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Last November, at ai-PULSE, we told you where AI was heading: big, efficient, and open. Fast forward to last week’s AI Action Summit, and we saw it all play out in real time.
 
## Big: Scaling Up for the AI Era

AI needs infrastructure. That’s not news to us at Scaleway, but it’s now a message backed by serious commitments. French President Emmanuel Macron announced a €109 billion investment to reinforce Europe’s AI capabilities.

We’re doing our part. The iliad Group announced a massive [€3 billion investment](https://www.iliad.fr/en/actualites/article/the-iliad-group-is-investing-3-billion-in-ai) to build out the future of AI infrastructure. Our goal is to make sure Europe has the compute power to compete at the highest level. Because you need to reach a critical size to succeed in AI.

![AI Action Summit Business Day (France Digitale – David AROUS)](https://www-uploads.scaleway.com/ai_action_summit_at_station_f_720_10bb3aaa3c.jpg)

Scaleway was the first European cloud provider to invest in [GPU clusters](https://www.scaleway.com/en/custom-built-clusters/) back in 2023. That early move paid off: we still have one of the biggest pools in Europe and some of the best models today were trained on our infrastructure. But being first isn’t always easy. Staying ahead means constant innovation, and we’re working hard to ensure our compute, network, and storage remain fast, reliable, and built for the future.

We’re not stopping here. Our 2025 roadmap is ambitious: we want to make access to large-scale compute easier than ever. AI’s future depends on [solid infrastructure](https://www.scaleway.com/en/cluster-on-demand/), and we’re building it.

## Efficient: Doing More with Less

Even before the AI Action Summit, DeepSeek stole the show by proving that AI can be smarter, not just bigger. And at the summit itself, Kyutai showed why efficiency matters. Their [Hibiki model](https://kyutai.org/2025/02/10/hibiki.html) is a voice-to-voice AI translation system that can run directly on a smartphone. It is a perfect example of what happens when a team of smart people focuses on efficiency.

Indeed, raw power isn’t everything. If we were in Asimov’s Foundation series, Europe wouldn’t be the Empire flexing its might. We’d be the Foundation, outthinking the competition. In complex systems, smart always beats big.

And let’s be real: Europe isn’t ignoring the climate crisis. Growth needs to be smart. France has one of the cleanest energy mixes in the world, thanks to nuclear power. That means AI models trained on French infrastructure have a lower carbon footprint than most alternatives.

![Scaleway Environmental Footprint Calculator](https://www-uploads.scaleway.com/environmental_footprint_calculator_scaleway_console_1_d4dc232f39.png)

At Scaleway, we don’t just talk about efficiency. We track it. That’s why we launched the first version of our [Environmental Footprint Calculator](https://www.scaleway.com/en/environmental-footprint-calculator/). In 2025, we’ll keep expanding it, giving users clearer insights into the impact of the technology they use. Knowing your footprint matters. It helps you make smarter choices.

## Open: AI in an Uncertain World

The AI Action Summit also made one thing clear: we live in unpredictable times. Yesterday’s allies might not be tomorrow’s. That’s why openness matters more than ever.

Open source is our best safeguard against uncertainty. It lets you control your future. And it also keeps innovation accessible to all. Because open source is about giving back. India’s role as co-chair of the summit reminds us that AI shouldn’t just serve a select few. It should benefit everyone, everywhere.

For Scaleway, open source is the best way to keep our clients in control, not locked in. We believe in giving choices and alternatives. That’s why, when we built our Generative APIs, we set ourselves a strict rule: make it compatible with OpenAI’s API. The result? Anyone can switch to the [open-weight models](https://www.scaleway.com/en/generative-apis/) we provide by changing a single line of code.

![Scaleway Model-as-a-Service](https://www-uploads.scaleway.com/model_as_a_service_scaleway_console_1_217c3b481b.png)

Open source isn’t going anywhere. Thanks to the battle for talent, state-of-the-art open-weight models will keep emerging in 2025. The best contributors don’t want to build behind closed doors. They want to share their work with the world. They want to be on the right side of history, as [Sam Altman](https://www.vice.com/en/article/openai-ceo-sam-altman-says-theyve-been-on-the-wrong-side-of-history/) would say.

At Scaleway, we’re all in. We’ll keep deploying the latest and greatest open-weight models, ensuring that you stay in control.

## 2025: The AI Playbook Remains the Same 

Big. Efficient. Open. If the AI Action Summit confirmed anything, it’s that these three principles will continue to define AI in 2025.

Want to see what’s next? Mark your calendar for November 18th, because [ai-PULSE](https://www.ai-pulse.eu) will be back. And we’re just getting started.


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slack-imgs.jpg

Big, Efficient, Open: The AI Future We Saw Coming

The ever-changing landscape of data management has given rise to a new era of database technology. Document databases in particular were designed to better handle the vast amounts of semi-structured and unstructured data generated by modern products and applications and to keep pace with the growing volume and variety of data that demands flexible, scalable, and fast processing.

In this article, we’ll dive into what document databases are, how they work, and why they’ve become a preferred solution for managing complex data, offering flexibility that traditional databases struggle to provide.

## A brief history of databases: from relational to document databases

Relational databases, the backbone of data storage since the 1970s, were designed for structured data. Built on a fixed schema, they efficiently organize data into rows and columns, enabling easy querying and analysis. However, as the types of data businesses generate have evolved—think images, videos, and IoT data—so too have the demands placed on databases. 

