How to Perform Cloud Cost Optimization Using GenAI?

Step 1: Start by Gathering the Right Cloud Data

Everything starts with visibility. But to give GenAI real context, the data must go beyond spend summaries.

Here’s the foundational data to collect:

→ Billing exports: From AWS CUR, Azure EA, or GCP billing export

→ Resource metadata: tags, labels, account IDs, environment

→ Usage metrics: CPU/memory/network activity logs

→ Infrastructure state: live inventory via cloud APIs or IaC

→ Business mappings: team ownership, projects, environments

This data is best stored in a queryable system like BigQuery, Athena, or a structured lakehouse.

What matters most isn’t where it’s stored, but how well it’s cleaned and connected.

Step 2: Turn Raw Data into Language-Ready Context

LLMs need structured language context, not raw logs.

That means translating spend and usage patterns into short, descriptive summaries, something like:

EC2 instance i-0623 ran at 13% CPU for 23 days. Tagged ‘test’, no deployment events, cost $92.37 in us-west-2.

RDS-Postgres instance in ap-south-1 cost $487 in April. Low query volume. Connected to BI pipeline that paused after May 1.

Each summary includes:

➡️ Cloud resource

➡️ Cost + usage

➡️ Business relevance (tags, owner, pipeline connections)

➡️ Historical patterns or anomalies

These records can then be embedded into a vector database like Weaviate, pgvector, or Pinecone for fast similarity search.

Step 3: Add GenAI on Top – Using RAG for Reasoning

GenAI works best when grounded in real context. That’s where RAG (Retrieval-Augmented Generation) fits in.

A typical setup looks like this:

1. User asks a natural language query: “Why did data-lake-prod costs spike last month?”

2. Backend pulls 10–20 relevant context chunks from the vector database:

→ Billing anomalies

→ Resource-level summaries

→ Usage history

→ Infra changes and CI/CD events

3. These are fed into a structured prompt and passed to an LLM (like GPT-4, Claude, Mistral).

4. The model generates a response that explains what happened, suggests next steps, and optionally recommends changes.

A bonus read for you: RAG as a Service

Step 4: Embed it into Daily Workflows

The most effective GenAI systems aren’t used through dashboards, they’re embedded into where teams already work.

Common touchpoints:

➜ Slack or Teams: Ask follow-up questions like, “What’s unused in staging?”

➜ CI/CD pipelines: Run cost checks before merging infra PRs

➜ Daily reports: Send cost insights directly to the right teams

➜ Dashboards: Add “explain this” buttons to billing charts

This shifts cloud cost optimization from quarterly reviews to daily, contextual nudges.

Step 5: Build Feedback and Trust Loops

For the system to be adopted, it has to be explainable and accountable.

Key practices:

➜ Log and audit all responses: Store prompts, context, and model output.

➜ Allow thumbs up/down: Enable human feedback on every suggestion.

➜ Always link to source data: Every insight should have a “see how” button.

➜ Use simulation mode first: Run dry actions before triggering real cleanups.

This avoids blind trust and builds confidence over time.

Step 6: What the Stack Looks Like

Here’s the typical system architecture at a high level:

LLMs used can range from OpenAI and Claude for quick prototypes to open-source (Phi-3, Mistral, LLaMA 3) for enterprise control.

Need Help Building This?

If you’re exploring how GenAI can make cloud cost optimization more explainable, actionable, and automated – this kind of system is exactly what we’ve been helping teams build at Azilen.

From connecting cloud billing data to designing Generative AI copilots that work inside Slack or your dashboards, we can help architect and implement the entire stack. Whether you want to prototype quickly or build something enterprise-grade, let’s talk.

Connect with us to explore how we can support your cloud and GenAI roadmap.

Top FAQs on Cloud Cost Optimization Using GenAI