Scaleway Inference Provider: Is It Worth It?

⚡ Cold starts on Scaleway average under 30ms, with inference results delivered in under one second.
💰 Serverless inference on Scaleway costs ~$0.0020 per second—significantly cheaper than AWS or GCP.
🌍 EU-based Scaleway supports GDPR and data residency concerns better than many U.S.-centric providers.
🤖 Over 80 models were deployed on Scaleway within 30 days of joining Hugging Face Inference Providers.
🚀 Serverless inference takes care of all infrastructure. This lets no-code platforms add LLMs easily.

If you're building AI features into your product—like a chatbot on WordPress, an email generator on Make.com, or a custom agent on Bot-Engine—picking the right way to run models is very important. Hugging Face’s Inference API lets you easily use powerful models. Now, with Scaleway as a new Inference Provider, you have a faster and cheaper way to run models without handling servers. We will explain why Scaleway is a strong choice for creators and developers who want to make AI setups simpler using serverless infrastructure.

What Is a Hugging Face Inference Provider?

Hugging Face Inference is a service that handles everything. It lets you deploy and run thousands of pretrained deep learning models from the Hugging Face Hub. These include advanced large language models (LLMs) like LLaMA and BERT. You can also find vision models like CLIP, or models for specific tasks like translation, summarization, question answering, and text classification.

To make things even simpler, Hugging Face started using the idea of Inference Providers. These are cloud platforms that run the background work for your model setups. They include big cloud companies like AWS and Google Cloud Platform (GCP), and also specialized providers like CoreWeave. Now, Scaleway is one of them.

When you use Hugging Face’s inference endpoints, you don't just call a model. You run that model on a chosen provider's hardware. Hugging Face hides these details to make it simple. It manages how much power is needed, makes sure the service is always ready, and handles access. So, you only need to focus on adding APIs to your app or workflow.

Why This Matters

Development teams no longer need to worry about Kubernetes clusters or setting up GPUs to get almost instant access to powerful AI models. Instead, Hugging Face APIs send your request, run it on partner hardware, and give you a response smoothly. This greatly reduces problems with development and getting models running. And then, it makes serverless AI possible for creators, marketers, and developers.

Example Use Cases

AI co-writing assistants added to WordPress blogs.
Generators for summaries in newsletters and PR updates.
Tools for sentiment analysis on social listening platforms.
Contract analysis tools for legal tech.
Image moderation for user-generated content on platforms.
Translation services added to project management software.

Who Is Scaleway and Why Are They Important Now?

Scaleway is a European cloud hardware provider. It runs from several data centers in France and the Netherlands. With a focus on being eco-friendly, keeping data within its own country's laws, and high-performance computing (HPC), Scaleway offers something special compared to big cloud companies like AWS and GCP.

Scaleway started as part of the Iliad Group. It has grown into a cloud platform that offers many different services. These include bare-metal compute, GPU instances, object storage, managed Kubernetes, and—most importantly for this topic—serverless inference.

What makes Scaleway stand out isn't just its hardware. It's their strong focus on:

🎯 Developer simplicity
♻️ Environmental sustainability
🇪🇺 Compliance with EU regulations such as GDPR
💸 Transparent, startup-friendly pricing

Why It Matters for AI Workloads

Scaleway is well-suited to help European users who need fast, rule-following, and affordable AI compute. Its GPU-optimized machines are made for running models. They give quick response times with very small cold start delays.

Developers or businesses wanting to build LLM solutions that need to keep data in Europe get a lot of help from Scaleway being there. Scaleway became a Hugging Face Inference Provider, and this offers these benefits with no extra setup work. You just pick it from a menu—and it works.

Serverless Inference: What It Means for You

Serverless inference is a way of setting up models where worries about hardware are completely hidden. This includes setting up hardware, making sure servers are always on, handling more users, and making things faster. It gives you an an easy-to-use setup that is very helpful for:

Indie developers
Automation platforms
Freelancers building solutions for clients
Rapid prototyping use cases
Teams adding LLMs to existing SaaS apps

With serverless inference, your AI model starts only when needed. It does the work (like text generation or classification) and then shuts down. You only pay for what you use, and responses are given almost instantly for most needs.

