ai-PULSE 2024 white paper

Affordable servers with the best price-performance ratio on the market

Start

Perfect balance of processing power, memory and storage

The high performance backbone of your mission-critical infrastructure

Core

For mission-critical data, fast storage, backup and streaming

Store

Dedicated GPU power with reliable performance and stability

Host your Hardware in our secured French datacenters

Dedirack

60 locations worldwide, starting at €4,99/month

Dedibox VPS

Dedibox - dedicated servers

Fully dedicated bare metal servers with native cloud integration, at the best price

Aluminium

Powerful, balanced and reliable servers for production-grade applications

Beryllium

Powerful dedicated server designed to handle high-workload applications

Iridium

Designed with huge local storage to keep, back up, and protect your data

Lithium

Power and stability of dedicated GPU hardware integrated into the Scaleway ecosystem

Titanium

Elastic Metal - bare metal cloud

Enjoy the Mac mini experience with great simplicity

Mac mini M1

Perform your daily tasks with speed and efficiency

Mac mini M2

Realize your most ambitious projects thanks to a new level of power

Mac mini M2 Pro

Apple

Dedibox and Bare Metal

Dedicated vCPU for the most demanding workloads (x86)

Production-Optimized

Secure, scalable VMs, equipped for high memory and compute demands (x86)

Workload-Optimized

Highly reliable and priced affordably Instances with shared vCPUs (x86 and ARM)

Cost-Optimized

A tiny instance to test and host your personal projects (x86)

Learning

Virtual Instances

Maximize your AI infrastructures with a versatile Instance

L4 GPU Instance

Universal Instance, faster than L4 and cheaper than H100 PCIe

L40S GPU Instance

Accelerate your model training with the most high-end AI chip

H100 PCIe GPU Instance

Dedicated NVIDIA® RTX 3070 with the best price/performance ratio

GPU 3070 Instances

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Render GPU Instances

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Serverless Jobs

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Build the next Foundation Model with one of the fastest and most energy-efficient supercomputers in the world

Custom-built Clusters

Rent a GPU-cluster from 32 to more than a thousand GPUs to speed up distributed training

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Clusters

Deploy AI models in a dedicated inference infrastructure. Get tailored security and predictable throughput

Managed Inference

Consume AI models instantly via a simple API call. All hosted in Europe

Generative APIs

Model-as-a-service

Accelerate the next generation of AI-enabled applications with the universal L40S GPU Instance, faster than L4 and cheaper than H100 PCIe

Maximize your AI infrastructure's potential with a versatile and cost-effective GPU Instance

Dedicated Tesla P100s for all your Machine Learning & Artificial Intelligence needs

Render GPU Instance

GPU Instances

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Object Storage

Cold Storage class to secure long-term object storage. Ideal for deep archived data.

Scaleway Glacier

Flexible and reliable storage for demanding workloads

Block Storage

Storage

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Improve the performance of your services as you grow

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Centralize and simplify your Scaleway IP address management

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Expose your HTTP services to the internet with security, reliability, and efficiency by design.

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Establish a hosted connection from your infrastructure to your VPC via a partner's network

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Go full serverless and take the complexity out of PostgreSQL database

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Build distributed and scalable client-server applications

NATS

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Sent to a variety of devices and platforms through a single code interface

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Hosting for individuals, professionals, and everyone in between.

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Ship your applications only in a few clicks.

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A purpose-built bridge between connected hardware and cloud.

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The easiest way to safely collaborate in the cloud

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Protect your sensitive data across your cloud infrastructure

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Cost Manager

Accurately track your environmental impact and make informed choices

Environmental Footprint Calculator

Security & Organization

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Deploy and manage your infrastructure directly from the command line

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At Scaleway, sustainability is a core concern. This why we decided to create a unique [environmental footprint calculator](https://www.scaleway.com/en/environmental-footprint-calculator/), designed to provide our users with a
users a clear and comprehensive view of the environmental impact of our products.
of our products. Unlike other cloud calculators, which only measure carbon for now, this tool measures not only carbon emissions (scopes 1, 2 and 3) - including hardware-specific energy consumption - but also water consumption. 

