Automate releases
Structure the build, test and deployment pipelines, with validation rules.
Our service
DevOps & Cloud Management: Controlled Deployments
DevOps & Cloud Management makes your releases reproducible and your incidents easier to diagnose. We work on environments, automation and operations, for your applications as well as for the AI services you put into production.
For
Teams held back by manual deployments, drifting environments or hard-to-diagnose incidents
What you get
Pipelines and configurations, monitoring and runbooks, including for your AI services
Technologies
AWS, Azure, GCP; GitHub Actions, GitLab CI, Terraform, Docker, Kubernetes, Prometheus, Grafana
Getting started
An audit of your existing setup, without rebuilding everything, then stabilization and improvement
What we build
A scope tied to your business.
Manual deployments, diverging environments or hard-to-diagnose incidents slow down the life of the product. We bring development and operations closer together to make these operations reproducible. The scope may cover an existing service or the preparation of a new application for production.
Organize the cloud
Standardize environments, access, secrets and infrastructure configuration.
Observe & improve
Connect logs, metrics and alerts to operational needs, then track costs and incidents.
For which needs
Why move to DevOps & Cloud?
Release to production more often, with less friction
Automated, verified releases replace manual and risky production releases.
Reduce incidents
Explicit reliability objectives, useful monitoring and prepared rollbacks.
Standardize environments
Development, staging and production built the same way, with no hidden differences.
Move to infrastructure as code
Infrastructure that is described, versioned and rebuildable, rather than configured by hand.
Secure pipelines and access
Identity, secrets and policy management applied automatically.
Control cloud costs
Visibility per service, budgets, alerts and sizing adjusted to actual usage.
Our DevOps & Cloud expertise
An end-to-end DevOps chain, designed for production.
We design, implement and run your DevOps practices on AWS, Azure or GCP, starting from your current practices and your level of industrialization.
DevOps & Cloud audit
Maturity, risks and quick fixes, often as part of a broader technical audit.
Continuous integration and deployment
Automated builds, tests and deployments, with a release and rollback strategy.
Infrastructure as code
Reproducible, versioned and consistent environments.
Containers & orchestration
Docker, and Kubernetes when the context justifies it.
Cloud security
Identities and permissions, secrets, hardening and provider best practices.
Observability
Monitoring, alerts, centralized logs and traces.
Cost governance
Resource tagging, budgets, alerts and optimization.
Operations
Operational maintenance, runbooks, incident analysis and continuous improvement.
Technologies
The DevOps & Cloud tools we use.
Tools are chosen to secure operations and automate what can be automated, without unnecessary complexity.
Cloud
AWS, Azure and GCP, depending on your information system and your constraints. See our AWS and Kubernetes expertise.
CI/CD
GitHub Actions, GitLab CI and Jenkins.
Infrastructure as code & containers
Terraform, CloudFormation, Bicep, Docker and Kubernetes.
Observability & security
Prometheus and Grafana, OpenTelemetry and ELK, identity and secrets management, security scans built into pipelines.
DevOps and AI
Deploying and running an AI service in production.
An assistant or an AI feature is still a service that has to be run. It requires managing model access keys as secrets, logging interactions while respecting data protection, tracking the provider’s response times and errors, rerunning evaluation datasets whenever the model or the prompt changes, and monitoring the cost of API calls.
We build these controls into the existing deployment pipeline and monitoring, which is often the missing step when taking over an AI POC. See our AI solutions for businesses.
From work to deliverables
How the engagement unfolds.
We describe the environments, secrets, permissions and release steps. Pipelines run the checks and produce identifiable artifacts. Monitoring tracks the signals that matter for operations. Backups, restores and rollbacks are reviewed with the people who will be responsible for the service.
Understand
Mapping of the architecture, constraints and priorities.
Build
Clean, reproducible environments and a clear deployment pipeline.
Secure
Access and permissions, secrets management, policies, compliance and change management.
Operate
Dashboards, useful alerts, runbooks and post-incident reviews.
Optimize
Sizing, autoscaling, choice of services and waste reduction.
What your team receives
- Pipelines and configurations
- monitoring
- runbooks
The choices that matter
The points to decide with your team.
The choice of a cloud or an orchestration platform must match the workload and the operational capacity. We examine resource costs and vendor dependencies. Support, hours and response times are defined separately from the technical tools put in place.
Operations can be entrusted to a dedicated team that develops and runs the product, which keeps knowledge of the code and the infrastructure in the same place.
The collaboration framework
Preparing the first conversation.
You can present how things currently work, the users involved and the difficulties you face. The documents, examples and access required are specified afterwards, depending on the chosen scope. The first objective is to understand the work to be done and the dependencies that may affect how it unfolds.
Frequently asked questions
DevOps & Cloud Management: your questions.
Can you work on an existing infrastructure without rebuilding everything?
Yes. The engagement starts with an audit of the existing setup to identify weaknesses, gaps with best practices and possible improvements. Actions then focus on stabilization, standardization and continuous improvement, according to your priorities.
When does Kubernetes make sense?
For distributed applications that require strong scalability, high availability or frequent deployments. It is not always necessary for a simple or low-volume architecture, where it mainly adds operational complexity.
How is security handled in a DevOps & Cloud environment?
From the design stage, through identity and access management, pipeline security, environment segmentation, secrets management and continuous monitoring. The goal is to reduce risk without giving up automation.
How do you reduce cloud infrastructure costs over the long term?
By analyzing actual resource usage, adjusting architectures, automating scaling and shutting down unused resources. Clear governance and tracking metrics then prevent costs from drifting.
When should you outsource DevOps and Cloud Management?
When infrastructure issues become critical or the in-house team lacks time or specialized skills. You focus on the product while keeping visibility and control over operations.
Does an AI service in production need a special environment?
Not a specific platform, but additional controls. Management of model access keys, privacy-conscious logging, tracking of costs and response times, and running evaluation datasets in the deployment pipeline.
Do you handle day-to-day operations?
Yes, if you wish. Monitoring, fixes, updates and continuous improvement, with a support level, hours and response times defined together.
Let’s talk about your production.
Tell us about your architecture, your current deployments and the difficulties you face. Together we will define the first scope to study.