Key Benefits

✅ No infrastructure to manage (no VMs, GPUs, or Kubernetes)
✅ Pay-per-use billing model avoids idle-time costs
✅ Perfect for sporadic or bursty workloads
✅ Ideal for low-latency use cases like chatbots or content scoring
✅ Integrates easily with automation tools like Make.com, Zapier, and Retool

Real-World Scenario

Imagine you're building a no-code automation on Make.com that creates product descriptions with an LLM. When you add a new product to your Shopify store, a webhook triggers an API call to Hugging Face. That request calls your chosen model—running on Scaleway—and gives back a description, all in seconds.

No containers, no GPU bills sitting unused overnight, no DevOps. Just smart automated tasks run by dependable hardware.

Scaleway’s Place in the Hugging Face Ecosystem

Scaleway joined the group of Hugging Face Inference Providers in early 2024. It became an option that can grow, is eco-friendly, and affordable. It sits alongside well-known companies like AWS, GCP, and CoreWeave.

You can now choose Scaleway right in the Hugging Face user interface when setting up your models using Inference Endpoints. It takes just minutes to get it ready.

Benefits of Native Integration

🔁 Full compatibility with Hugging Face API tokens and access workflows
🌎 Global availability for most endpoints
🤝 No separate Scaleway subscription needed
🪄 Automatically scales with request load
🎥 Works with streaming I/O and multi-turn conversation models

Developers can start using Scaleway's GPUs to run large models. They won't need to sign up for another cloud provider, set limits, or worry about delays between different locations. (If your users are mainly in Europe, Scaleway often works better than others).

What Models Can You Use with Scaleway?

Scaleway supports a large collection of community and business-ready models available on the Hugging Face Hub. If you're using basic LLMs, vision transformers, or custom-tuned models, serverless endpoints on Scaleway can run them well.

Here’s a sample of popular supported models:

Text Generation

🧠 Meta's LLaMA family: Very good for long, multi-language text generation.
💡 Mistral 7B: A small, open-weight model known for excellent reasoning.
🗞️ Falcon 180B: Known for its top performance on summarization and QA tests.

Text-to-Text

🧾 T5 (Text-To-Text Transfer Transformer): Excellent for translation, summarization, and question answering.
📚 BART: Widely used in summarization and data compression tasks.

Vision Models

👀 BLIP: Combines vision and language for captioning and image-questioning.
🔎 CLIP: Connects text and images through embeddings—perfect for image search and moderation.

Private and Custom Models

You can also deploy private models or custom-tuned versions for business needs. For example, a marketing agency might fine-tune a sentiment model to match their brand voice. Then, they could deploy it to Hugging Face and run it for clients using Scaleway’s fast endpoints.

Scaleway Performance: How Fast Is It?

How fast models run is very important for users. This is true for interactive apps like chatbots or tools that create content. Scaleway delivers.

According to internal Hugging Face performance indicators:

Cold starts typically under 30ms and inference under 1s for LLMs (Scaleway test suite via Hugging Face benchmarks, 2024).

These results put Scaleway in the best group of inference backend options. It works better than some big cloud companies that often have unexpected cold-start delays.

Where Performance Matters

🏃 Chat interfaces where users expect instant replies.
🪄 Streaming generation tools that need partial outputs quickly.
🧩 Automation chains in tools like Zapier and Make.com.
🗃️ Batch summarization jobs where throughput is king.

How Does Scaleway Pricing Stack Up?

Scaleway’s pricing, when used through Hugging Face serverless inference, is made to be affordable, clear, and pay-as-you-go. This is perfect for growing startups and creators trying out AI.

Pricing Snapshot

💵 ~$0.0020 per second of model execution time
🧾 No monthly minimums or upfront fees
🎯 Ideal for apps with unpredictable usage

AWS Sagemaker or GCP Vertex AI often make things seem harder (and more costly) with managed endpoints and paying for machines. Scaleway’s flat costs, based on what you use, are easier to predict and grow with.

Serverless inference costs ~$0.0020 per second; Scaleway runs well within that budget.