Based on as a robust a methodology as the PCR of French national ecological agency [ADEME](https://www.ademe.fr/en/frontpage/), the calculator provides transparent, reliable estimates, both at the time of purchase and in detailed and in detailed monthly reports. The aim is simple: to enable Scaleway clients to make informed decisions and optimize their infrastructures, in order to reduce their environmental impact.

A detailed and comprehensive methodology is essential to guarantee the reliability of environmental impact data and to make it usable in infrastructure choices. By providing an accurate, multi-criteria view, we enable companies to understand the full ecological footprint of their operations.

This data, transparent and aligned with recognized standards, serves as a solid basis for comparing different options, identifying levers for improvement, and making informed decisions. In this way, a rigorous methodology does more than simply measure impact: it becomes a genuine strategic tool for effectively reducing one's footprint while supporting a more responsible digital transition.

Yet this journey has not been without its pitfalls. Accurately measuring environmental footprint, particularly scope 3 (which encompasses the indirect emissions of suppliers and purchased products), is a complex challenge.


## Scope 3 data collection: a major challenge

Scope 3 represents the CO2 emissions of the suppliers and equipment we use. This category is often the most difficult to assess, as it depends on the quality and availability of data supplied by equipment manufacturers and other partners. Nonetheless, it is indispensable. 

As a cloud provider, scope 3 represents over [80% of the carbon footprint of our services](https://www.itu.int/pub/D-STR-DIGITAL.04-2024). To ignore this category would be to miss out on a substantial part of our overall footprint, which makes our commitment to its measurement all the more crucial for comprehensive and relevant environmental impact monitoring.

At Scaleway, we have a wide variety of hardware equipment from multiple suppliers, some of which has **a lifespan in excess of 10 years**. We also practice intensive recycling, reusing components to integrate them into other servers, which extends the lifespan of our hardware. 

However, this approach adds a further layer of complexity to the precise calculation of the environmental impact of our servers, due to the traceability and (lack of) heterogeneity of the equipment used. **Collecting accurate impact data for these machines, particularly the older ones, proved a major challenge**. Indeed, some of our equipment suppliers did not have sufficiently detailed impact data, or such data was not always available for older equipment.

After encountering a number of obstacles on this front, we decided to capitalize on the excellent work carried out by French association [Boavizta](https://boavizta.org/en), which has developed impact models based on emission factors and the average life cycle analysis (LCA) of hundreds of types of hardware, including servers. This enabled us to guarantee more robust estimates, although some uncertainty remains.

It is important to point out that our methodology is still evolving, and we are ready to integrate data from our suppliers as soon as they become available, in order to refine our calculations and guarantee even greater accuracy.

Another major challenge concerned our infrastructure servers used for in-house tools. The lack of comprehensive, automatable data on these servers, such as their specifications (CPU, RAM, etc.), made it difficult to assess their environmental impact linked to manufacturing. This time, the difficulty was linked to our internal organization. To overcome this problem, we implemented a progressive data collection method, automating as far as possible the retrieval of hardware specifications. At the same time, we applied generic impact coefficients based on standard specifications for servers for which data was not available.


## Data center construction: the uncertainty of emission factors

The **environmental impact of data center construction is a particularly sensitive issue**. In the absence of specific data on the construction of each data center, we had to rely on estimates based on generic emission factors provided by ADEME. However, the level of uncertainty surrounding these estimates remains relatively high.

We have chosen to precisely document this uncertainty in our methodology, while incorporating the best available estimates. The use of emission factors per m² provided by ADEME enables us to calculate a carbon footprint in relation to the size of our infrastructures, although we continue to work on improving the accuracy of the data as new information becomes available.


## Non-IT impact: complex methodological issues

**Non-IT impacts** (offices, travel, etc.) represent a more indirect part of our environmental footprint, but are nonetheless crucial to measure. We had to define how and how often to update these data: should we base ourselves on annual averages, monthly data or choose another approach?

We opted for an annual update of non-IT data, based on personnel stats in our [Impact Report](https://www-uploads.scaleway.com/Impact_Report2024_A4_EN_e63efcae20.pdf), in order to offer our users a consistent and comparable overview. This decision enabled us to erase seasonality concerns, and to maintain a good balance between accuracy and practicality in the ongoing monitoring of our global footprint.


