LLMOps: take your LLMs to production — reliably and cost-efficiently
IASWITCH industrializes LLMs in production: Kubernetes deployment (vLLM), RAG, fine-tuning, continuous evaluation and cost/latency control — with GPU cost reductions of up to 40%.
In brief
- Deploying and scaling LLMs (open-source or API) on Kubernetes
- Secure RAG pipelines with continuous evaluation and guardrails
- Dedicated observability: cost per request, p95 latency, hallucination rate
- LLM FinOps: quantization, multi-model routing, GPU autoscaling
What is LLMOps?
LLMOps (Large Language Model Operations) is the set of practices for deploying, versioning, monitoring and evolving large language models in production. It extends MLOps to LLM-specific concerns: prompts, RAG, fine-tuning, qualitative evaluation and inference cost.
IASWITCH covers the full lifecycle: from model selection to production operations, including guardrails and observability.
How we deploy an LLM to production
We serve models with vLLM on Kubernetes, with per-request autoscaling and multi-model routing to optimize GPU usage. RAG pipelines are secured (data isolation, source traceability) and continuously evaluated against a business test set.
Every deployment is wrapped in guardrails (filtering, output validation) and explicit SLOs on latency and cost.
What you get
A reproducible inference platform in Infrastructure as Code, observable (cost, latency, quality) and handed over to your teams. The goal: LLMs in production that are under control on both reliability and budget.
Frequently asked questions
How do you deploy an LLM to production on Kubernetes?
IASWITCH deploys LLMs via vLLM on Kubernetes with per-request GPU autoscaling, multi-model routing, guardrails and observability (cost, latency, quality), all provisioned as Infrastructure as Code.
What is the difference between LLMOps and MLOps?
MLOps industrializes classic ML models (training, registry, deployment, drift). LLMOps adds LLM-specific concerns: prompts, RAG, fine-tuning, qualitative evaluation and inference cost control.
How do you reduce LLM inference costs?
Through quantization, multi-model routing (small model by default, large model when needed), per-request GPU autoscaling and caching. This cut GPU costs by up to 40% on an inference platform we built.
Got an AI project to ship to production?
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