Context : 

As companies adopt generative AI, teams face a lot of common challenges:

• Which tasks can AI automate to free up human time, resources and reduce costs?
• Which functions across the organisations can we leverage AI and save costs?
• How to optimize costs of AI agents?
• What agent actions consume credits and costs?
• Which LLM should we use for our use case - balancing cost, latency, and quality?

AI developers, product managers, tech leads, and senior decision-makers need clarity on AI architecture, selecting the right LLMs, calculating ROI, estimating implementation and inference costs, and developing financial literacy around the total cost and value of deploying AI agents.

Build an AI Cost Optimization Advisor Agent on Lyzr Studio that helps enterprise teams by acting as the agent architect - a consultant to optimise costs for the organization through AI.

• AWS Bedrock to access models like Claude, Mistral, and Nova.
• Amazon Nova models as cost-efficient alternatives for common workloads.
• UI builders (like replit, cursor, lovable) and automation tools if needed.

• Each user gets 500 Lyzr credits on signing up to build their solution.
• Make sure to have a detailed understanding of credit consumption, prompting, which LLMs cost less etc.
• Use your credits wisely and carefully. Creating agents and orchestration does not consume credits, only testing does - be conscious of the frequency of testing and iterating.