# Prem vs Scale

Prem records 45,892 monthly visits with +4.3% month-over-month growth and Contact for Pricing pricing. Scale records 616,915 monthly visits with +12.5% growth and Contact for Pricing pricing.

| Metric | [Prem](https://openaitools.app/tool/prem) | [Scale](https://openaitools.app/tool/scale) |
| :--- | :--- | :--- |
| Website URL | https://www.premai.io | https://scale.com |
| Categories | Open Source AI Models, Large Language Models (LLMs), AI Models, AI API | AI Developer Tools, Large Language Models (LLMs), AI Models, AI Agent |
| Pricing Model | Contact for Pricing | Contact for Pricing |
| Monthly Visits | 45,892 | 616,915 |
| MoM Growth Rate | +4.3% | +12.5% |
| Primary Market | United States | United States |
| Direct Visits | 11,903 | 191,408 |
| Review Score | 4.3 | 4.7 |
| Saved Count | 0 | 3 |
| Supported Platforms | Website | Website |

## Prem

Prem is an enterprise AI company built around the idea of sovereign intelligence: verifiable, private and owned by the organization rather than rented from a public cloud API. Its flagship developer product, Enclave, runs frontier open models on encrypted GPUs with hardware-verified attestation at every layer — the company's own claim is that even Prem cannot access your data. Language models, vision-language models and secure voice transcription are available through it.

On top of that infrastructure, Prem offers Fluso, an agent platform for building and deploying enterprise agents grounded in proprietary SOPs, with self-improving skills, connectors to more than a hundred enterprise tools, a no-code builder and event or schedule triggers. Prem Studio handles model customization and fine-tuning on private data for teams that need their own tuned models.

The target market is regulated organizations — healthcare, financial services, government and legal — where public cloud AI is often ruled out. Published case studies describe a European RegTech firm processing regulatory documents inside its own perimeter, a North American healthcare provider extracting structured data from millions of patient records under HIPAA, and a Fortune 50 retailer running on-premise demand forecasting. Prem is designed and powered in Switzerland with offices in Lugano, Wilmington and Italy, and pricing is arranged through a demo.

**Pricing & Plans:** Contact for pricing. Prem is an enterprise platform sold through demos and custom agreements rather than published tiers; developers can start with the documentation and API, and use the model playground to evaluate capabilities.

- **Encrypted inference (Enclave):** Run frontier models on encrypted GPUs with hardware-verified attestation at every layer, so the provider cannot access your data.
- **Private agents (Fluso):** An agent platform for building and deploying enterprise agents on your own SOPs, with skills, 100+ tool connectors and no-code building.
- **Model customization (Prem Studio):** Customize and fine-tune models on private data — vision, language and secure voice transcription included.
- **Deployment inside your perimeter:** Deploy inside your own perimeter so data residency and compliance rules for healthcare, finance, government and legal are respected.
- **Compliance posture:** SOC 2 Type I, ISO 27001 and FADP referenced, with case studies across regulated industries.

**Strengths:** Strong privacy story: encrypted inference with hardware attestation.; Covers inference, agents and model customization in one platform.; Aimed squarely at regulated industries with compliance references.; Open-model friendly and available as a deployable, in-perimeter stack.

**Limitations:** Enterprise-focused, so there is no simple self-serve signup for small teams.; Pricing requires a demo and is not published publicly.; Harnessing the full platform benefits from engineering resources.

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

## Scale

Scale positions itself as the infrastructure layer for reliable AI - the data, evaluations and deployment support that sit underneath working systems rather than a single consumer-facing model. Founded in 2016, it built its reputation supplying training data and labelling capacity to the autonomous vehicle and then the generative AI wave, and now describes itself as powering a large share of the world's leading generative AI model builders.

The offering spans three broad areas. Data covers sourcing and expert annotation for training and fine-tuning, including RLHF-style preference data. Evaluation covers public leaderboards and private benchmarks used to measure model capability and safety. Applications covers the build-and-deploy side: identifying a viable use case, constructing the system and owning the outcome, with agentic AI for enterprise operations.

Scale's customer list leans heavily enterprise and public sector, including large technology companies, healthcare providers, media organisations, energy companies and government defence programmes. Because engagements are scoped rather than self-serve, pricing is arranged through a sales process, and the platform is aimed at organisations with serious data volume, strict compliance needs and a mandate to put AI into production.