How to Use Scaleway with Hugging Face

Setting up a model using Scaleway as your Inference Provider on Hugging Face takes just a few minutes:

Login to your Hugging Face account.
Visit the model page (yours or from the Hub).
Click “Deploy” and choose “Inference Endpoints.”
Select “Serverless Inference” under deployment type.
Pick “Scaleway” from the Inference Provider dropdown.
Wait for the provisioning to finish—you're now ready to hit the API!

This process is like setting up on AWS/GCP, but it removes the cloud console and IAM problems. You don't need to link your billing account to many services. Hugging Face handles it all.

When Scaleway Makes Sense for Builders

Scaleway is an especially good choice in these situations:

⚡ Quick MVPs and prototypes using LLMs
🤖 Automation flows via Make.com, Zapier, Bot-Engine
🧩 Customer-facing microservices requiring short burst inference
🚦 GDPR-compliant deployments for EU-based businesses
📈 Traffic that spikes occasionally but doesn’t justify 24/7 infrastructure

If you’re a SaaS founder wanting to add smart features inside your product—or an automation consultant building specific automated tasks—Scaleway takes away problems with both hardware and money.

Tradeoffs and Considerations

Scaleway is strong, but it might not be the best choice in every situation. Before picking it, think about these points:

Downsides

💤 Cold starts are minimized, but might not vanish entirely in ultra-low latency settings (e.g., gaming).
🧪 Not intended for massive training jobs—only inference.
🌐 Limited data center regions compared to AWS/GCP.
🔒 Harder to do complex IAM setups if you're not familiar with Hugging Face deployment flows.

But for apps mainly focused on running models—especially those that don't need GPUs all the time—these downsides are small.

Real-World Uses: Bot-Engine and Beyond

Scaleway, used with Hugging Face, is already running on a range of real-world platforms:

📈 Marketing Automation

Improving CRM entries with custom LLM notes.
Quickly creating blog and ad copy as needed.
Making emails unique for each person using tags about who the reader is.

🤖 Bot-Engine Integrations

Setting up fast chatbots that speak many languages.
Creating models that find out what people mean, using NLU tasks.
Adding smart assistants that understand the situation, based on custom tuning for specific websites.

🧰 Internal Tools

Summarizing technical documentation.
Generating weekly team digests from Slack or Notion.
Sentiment-flagging customer support tickets.

Community Momentum and Market Signals

The developer community has quickly welcomed Scaleway becoming part of Hugging Face Inference Providers.

In 2024, more than 80 models were deployed within 30 days of Scaleway's provider launch inside Hugging Face serverless inference group of services.

It’s not just about cheaper costs. It's about making things easier to think about while growing AI apps that work worldwide. Startups in Europe are very excited. They say it helps with GDPR rules and has less delay compared to US-based clouds.

Quick Comparison: Scaleway vs CoreWeave, AWS, GCP

Provider	Latency	Cost Efficiency	Setup Simplicity	Region Flexibility
Scaleway	⚡ Fast (sub-1s)	✅ Very High	✅✅ Extremely Easy	🌍 Strong in Europe
CoreWeave	⚡ Fast	🟡 Moderate	🟡 Medium	🇺🇸 Primarily U.S.
AWS	🟠 Medium	🔻 Low	🔻 Complex	🌎 Global (but complex)
GCP	⚡ Fast	🟡 Moderate	🟡 Moderate	🌎 Global

Is Scaleway Worth Considering?

Yes, absolutely. Scaleway is just right for serverless inference: it’s fast, affordable, and easy to use. If you're an automation professional, product team, or indie hacker, it makes the way from model idea to working product easier.

With its Hugging Face partnership, Scaleway opens up modern AI-to-API processes. You don't need to manage any servers. This makes it a good choice for creators at every stage.

Ready to try it? Go to Hugging Face and set up your next model on Scaleway Inference today.

Citations

Hugging Face. (2024). Scaleway joins Hugging Face Inference Providers. Retrieved from https://huggingface.co
Scaleway. (2024). Performance benchmarks and pricing data. Internal test environment stats (as referenced in the Hugging Face blog post).
Statista. (2023). AI infrastructure costs per hour by cloud provider. Retrieved from https://statista.com