AI on Azure: How to Control Costs Without Slowing Innovation

Choose the right model - not the biggest one

The most powerful model is not always the best choice. Each Azure OpenAI model (GPT-4o series, GPT-4.1, Phi-3, open-source models, etc.) has drastically different inference costs.

Best practices:

• Start with smaller, cheaper models (e.g., Phi-3, GPT-4o-mini).
• Scale up only if real usage requires it.
• Benchmark different models, a well-engineered prompt can often outperform brute force.
• Use batch processing whenever possible.

Monitor consumption in real time

Azure provides native tools to prevent unpleasant surprises on the invoice:

• Cost Management + Billing
• Budgets & Alerts
• Azure Monitor / Application Insights for request volume and latency
• Quota limits to cap unexpected spikes

At Luza, we always recommend setting automated alerts via email or Teams when costs approach predefined thresholds.

Apply technical limits (not just financial ones)

Cost control is not only about budgets, it’s about technical guardrails. Set limits on:

• maximum tokens per request
• maximum input size
• request rate per user or app
• number of actions a GenAI agent can perform in a single reasoning cycle

This is critical in Agentic AI, where agents may trigger cascaded operations.

Poorly designed RAG = unnecessary costs

RAG (Retrieval-Augmented Generation) can be a cost-saver or a cost accelerator depending on the architecture. 

Key considerations:

• Use meaningful document chunking (200–500 tokens).
• Choose low-cost embedding models (Phi-3 embeddings, for example).
• Reduce LLM calls using:
• preprocessing pipelines
• semantic validation
• result caching

Efficient RAG ≠ “always ask the model.”

Implement smart caching to avoid redundant inference

Many AI queries repeat patterns. A well-designed cache can reduce costs by up to 60%.

Types of caching:

• Semantic cache (reuse responses to similar questions)
• Prompt cache
• Redis caching
• Storing agent decisions to avoid repeated reasoning

Choose the right architecture: Serverless vs. Kubernetes

When running AI applications:

• Azure Functions / Logic Apps → cost-effective for event-driven or low-frequency workloads.
• AKS / Container Apps → ideal for heavy pipelines, batch operations, or custom model hosting with GPUs.

Choose based on:

• workload predictability
• latency requirements
• GPU needs
• maintenance versus flexibility

Control Dev/Test environments before they explode your budget

Non-production environments often hide runaway costs.

Best practices:

• Shut down non-essential resources outside working hours.
• Use Azure Policies to block expensive resources (e.g., premium GPUs).
• Apply RBAC to prevent teams from deploying unnecessary infrastructure.

Leverage efficient open-source models when appropriate

Azure now supports optimized open-source models such as Llama, Mistral, and Phi-3 across multiple environments.

Advantages:

• Lower cost
• Faster inference
• Easier fine-tuning for specific business needs

Great for organisations requiring AI scalability without excessive cloud spend.

Observability is not optional

Every production-grade AI architecture must include:

• Telemetry for LLM calls
• Logging for agent actions
• Cost-per-user or cost-per-feature tracking
• Dashboards via Azure Monitor or Fabric

You cannot control what you cannot see.

AI responsibility also includes cost responsibility

At Luza, we believe Responsible AI isn’t only about ethics, governance and safety, it’s also about cost efficiency.

Teams should understand:

• token usage
• inference costs
• quotas and limits
• efficient prompting
• the financial impact of agents running autonomously

Responsible AI = Sustainable AI.

Conclusion

AI innovation doesn’t have to come with an unpredictable bill. With the right governance, architectural choices and continuous cost optimization, you can build AI systems that deliver real business value while staying financially controlled.

At Luza Tecnologia, we have learned and we can help organizations to take full advantage of Azure by implementing efficient architectures, robust governance and cost-optimized AI strategies so the cloud empowers innovation without compromising budgets. 

by Gonçalo Pedro, Data Engineer at Luza