Rental:** Rent data-center GPUs by the instance-hour when you need raw compute for fine-tuning, custom serving or burst workloads.
- **Zero-Retention Privacy:** Inputs and outputs are not retained, and the platform holds SOC 2 and ISO 27001 certifications for information security.
- **Managed Deployment Options:** DeepStart and DeepCluster let teams deploy their own models and scale clustered inference without building the infrastructure themselves.

**Strengths:** Very broad model catalog covering text, image, audio and video tasks behind one API; Transparent per-token pricing with cached-input rates and frequent promotional discounts; Security posture is documented: zero retention, SOC 2 and ISO 27001; Self-hosted, inference-optimized hardware with GPU rental and cluster options for scaling

**Limitations:** Costs are metered per token, so spend can grow quickly with high-volume or long-context workloads; The catalog changes fast, which means model names and prices need periodic rechecking; Some models in the listing are previews or promotional, so production reliability varies by model

[Profile (Markdown)](https://openaitools.app/tool/deep-infra.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 DeepInfra and FluidStack compare in monthly traffic?

DeepInfra has 457,810 monthly visits, while FluidStack has 154,620. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

DeepInfra has a month-over-month growth rate of -8.9%; FluidStack has +7.3%.

### How does pricing compare?

DeepInfra: Paid. Paid, pay-as-you-go. Model inference is billed per million tokens with separate input, output and cached-input rates, and many models show temporary percentage discounts. On-demand GPUs are billed per instance-hour. No long-term contracts; enterprise support and custom arrangements are available via contact sales. 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 | [Google AI Studio](https://openaitools.app/tool/google-ai-studio) | 112,620,020 | +2% | Freemium | Provides direct developer API access to Google's proprietary Gemini models rather than community open-weight models. | [.md](https://openaitools.app/tool/google-ai-studio.md) |
| 2 | [OpenRouter](https://openaitools.app/tool/openrouter-ai) | 19,941,437 | -6.2% | Paid | Acts as an API router across multiple hosting providers rather than hosting models directly like DeepInfra. | [.md](https://openaitools.app/tool/openrouter-ai.md) |
| 3 | [Groq](https://openaitools.app/tool/groq) | 3,987,936 | +10% | Paid | Delivers significantly faster token generation speeds using specialized LPU hardware, though supporting a smaller model catalog. | [.md](https://openaitools.app/tool/groq.md) |
| 4 | [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) |
| 5 | [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) |
| 6 | [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) |
| 7 | [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) |
| 8 | [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) |
| 9 | [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) |
| 10 | [Replicate AI](https://openaitools.app/tool/replicate-ai) | 904,038 | -8.4% | Paid | Stronger ecosystem for deploying custom machine learning models and fine-weights, but generally higher inference pricing. | [.md](https://openaitools.app/tool/replicate-ai.md) |

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Source: https://openaitools.app/compare/deep-infra-vs-fluidstack

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