## A constantly evolving process

Measuring environmental impact accurately and comprehensively is a complex challenge, particularly when it comes to scope 3 emissions. However, thanks to our commitment to transparency and the continuous improvement of our methods, we are able to provide our users with reliable information to help them reduce their carbon footprint. Our collaboration with experts and our participation in working groups such as ADEME's PCR group reinforce our ability to innovate in this crucial area.

We are pursuing our optimization mission, and will continue to listen to feedback from our users as we refine our tools and calculation methods.

Find out more about the [environmental footprint calculator here](https://www.scaleway.com/en/environmental-footprint-calculator/); and check out our latest presentation on this topic, from Green IO Paris, [here](https://drive.google.com/file/d/1x45gJGdzx6epPqZTC8ZM1YAFGikWAd2k/view?usp=sharing)!

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Overcoming the challenges of Cloud environmental impact measurement

_The second edition of ai-PULSE, held at Paris's iconic Station F on November 7, kicked off with a powerful morning session that highlighted some of the most important voices in AI today. The morning’s keynote speakers shared their insights into the latest advancements, the growing power of open-source AI, and the urgent need for Europe to position itself as a hub for sovereign, open, and efficient AI solutions._


## A future shaped by open, efficient, and sovereign AI

Europe is not only catching up with AI powerhouses in the US and China, but is emerging as a formidable force in its own right. **Xavier Niel, Founder of Iliad Group,** began his address with a bold statement: "18 months ago, when we said we wanted to create a tech AI conference in Paris, people told us, 'Guys, you're completely crazy, Europe has no AI ecosystem!' Today, we are proving them wrong." He added, "thanks to our talent, we’re building amazing startups like **Mistral, Poolside,** and **Argil.** And to keep that talent, we’ve created **Kyutai**, where we’re assembling the **‘Avengers of AI’,** bringing together the best minds to develop open source AI models in Europe, built according to our values and regulations."

![Xavier Niel](https://www-uploads.scaleway.com/AI_Pulse_0443_2a6832e2ad.jpg)

One of the most exciting innovations to emerge from **Kyutai** is [Moshi](https://moshi.chat/), an open source conversational AI model. "Moshi is the first truly **open-source AI model** created in Europe, and it’s completely free to use. It’s a game-changer for the industry," **Niel** declared. 

**Damien Lucas, CEO of Scaleway** and **Aude Durand, Deputy CEO of iliad Group,** took to the stage next, declaring that **Moshi** is now available as a one-click deployment service through Scaleway's platform.

**Durand** then emphasized the theme of this year’s event: "Big, Efficient, Open." "In AI, we know that top-tier models require immense computational power," she said. **Lucas** added: ”Scaleway is proud to support **cutting-edge AI models** by offering access to **3000 NVIDIA H100 GPUs today** - triple our total this time last year - and in the coming weeks, **we will surpass 5000 GPUs.** This will allow developers across Europe to leverage the power of AI, while minimizing **environmental impact.**"

![Aude Durand and Damien Lucas](https://www-uploads.scaleway.com/Aude_Durand_and_Damien_Lucas_5e4ed468a5.jpg)

As part of its commitment to sustainable AI, **Scaleway** has also launched its new [Environmental Footprint Calculator](https://www.scaleway.com/en/environmental-footprint-calculator/). "Transparency matters," said **Lucas.** "We want to give developers the tools to understand the environmental impact of their AI workloads, covering everything from emissions to water consumption. It’s the most comprehensive calculator available today, and it’s already in beta." Lucas also unveiled [Generative APIs](https://www.scaleway.com/en/generative-apis/), a new Scaleway service which allows developers to move from Open AI to alternative open source models in just one line of code. More about Scaleway's ai-PULSE announcements [here](https://www.scaleway.com/en/news/scaleway-affirms-ais-big-efficient-and-open-future-at-ai-pulse-2024/).

