Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training LiteLLM - Orchestrating Multi-Provider LLMs in Production training are: Mastery of Python, use of LLM APIs like OpenAI, and Docker basics. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training LiteLLM - Orchestrating Multi-Provider LLMs in Production training is available in in-person. The format can be adapted to your needs (inter, intra, custom).

Training LiteLLM - Orchestrating Multi-Provider ... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training LiteLLM - Orchestrating Multi-Provider LLMs in Production training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training LiteLLM - Orchestrating Multi-Provider LLMs in Production training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training LiteLLM - Orchestrating Multi-Provider LLMs in Production training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master LiteLLM to unify professional multi-provider LLM calls

Develop skills in intelligent routing and certified load balancing

Implement secure LiteLLM deployments in enterprise environments

Optimize costs and performance of LLM pipelines with LiteLLM

Design resilient architectures for scalable AI applications

Integrate LiteLLM into DevOps workflows for increased productivity

Deploy OpenAI-compatible LLM proxies for certified projects

Training program

Module 1LiteLLM Fundamentals: Unified Proxy Configuration (OpenAI, Anthropic, Azure)

Quick installation of LiteLLM via pip and Docker, YAML file configuration to connect multiple LLM providers like OpenAI, Grok, and Mistral, first unified call tests with complex prompts, practical exercises on OpenAI API compatibility, creation of a functional local proxy with detailed logs, resolution of real enterprise cases to validate basics in a continuous and performant flow.

Module 2Advanced LiteLLM: Intelligent Routing and Load Balancing (fallbacks, budgets)

Implementation of dynamic routing rules based on costs and latency, automatic fallbacks between providers for high availability, configuration of virtual budgets and token limits per model, practical workshops on traffic overload scenarios, optimization of multi-model prompt chains with LiteLLM, production of real-time metrics and live adjustments for optimal enterprise performance.

Module 3LiteLLM Security and Integrations: Auth, Observability (Prometheus, Langfuse)

Securing the LiteLLM proxy with JWT, rotating API keys, and rate limiting, integration with observability tools like Prometheus and Grafana to monitor LLM costs, connection to vector databases via LiteLLM for advanced RAG, exercises on security audits and GDPR compliance, development of a protected custom API, simulated intrusion tests, and complete documentation of setups for immediate production adoption.

Module 4Production Deployment of LiteLLM: Kubernetes Scaling and CI/CD (Helm, GitHub Actions)

Scalable deployment of LiteLLM on Kubernetes with custom Helm charts, CI/CD automation via GitHub Actions for zero-downtime updates, advanced monitoring with cost and error alerts, final performance optimization for 1000+ req/s, completion of a complete multi-cloud orchestration red thread project, code review by the trainer, delivery of enterprise-ready configurations, and certified maintenance plan.