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Read ArticleDeploy and scale Large Language Models reliably and responsibly with Xebia’s enterprise-grade LLMOps solutions.
Ensure reliable, scalable, and compliant deployment of LLMs with Xebia’s robust LLMOps platform and expert services.
LLMOps is the critical discipline that brings reliability, governance, and scalability to Large Language Models (LLMs) in enterprise environments. With organizations increasingly adopting LLMs to power chatbots, search, document processing, and code generation, a robust operational framework is essential. Xebia’s LLMOps solution enables teams to deploy, monitor, govern, and continuously improve LLMs—securely and efficiently. We help clients manage multiple LLM use cases, ensure responsible AI use, and streamline collaboration between data science, engineering, and compliance teams. From infrastructure to monitoring, and from prompt management to responsible rollout, Xebia ensures that your LLM initiatives are production-ready, auditable, and scalable.
Evaluate your current AI infrastructure, security posture, and readiness to deploy and scale LLMs.
1
Design and implement a robust LLMOps platform to enable collaboration, reuse, and compliance.
2
Rapidly launch use cases with managed access, logging, prompt/version control, and data safeguards.
3
Track model performance, detect drift, and monitor usage in real time to ensure business value and safety.
4
Implement policies for responsible AI use—covering fairness, explainability, data privacy, and auditing.
5
Continuously improve prompts, workflows, and integrations through feedback loops and model iteration.
6
Manage inference costs through smart routing, usage analytics, and model selection strategies.
+50%
Accelerate team collaboration with shared prompt libraries, audit trails, and consistent evaluation practices.
x10
Detect anomalies, usage spikes, or performance drops with integrated observability tools.
+30%
Implement access restrictions, usage logging, and responsible AI compliance out-of-the-box.
High
Enable fast rollout of multiple use cases with centralized infrastructure and reusable components.
Scale
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