**Michael Dell, Chairman and CEO of Dell Technologies,** then shared his thoughts on the accelerating pace of AI innovation, drawing comparisons with the rise of the internet. "If you remember **the internet in the mid-90s**, people were wondering what it really meant,” he said. “It took about a decade before the full impact was understood. We're in that same phase with AI today." **Dell** then asserted his belief in the transformative power of AI, both in the cloud and on-device: “AI will be used **both on the cloud and on devices.** It will be used at the edge, on the point of activity closest where the action is. It will run on cloud platforms, on your phone, in cars, factories, and retail stores,” he said. 

**Dell** also spoke passionately about open-source AI, emphasizing that it is a critical driver of innovation. "**Open-source AI has already revolutionized the field**, and it’s just getting started," he enthused. "**Last year would’ve been more about closed models; the world has shifted now. Two thirds of activity is now happening in open models**; derivative, small models that don’t necessarily need to run on massive clusters."

 
![Michael Dell, Dell](https://www-uploads.scaleway.com/AI_Pulse_0578_a46729ee39.jpg)

As AI continues to evolve, **Dell** predicted "we’re transitioning **from calculating to thinking.** The future of AI will require new architectures that bring together data, computing, and cognition in a way that helps humans become more successful," he explained.


## The road to power-efficient AI

**Renee J. James, Chairman and CEO of Ampere Computing,** took the stage next to talk about the future of AI hardware. She reflected on the importance of power efficiency in AI infrastructure. “We set out to build something twice as powerful, but with half the energy consumption, and we ended up getting **3 to 4 times the performance,**” said **James.**

![Renee J. James, Ampere Computing](https://www-uploads.scaleway.com/AI_Pulse_0822_07e72ff1c0.jpg)

In a speech echoing her CPO **Jeff Wittich’s** [assertion last year](https://www.scaleway.com/en/blog/why-cpus-also-make-sense-for-ai-inference/) that Ampere CPUs could handle many inference workloads whilst using 3-5 times less energy than comparable GPUs, she stressed that **AI hardware, particularly processors, need to evolve to meet the increasing demands of AI models.** “Today, we are all about training models, we have people building unlimited clusters to train,” she explained. “But as [AI] starts to moderate and become mainstream, **we have to make it affordable and environmentally efficient,** because we don’t have more power. That's the journey we are going to begin.”

**James** also pointed out that **Europe has an untapped wealth of intellectual talent,** especially in computer architecture, and she believes the continent must focus on increasing investment levels to continue pushing the boundaries of AI hardware. “Access to capital is one of the most important things that needs to continue to improve and the ability for investors to take more risks,” she concluded.

![Renen Hallak, VAST Data, and Aude Durand](https://www-uploads.scaleway.com/AI_Pulse_1195_498a6c0c48.jpg)

**Renen Hallak, Founder and CEO of VAST Data,** agreed with James on the growing importance of efficient data management in **AI scaling.** “AI is changing from allowing us to analyze numbers and rows and columns to pictures and videos, natural language and sound. Older systems were not built for that. So we need to break that paradigm, and that requires a **new software architecture,**” said **Hallak.** He then discussed the shift from CPUs to GPUs for data analysis, emphasizing the need for new software architectures “when you build exabyte-scale clusters that need to manage themselves.” **Hallak** also addressed the growing role of AI agents in problem-solving. “**AI agents will soon be communicating with each other, generating ideas, and solving problems that humans cannot solve on their own,”** he said.

**Bryan Catanzaro, VP of Applied Deep Learning Research Research at NVIDIA,** and **Jean-Baptiste Kempf, Scaleway’s Chief Technical Officer,** shared insights on the future of AI infrastructures. Catanzaro discussed the challenges of scaling AI models, particularly when it comes to GPU clusters. “**We’re going to find that instead of putting a million GPUs in a single data center, we're going to run training and inference in a more distributed way, so we can have better access to electricity, and scale in a more efficient way**,” he said, highlighting the importance of optimizing AI for **both performance and sustainability.**

![Bryan Catanzaro, NVIDIA, and Jean-Baptiste Kempf](https://www-uploads.scaleway.com/AI_Pulse_0947_7cb28e1cf6.jpg)

**Kempf echoed** this sentiment, explaining that new AI models will need to be designed with compute efficiency top of mind. “We talked about thousands or hundreds of thousands of GPUs for training, but **90% of the compute power will actually be dedicated to inference.** So, I don't believe we’ve reached the maximum capacity for inference yet,” he said.