**Pricing & Plans:** Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity.

- **Data Engine:** Human-sourced and expert-annotated training data, including specialist contributors with advanced degrees, at the quality bar frontier labs require.
- **Model evaluation & benchmarks:** Public leaderboards plus private benchmarks that test model capability and safety for the most ambitious AI developers.
- **Enterprise AI applications:** Platform and services teams that pick the right use case, build the system and take ownership of the outcome.
- **Agentic deployment:** Agentic systems designed around measurable business outcomes across private equity portfolios, energy and healthcare operations.
- **Domain expertise:** Programmes spanning healthcare, robotics, defence, public sector and enterprise with human oversight built in.

**Strengths:** Backed by a long track record in training data and model evaluation.; End-to-end coverage from data sourcing through to deployed systems.; Experienced with regulated, classified and safety-critical environments.; Benchmarks and leaderboards give an independent view of model capability.

**Limitations:** No public pricing or self-serve signup - you need a sales conversation to get started.; Enterprise-scale contracting and data operations require real internal commitment.; Overkill for small teams with modest data or evaluation needs.; Some projects involve sensitive government and defence work with strict compliance overhead.

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

## Frequently Asked Questions

### How do Prem and Scale compare in monthly traffic?

Prem has 45,892 monthly visits, while Scale has 616,915. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

Prem has a month-over-month growth rate of +4.3%; Scale has +12.5%.

### How does pricing compare?

Prem: Contact for Pricing. Contact for pricing. Prem is an enterprise platform sold through demos and custom agreements rather than published tiers; developers can start with the documentation and API, and use the model playground to evaluate capabilities. Scale: Contact for Pricing. Contact for pricing. Scale does not publish rates; engagements are scoped through a sales conversation and depend on data volume, services and programme complexity.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [OpenAI Platform](https://openaitools.app/tool/openai) | 234,678,639 | -9.6% | Paid | Enterprise developer platform and API for deploying GPT-6 Astra, Sol, Luna, and realtime multimodal agents. | [.md](https://openaitools.app/tool/openai.md) |
| 2 | [BigModel](https://openaitools.app/tool/bigmodel) | 5,700,686 | -17% | Freemium | Zhipu AI's open platform for GLM large models, multimodal APIs, and agent building | [.md](https://openaitools.app/tool/bigmodel.md) |
| 3 | [Gemma Open Models by Google](https://openaitools.app/tool/gemma-open-models-by-google) | 4,900,914 | -13.5% | Freemium, Paid | Google's family of lightweight, open-weight AI models | [.md](https://openaitools.app/tool/gemma-open-models-by-google.md) |
| 4 | [Groq](https://openaitools.app/tool/groq) | 3,987,936 | +10% | Paid | Ultra-fast AI inference on the LPU and LPX platform | [.md](https://openaitools.app/tool/groq.md) |
| 5 | [fal.ai](https://openaitools.app/tool/fal-ai) | 3,093,684 | -3.7% | Paid, pay-as-you-go usage-based pricing | One API for 1,500+ production-ready generative media models | [.md](https://openaitools.app/tool/fal-ai.md) |
| 6 | [BytePlus](https://openaitools.app/tool/byteplus) | 3,036,127 | -30% | Freemium, Paid | AI-native cloud for building and scaling enterprise AI | [.md](https://openaitools.app/tool/byteplus.md) |
| 7 | [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) |
| 8 | [Roboflow](https://openaitools.app/tool/roboflow) | 1,431,204 | +6.6% | Freemium, Contact for Pricing | Streamlines computer vision dataset annotation and model training, but is less suited for general LLM text tasks. | [.md](https://openaitools.app/tool/roboflow.md) |
| 9 | [Unsloth AI](https://openaitools.app/tool/unsloth-ai) | 887,457 | -17.6% | Free | Open-source, memory-efficient fine-tuning and local model running | [.md](https://openaitools.app/tool/unsloth-ai.md) |
| 10 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Focuses heavily on enterprise data labeling and training data curation, similar to Scale's core annotation business. | [.md](https://openaitools.app/tool/labelbox.md) |

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Source: https://openaitools.app/compare/prem-vs-scale

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