The rise of NoSQL databases in the early 2000s offered a solution to these new demands by providing flexible, schema-less architectures capable of storing vast amounts of unstructured data. Among these NoSQL options, document databases have emerged as a versatile tool that can adapt to the complexity and scale of modern data workloads.

### Back to basics: what is a document database?

A document database is a type of NoSQL database that stores data in document-like structures, most commonly using JSON or BSON formats. Each document represents a record, and within these documents, data is organized as key-value pairs, with the ability to nest arrays and objects. 

The beauty of document databases lies in their flexibility. Unlike relational databases, which require data to fit into predefined schemas, document databases allow data to be stored in its original, often messy form. This makes them ideal for managing unstructured or semi-structured data **without requiring significant reformatting or processing**.

## Structured vs. unstructured data

Data generally falls into two categories: structured and unstructured.

- **Structured data** fits neatly into predefined formats, such as spreadsheets or relational databases
- **Unstructured data**, on the other hand, doesn’t conform to a specific model. Examples include images, audio files, videos, and social media posts—types of data that relational databases struggle to handle efficiently.

Document databases bridge this gap by offering a flexible schema. This means you can store various types of data—whether structured, semi-structured, or unstructured—in their natural form, without having to alter or standardize them.

## How document databases work

In a document database, each document is self-contained, meaning the structure of one document can differ significantly from the next. These databases are highly adaptable because they allow changes to be made on the fly, without the need for complex schema migrations.

- **Flexible schema**: new fields can be added to documents at any time, providing agility in development and reducing the operational burden.
- **Variety of formats**: multiple data formats can be stored **within the same collection**, enabling you to manage diverse data types together.

This architecture makes document databases** highly scalable** and** easy to maintain**, especially in applications where data is continuously changing or evolving.

## Benefits of document databases

**Reduced operational overhead**
Traditional relational databases often require extensive data transformation to fit structured schemas, which can be labor-intensive and time-consuming. Document databases eliminate much of this overhead by allowing data to be stored in its original format, cutting down on reformatting efforts and freeing up resources to focus on more valuable tasks.

**Improved agility**
Document databases’ flexible schema design enables rapid iterations and updates without the need for complex schema alterations. This allows teams to ship new features faster and adjust data models as business needs evolve, fostering greater agility in product development.

**Performance**
When it comes to performance, document databases have a distinct advantage in handling hierarchical data. By storing all relevant information within a single document, these databases are able to retrieve and manipulate data with greater efficiency. In contrast, relational databases often require the use of joins to gather related data, resulting in increased read and write latency. This fundamental difference in architecture enables document databases to deliver faster and more responsive performance, making them an attractive choice for applications that rely on complex, hierarchical data structures.

## Use cases: when to choose a document database

Document databases are best suited in scenarios where data is **unpredictable, unstructured**, or **subject to rapid change**. Here are some common use cases where document databases are the better option:

**Internet of Things (IoT)**
IoT devices produce a continuous stream of data, often in different formats. Document databases can store this data as-is, enabling real-time processing and analysis without the need for data standardization.


**Content Management Systems**
Data is often semi-structured and constantly evolving. By storing content, metadata, and related information in a single document, CMS platforms can efficiently manage and retrieve complex data sets, such as articles, blogs, and user profiles. This flexible data model enables developers to adapt to changing content requirements, while also providing fast and scalable performance, making it an ideal choice for large-scale content management applications.

**E-commerce product catalogs**
Document databases allow for efficient storage and retrieval of complex product information, including descriptions, pricing, inventory, and customer reviews. By storing all product data in a single document, e-commerce platforms can quickly retrieve and update product information, reducing latency and improving the overall shopping experience. Additionally, document databases can handle large volumes of product data, making them an ideal choice for large-scale e-commerce applications with extensive product catalogs.

**Mobile and web applications**
They often require flexible data models to accommodate changing user behavior, new features, and evolving business requirements. Document databases are well-suited for these applications, allowing developers to store and manage complex, semi-structured data in a flexible and adaptable way. By using a document database, developers can quickly iterate and refine their data models, adding new fields, documents, or collections as needed, without the need for costly and time-consuming schema changes, making it an ideal choice for agile development teams and fast-paced application development environments.

## Relational vs. non-relational: how to choose the right database for your needs

When evaluating database options, the primary consideration is often the specific needs of the application or use case. For applications where data consistency and integrity are paramount, and complex querying and reporting are essential, relational databases are the preferred choice. Their robust support for transactions, constraints, and joins ensures that data remains accurate and reliable, making them well-suited for applications that require strict data governance and compliance.

On the other hand, document databases are the ideal choice for applications that require flexibility, speed, and the ability to handle unstructured or semi-structured data. Their flexible schema and high-performance data retrieval capabilities make them perfect for building lightning-fast applications that require rapid data ingestion and processing. Additionally, document databases can efficiently handle large volumes of unstructured data, such as text, images, and videos, making them a popular choice for big data and real-time analytics applications.

**Document databases for the modern data landscape**

Document databases offer a powerful, flexible solution for managing today’s data complexities. By allowing you to store unstructured and semi-structured data in its natural form, they eliminate the need for time-consuming data reformatting, reduce operational overhead, and increase agility in development.

If your organization is grappling with the challenges of handling growing data volumes, adopting a document database could be the key to unlocking more efficient data management and faster innovation. 

Learn more about our [Managed MongoDB®](https://www.scaleway.com/en/managed-mongodb/) database to discover how it can help streamline your data processes, cut costs, and accelerate your business growth.

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