**Catanzaro** added, “we change everything at NVIDIA. My job is to figure out how to change our models and make them much more efficient, both for training and deployment. **NVIDIA is not just a chip company; we focus on accelerating computing through full-stack optimization.** The level of specialization that goes into our GPUs for AI is something we decide on every day, focusing most importantly on machine learning and intelligence.”


## Multimodal foundation mode and groundbreaking voices in AI

**Patrick Perez, CEO of Kyutai,** then spoke about the groundbreaking work behind **Moshi**. “Since launching in mid-September, **Moshi has already been experienced by over half a million people**,”said Perez. He explained that Moshi is more than just a conversational AI; it is a **multimodal foundation model** capable of performing various tasks, from speech synthesis to question answering. “What we’ve created with Moshi is not just a voice model, but a platform for developers to innovate on top of,” he added. **The open-source nature of Moshi is a key part of Kyutai’s mission to democratize AI and ensure that European models can compete on the global stage.**

![Patrick Perez, Kyutai](https://www-uploads.scaleway.com/AI_Pulse_1261_f265aaf8ab.jpg)

**Charles Kantor, CEO of H** then addressed the potential of action models in AI, highlighting the importance of **combining LLMs (Large Language Models) and VLMs (Vision Models) for specialized tasks.** "Action models are incredibly powerful today. By orchestrating LLMs that generate plans and workflows and VLMs that understand interfaces like desktop or Android, you can create systems that can take action effectively," said Kantor. He also emphasized the need for **sector-specific focus**: "the goal is to focus on one industry and master it, building a holistic system with well-orchestrated layers."

![Charles Kantor, H](https://www-uploads.scaleway.com/AI_Pulse_1478_966c47ed21.jpg)

## Building an inclusive AI ecosystem

**Clara Chappaz, France’s Secretary of State for AI and Digital Affairs,** concluded the morning's sessions by discussing the importance of building a distinctive AI ecosystem in Europe. "We are at the very beginning of this technology. **It’s crucial to ensure AI is inclusive, frugal, and ethical**," said Chappaz. She praised **France’s strong educational system for producing top AI talent,** and emphasized the role of infrastructure and private companies, like **Scaleway,** in providing the necessary tools for that talent to thrive and launch companies in France. 

![Clara Chappaz](https://www-uploads.scaleway.com/AI_Pulse_1616_f2ee613181.jpg)

With AI’s growing energy demands, she mentioned the need for green energy in powering infrastructures. “**We [in France] have some of the greenest (lowest carbon) energy in the world.** You all know how much these machines consume when it comes to power and we need to think about how we feed these models with energy that is green, how do we continue fostering these technologies without damaging the planet.” 

![Aude Durand, Clara Chappaz & Damien Lucas](https://www-uploads.scaleway.com/AI_Pulse_1713_e3eb7c2284.jpg)

For Chappaz, what's required is a **'third way' for AI;** a new, collaborative, and inclusive approach to AI development, distinct from the competition-driven dynamics seen in the US and China. “The way we want to build this ecosystem and want to use AI is a way that can resonate with a lot of people. We like to call it the third way; one that brings people together," she concluded. 


- _This is an extract from Saleway's forthcoming **ai-PULSE 2024 white paper**, which will wrap up all of the event's key AI insights. Watch this space!_
- _Meanwhile, you can check out replays of all of the above sessions, plus many more besides, on our [YouTube channel](https://www.youtube.com/channel/UC-Le3MdpQ79hWz8_t2_pGEw). Enjoy!_


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Kubernetes has become a game-changing technology for container orchestration, and the vibrant open-source community has been a driving force behind its success. The curated selection of Kubernetes tooling presented here shows the breadth and depth of the ecosystem that has grown around Kubernetes.

From automation and monitoring to networking and security, these tools provide a solid foundation for managing containerized applications. The ongoing development of new open-source projects within the Kubernetes community is a testament to its vitality and innovation.

