DevOps for the AI Era

AI applications require a different infrastructure stack. LLMOps, model serving, GPU-aware pipelines, and experiment tracking — we build the DevOps foundation that AI teams need.

Duration: 4-12 weeks Team: 1 AI DevOps Architect + 1 MLOps Engineer
The Challenge

You might be experiencing...

Your data science team produces models that never make it to production — the gap between Jupyter and a production API is a 6-month engineering project.
You're serving an LLM in production but have no observability — you don't know latency, cost per request, or drift.
Your AI application's infrastructure costs are unpredictable — GPU instances running 24/7 for workloads that run for 2 hours per day.
You need to A/B test two model versions in production but have no infrastructure for traffic splitting between model versions.

AI-native DevOps bridges the gap between AI research and AI production. As Bahrain engineering teams build more AI-powered products — particularly in fintech, government digital services, and healthcare — the infrastructure underneath them requires specialist knowledge that traditional DevOps engineers don’t always have.

Bahrain’s position as a regional fintech hub and the support of initiatives like Bahrain FinTech Bay mean that AI-powered financial services are a growing priority. Getting the LLMOps and model serving infrastructure right from the start is far cheaper than retrofitting it later.

Contact us to discuss your AI infrastructure challenges — free 30-minute consultation with our AI DevOps team.

Our Approach

Engagement Phases

Week 1-2

AI Infrastructure Audit

Assess current AI/ML infrastructure: how models are trained, versioned, deployed, and monitored. Identify the gap between experiment and production. Map GPU resource utilisation and cost.

Weeks 3-6

MLOps Pipeline

Implement ML pipeline: data versioning (DVC), experiment tracking (MLflow or W&B), model registry, and automated retraining triggers. Configure reproducible training environments with container-based jobs.

Weeks 7-10

Model Serving Infrastructure

Deploy model serving: vLLM or TGI for LLMs, Triton Inference Server for classical ML. Configure GPU-aware Kubernetes scheduling. Implement A/B testing and canary model deployments.

Weeks 11-12

LLMOps & Observability

Implement LLM-specific observability: token cost tracking, latency percentiles, prompt/response logging (with PII redaction), and model drift detection. Configure alerts for degraded model quality.

What You Get

Deliverables

AI infrastructure architecture diagram
MLOps pipeline (training → evaluation → registry → production)
Model serving infrastructure (GPU-aware Kubernetes)
Experiment tracking setup (MLflow or Weights & Biases)
LLM observability dashboard (cost, latency, quality metrics)
A/B testing infrastructure for model versions
GPU resource optimisation (spot instances, auto-scaling)
Expected Outcomes

Before & After

MetricBeforeAfter
Model Time to Production3-6 months: manual handoff from data science to engineering1-2 weeks: automated pipeline from training to serving
GPU Cost24/7 GPU instances for batch workloads50-70% cost reduction via spot instances and auto-scaling
AI Production VisibilityNo observability — flying blind on model performanceFull visibility: cost, latency, quality, and drift alerts
Technology

Tools We Use

MLflow / Weights & Biases vLLM / TGI / Triton NVIDIA GPU Operator / KEDA DVC LangSmith / Phoenix
Common Questions

Frequently Asked Questions

What is LLMOps?

LLMOps (Large Language Model Operations) is the set of practices for deploying, monitoring, and maintaining LLM-based applications in production. It extends MLOps with LLM-specific concerns: prompt versioning and evaluation, token cost management, context window optimisation, RAG pipeline observability, and safety monitoring. As LLMs become a core part of Bahrain engineering products — particularly in fintech, government, and healthcare — LLMOps is becoming as essential as standard DevOps.

Do we need GPU servers on-premise or can we use cloud GPUs?

For most Bahrain companies, cloud GPUs (AWS p3/p4/g5, Azure NCsv3, GCP A100s) are the right answer — they offer flexibility, no capital expense, and spot pricing for training workloads. AWS me-south-1 in Bahrain has limited GPU instance types, so training workloads often run in EU or US regions with inference served locally. On-premise GPUs make sense when you have very high GPU utilisation or strict data sovereignty requirements. We model the economics for your specific workload before recommending.

How do we evaluate LLM quality in production?

LLM quality evaluation in production uses a combination of: automated metrics (BLEU, ROUGE, BERTScore for summarisation tasks; exact match for structured outputs), LLM-as-judge (using a reference model to score outputs), human feedback collection via thumbs up/down or rating interfaces, and A/B testing between model versions. We implement the right evaluation approach for your use case — there's no one-size-fits-all LLM metric.

Get Started for Free

Schedule a free consultation. 30-minute call, actionable results in days.

Talk to an Expert