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Services/AI Infrastructure & LLMOps

AI Infrastructure & LLMOps: Production-Ready AI Systems

We set up the backbone for reliable AI systems — deployment pipelines, monitoring, evals, scaling, and cost control — so your models run smoothly in production.

AWSGCPKubernetesEKSTerraformMLflowMonitoring

Running an AI demo is straightforward. Running an AI system in production — reliably, at scale, under cost pressure, with observable failure modes — is an engineering discipline that most teams underestimate. LLMOps is the difference between a promising prototype and a system your business can actually depend on.

We build and operate the infrastructure layer that keeps AI systems running: model serving pipelines, monitoring, evaluation frameworks, CI/CD for models, and cost controls.

What We Build

Model Serving Pipelines

Production-grade serving infrastructure for LLMs and custom models — on AWS SageMaker, GCP Vertex AI, or self-hosted using vLLM or TGI on Kubernetes. We handle autoscaling, GPU instance selection, fallback routing, and latency optimisation so your models serve requests reliably under variable load.

LLMOps Tooling

End-to-end MLOps pipelines for language model workflows: experiment tracking with MLflow or Weights & Biases, model versioning and promotion gates, automated regression testing before any model update ships to production. We've built LLMOps pipelines that catch quality regressions before they reach users.

Kubernetes-Based AI Deployment

Container orchestration for AI workloads using EKS or GKE — with Helm charts for environment-specific configuration, Karpenter for spot/on-demand autoscaling, and GitOps-based deployment via GitHub Actions or ArgoCD. We've executed zero-downtime migrations from legacy infrastructure to managed Kubernetes.

AI Observability

Monitoring purpose-built for AI systems: token cost tracking, latency percentiles by model and prompt template, hallucination rate monitoring, output quality drift detection, and alerting integrated with PagerDuty or Slack. You can't improve what you can't measure.

CI/CD for Models

Automated pipelines for model retraining, fine-tuning, and evaluation — triggered by data drift, scheduled runs, or manual gates. New model versions go through automated evaluation against held-out test sets before promotion. No more manually testing models before shipping.

Cost Optimisation

AI inference costs can scale unexpectedly. We implement caching layers (semantic caching for repeated queries), batch processing for non-real-time workloads, model routing (cheap models for simple queries, expensive models for complex ones), and reserved capacity planning to reduce cost per query by 30–60%.

How We Work

We typically start with an infrastructure audit of your current setup — identifying reliability risks, cost inefficiencies, and observability gaps. From there we build a prioritised roadmap and execute in two-week sprints.

Our stack: AWS (EKS, SageMaker, Batch, Lambda), GCP (Vertex AI, GKE), Terraform, Helm, Kubernetes, Docker, GitHub Actions, ArgoCD, MLflow, Weights & Biases, Datadog, Prometheus, Grafana, vLLM, TGI.

Related Work

We migrated a production workloads to AWS EKS using Terraform and Helm — zero downtime, 40% infrastructure cost reduction, and deployment frequency increased from weekly to multiple times per day. On-call incidents dropped 60% in the first 90 days.

Frequently Asked Questions

LLMOps (Large Language Model Operations) is the set of practices, tools, and infrastructure for deploying and operating LLM-powered systems in production. It includes model versioning, evaluation pipelines, monitoring for drift and quality degradation, and CI/CD for model updates. Without it, AI systems are fragile: models get updated without regression testing, costs spiral without visibility, and failures are invisible until users complain.
No. We work with what you have and improve it incrementally. Most engagements start with an audit of existing infrastructure, then prioritise the highest-impact improvements — often observability and cost controls first.
We implement blue-green or canary deployment patterns for model updates, with automated evaluation gates that must pass before traffic shifts. A new model version is tested against a held-out evaluation set, and only promoted if it meets quality thresholds. Traffic can be split (e.g., 5% to new model, 95% to old) for gradual rollout.
Beyond standard infrastructure metrics (CPU, memory, latency), AI-specific monitoring tracks: token usage and cost per request, output quality scores (if you have ground truth), input/output length distributions, error rates by error type, and latency breakdown by model vs. retrieval vs. orchestration. We instrument all of this using Datadog or Grafana.
Yes. Common levers: semantic caching (serve cached responses for similar queries), prompt compression, model routing (use GPT-4o-mini for classification, GPT-4o for generation), batching async workloads, and switching non-latency-sensitive tasks to open-source models. We typically target 30–50% cost reduction in the first engagement.

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