If you have spend countless hours working with Kubernetes, you understand the unique challenges of managing a complex infrastructure at scale. While Kubernetes is an incredibly powerful platform for deploying and managing containerized applications, it can also transform into a complex mess. So let's discover new tools to orchestrate your infrastructure!

![](https://www-uploads.scaleway.com/Kubernetes_tooling_Scaleway_5cef6c71ac.gif)

## Deployment and configuration management 

### Kustomize
[Kustomize](https://kustomize.io/) is a tool that lets you customize Kubernetes resources by applying patches to base configurations. It provides a simple and declarative way to manage your Kubernetes configurations, allowing you to manage different environments and configurations with ease. 
👉 [Kustomize’s GitHub](https://github.com/kubernetes-sigs/kustomize) 

### Jsonnet
[Jsonnet](https://jsonnet.org) is a configuration language that can be used to generate Kubernetes manifests. It provides a clear way to define Kubernetes resources using a flexible and expressive syntax, which can be used to manage complex and large configurations. 
👉 [Jsonnet's GitHub](https://github.com/google/jsonnet)

### Skaffold
[Skaffold](https://skaffold.dev/) is a command-line tool that streamlines the development workflow for Kubernetes applications. It supports many popular programming languages and development tools, and can be integrated with CI/CD pipelines.
👉 [Skaffold's GitHub](https://github.com/GoogleContainerTools/skaffold)

### Helm
Helm is the well-known package manager for Kubernetes that simplifies the installation and management of complex applications. It provides a templating system that allows you to define your applications as charts, which can be effortlessly installed and configured on your Kubernetes cluster. 
You can also read more on our [blog post about Helm](https://www.scaleway.com/en/blog/helm-guide/).
👉 [Helm's GitHub](https://github.com/helm/helm) 

### Kubeconfig-generator
[Kubeconfig-generator](https://github.com/kubernetes/kubeconfig-generator) is a tool for generating Kubernetes configuration files (kubeconfig) for multiple clusters and users. With Kubeconfig-generator, you can simplify the process of managing multiple clusters and users by generating configuration files automatically. It supports many popular Kubernetes distributions and can be integrated with CI/CD pipelines. 
👉 [Kubeconfig-generator's Github](https://github.com/kubernetes/kubeconfig-generator) 



## Monitoring and observability 

### Prometheus
[Prometheus](https://prometheus.io) is a monitoring tool that collects metrics from Kubernetes resources and stores them in a time-series database—it is a powerful way to monitor the health and performance of your Kubernetes cluster and applications. 
👉 [Prometheus's GitHub](https://github.com/prometheus/prometheus) 

### Thanos
[Thanos](https://thanos.io) is a tool that extends Prometheus by adding long-term storage, global querying, and cross-cluster federation capabilities to store your Kubernetes metrics over a long period of time. 
👉 [Thano's GitHub](https://github.com/thanos-io/thanos) 

### Grafana
[Grafana](https://grafana.com) is a visualization tool that can be used with Prometheus to create dashboards and visualizations of your Kubernetes metrics, making it smoother to understand the health and performance of your applications. 
👉 [Grafana's GitHub](https://github.com/grafana/grafana) 

### Kubevisor
[Kubevisor](https://kubevisor.io) is a lightweight and extensible Kubernetes resource monitor that provides real-time insights into the health and performance infrastructure. You can visualize resource usage and metrics, set alerts and thresholds, and troubleshoot issues quickly. Kubevisor supports many popular Kubernetes distributions and can be integrated with Prometheus and Grafana. 
👉 [Kubevisor's GitHub](https://github.com/kubevisor/kubevisor)


### Sysdig
[Sysdig](https://sysdig.com) unifies visibility, security, and compliance for Kubernetes and other cloud-native technologies. It allows you to monitor your Kubernetes infrastructure and applications in real-time, detect security threats and vulnerabilities, and enforce compliance policies. Sysdig provides a set of features for monitoring and observability, including container visibility, network security, and compliance auditing. 
👉 [Sysdig's GitHub](https://github.com/sysdiglabs) 



## Logging and tracing 

### Elasticsearch
[Elasticsearch](elastic.co) is a search engine that can be used to store and search logs generated by Kubernetes resources. It simplifies the management and search of logs in Kubernetes to troubleshoot issues in your applications. 
👉 [Elasticsearch's GitHub](https://github.com/elastic/elasticsearch)

### Fluentd
[Fluentd](https://www.fluentd.org) is a data collection tool that can collect logs and send them to Elasticsearch or other destinations. 
👉 [Fluentd's GitHub](https://github.com/fluent/fluentd) 


### Jaeger
[Jaeger](https://www.jaegertracing.io) is a distributed tracing system that can be used to monitor the performance of your Kubernetes applications by tracing requests through your Kubernetes cluster, making it easy to identify performance bottlenecks and troubleshoot issues. 
👉 [Jaeger's GitHub](https://github.com/jaegertracing/jaeger) 

### OpenTelemetry
[OpenTelemetry](https://opentelemetry.io) provides a vendor-neutral and language-agnostic way to collect, process, and export telemetry data to collect logs, traces, and metrics from your  applications and infrastructure, and export them to popular observability platforms such as Prometheus and Grafana. 👉 [OpenTelemetry's GitHub](https://github.com/open-telemetry) 

### Fluent Bit
[Fluent Bit](https://fluentbit.io) is a lightweight and high-performance log and event collector that supports many popular logging systems and formats. With Fluent Bit, you can collect and process logs from your Kubernetes applications and infrastructure, and export them to popular logging platforms such as Elasticsearch and Splunk. 
👉 [Fluent Bit's GitHub](https://github.com/fluent/fluent-bit) 


## Networking and service discovery 

### CoreDNS
[CoreDNS](https://coredns.io) is a DNS server that can be used to provide service discovery within your Kubernetes cluster to manage DNS records for your Kubernetes resources, making it easy to discover and communicate with services within your cluster. 
👉 [CoreDNS's GitHub](https://github.com/coredns/coredns)

### Traefik
[Traefik](https://traefik.io) is a reverse proxy and load balancer that can be used to manage network traffic between Kubernetes resources to manage network traffic and enforce security policies to secure and optimize communication between your Kubernetes resources. 
👉 [Traefik's GitHub](https://github.com/traefik/traefik)

### Istio
[Istio](https://istio.io) is a service mesh that can be used to manage and secure communication between services within your Kubernetes cluster to manage traffic routing, load balancing, and security policies. 
👉 [Istio's GitHub](https://github.com/istio/istio)

### Contour
[Contour](https://projectcontour.io) is a Kubernetes ingress controller that provides a high-performance and scalable way to manage incoming traffic. Contour supports many popular features, such as TLS termination, load balancing, and path-based routing. 
👉 [Contour's GitHub](https://github.com/projectcontour/contour)

## Security 

### Kubernetes Secrets
[Kubernetes Secrets](https://kubernetes.io/docs/concepts/configuration/secret) is a tool that can be used to store and manage sensitive information like passwords and API keys. It provides a simple and secure way to manage secrets within your Kubernetes cluster to protect sensitive information. 
👉 [Kubernetes Secrets's GitHub](https://github.com/kubernetes/kubernetes) 

### RBAC (Role-Based Access Control)
[RBAC](https://kubernetes.io/docs/reference/access-authn-authz/rbac) is a security mechanism that can be used to control access to Kubernetes resources based on roles and permissions to manage access control within your Kubernetes cluster, making it easy to enforce security policies. 
👉 [RBAC's GitHub](https://github.com/kubernetes/kubernetes) 

### Open Policy Agent (OPA)
[OPA](https://www.openpolicyagent.org) is a policy engine that can be used to enforce security policies and access control within your Kubernetes cluster to define policies, which can be used to enforce security and compliance requirements. 
👉 [OPA's GitHub](https://github.com/open-policy-agent/opa) 

### Kyverno
[Kyverno](https://kyverno.io) is a policy engine designed for Kubernetes to ensure compliance and security for your Kubernetes infrastructure and applications. Kyverno is easy to deploy and configure and provides a powerful set of features for policy enforcement. 
👉 [Kyverno's GitHub](https://github.com/kyverno/kyverno)

### Gatekeeper
Gatekeeper is a policy engine designed for Kubernetes. It provides a way to enforce policies and rules for Kubernetes resources using the Open Policy Agent (OPA) engine. With Gatekeeper, you can ensure compliance and security for your Kubernetes infrastructure and applications. 
👉 [Gatekeeper's GitHub](https://github.com/open-policy-agent/gatekeeper)


## Continuous integration/Continuous deployment 

### Dagger
[Dagger](https://dagger.io/) is a Kubernetes-native deployment tool that simplifies the deployment and management of applications on Kubernetes. With Dagger, you can define and deploy complex applications in minutes, without the need for complex YAML files or custom scripts. It provides a simple and intuitive user interface for managing deployments, with real-time monitoring and logging. 
👉 [Dagger's GitHub](https://github.com/dagger/dagger)


### Jenkins
[Jenkins](https://www.jenkins.io) is a popular CI/CD tool that can be used to automate the process of building, testing, and deploying your applications to Kubernetes. It provides a simple and flexible way to define pipelines, which can be used to automate the entire software delivery process. 
👉 [Jenkins's GitHub](https://github.com/jenkinsci/jenkins) 

### GitLab
[GitLab](https://about.gitlab.com) is a platform that provides CI/CD pipelines for building, testing, and deploying your applications to Kubernetes. It provides a simple and powerful way to manage your source code and automate the software delivery process.
👉 [GitLab's GitHub](https://github.com/gitlabhq/gitlabhq) 

### Argo CD
[Argo CD](https://argoproj.github.io/cd/) is a tool that can be used to automate the deployment of your applications to Kubernetes by using GitOps principles. It provides a simple and powerful way to manage the deployment of your applications, making it easy to ensure consistency and reliability. 
👉 [Argo CD's GitHub](https://github.com/argoproj/argo-cd) 


### Tekton
[Tekton](https://tekton.dev) is an open source framework for building CI/CD pipelines on Kubernetes to define and execute pipelines using Kubernetes resources. Tekton is gaining popularity as more organizations adopt Kubernetes for CI/CD. 
👉 [Tekton's GitHub](https://github.com/tektoncd/pipeline)


## Infrastructure-as-Code (IaC)

### Terraform
[Terraform](https://www.terraform.io) is a tool that can be used to define and manage your Kubernetes infrastructure as code, making it easy to provision and manage your Kubernetes cluster and associated resources.
Learn how to deploy your Terraform module in a few click with our [guided hands-on here](https://www.scaleway.com/en/blog/terraform-module-deploy-your-infrastructure-in-one-click/).
👉 [Terraform's GitHub](https://github.com/hashicorp/terraform) 

### Ansible
[Ansible](https://www.ansible.com) is a tool that can be used to automate the provisioning and configuration of your Kubernetes resources. It provides a simple and powerful way to manage your infrastructure, making deploying and managing your Kubernetes resources easy. 
👉 [Ansible's GitHub](https://github.com/ansible/ansible) 

### Pulumi
[Pulumi](https://www.pulumi.com) is a tool that can be used to create and manage your Kubernetes resources using familiar programming languages like Python and JavaScript. 
👉 [Pulumi's GitHub](https://github.com/pulumi/pulumi)

### Crossplane
[Crossplane](https://crossplane.io) is an open source platform for managing cloud-native infrastructure that helps to manage infrastructure across multiple clouds. Crossplane is gaining popularity as more organizations [adopt multi-cloud strategies](https://www.scaleway.com/en/blog/k8s-multi-cloud/). 
👉 [Crossplane's GitHub](https://github.com/crossplaneio/crossplane) 


## Final thoughts

The momentum Kubernetes has right now is driving so many positive things. Every vendor is taking a Kubernetes-first approach to building their solutions and tools. For example, modern applications are being built and packaged to run on Kubernetes, and most innovations in areas like security and development are being built around Kubernetes. As well as this, every major cloud offers its own Kubernetes services. The ecosystem is huge, it’s still growing, and it’s very welcoming. That last bit is helping lower the entry barrier and allowing more people to enter.

As Kubernetes continues to evolve, we can expect even more exciting developments and advancements to emerge. It's an exciting time to be part of the Kubernetes community, and we look forward to seeing the new tools and solutions that will be developed in the future.










Scaleway Blog

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