Make LLM cost part of the engineering conversation
AI cost optimisation is not a final spreadsheet exercise. The cost of an AI feature is connected to the workflow it supports, the models and infrastructure behind it, and how often people use it. Teams that make those relationships visible can discuss trade-offs with more clarity while they design and ship.
My role is to help product and engineering teams look at that system as a whole. The emphasis is practical: understand the use case, connect it to LLM usage, and keep cost considerations alongside quality and delivery. This is a focused service area within my wider AI consulting work.
What the cost optimisation focus covers
- Usage awareness: connect product workflows to the LLM usage they create.
- Engineering trade-offs: discuss model and infrastructure choices in the context of the feature.
- Delivery discipline: treat cost as part of building and operating an AI-first system, not as a separate afterthought.
- Team understanding: give product and engineering people a shared language for AI cost decisions.
Cost questions often sit beside adoption and agent questions. See the related pages on AI adoption strategy and AI agent frameworks for those connected areas.
Relevant experience at Inc42 Media
At Inc42 Media, an AI-first approach cut shipping by roughly 80%. The work involved building AI infrastructure from zero, creating in-house agent frameworks, and working on LLM cost optimisation. These are the facts behind this page: the cost conversation belongs with the systems and workflows that make AI delivery possible.
I have been at Inc42 since 2020. Earlier experience includes Beebom (2018–2020) and Clicue (2016–2018); I have also been the founder of Gipcus since 2019. I am based in Delhi / Greater Delhi Area and work with teams across India.
Frequently asked questions
What does AI cost optimisation cover?
It connects LLM usage with the product use case, models, infrastructure, and delivery decisions around an AI feature.
Why discuss cost during AI adoption?
Cost is part of operating an AI feature, so product and engineering teams benefit from considering it alongside quality and delivery.
What experience do you have with LLM cost optimisation?
At Inc42 Media, I worked on LLM cost optimisation while building AI infrastructure from zero and developing in-house agent frameworks.
How do I start?
Email er.ashish029@gmail.com with your product context and the AI cost question you are working through. You can also connect on LinkedIn.
Talk through your AI cost question
If LLM usage, AI infrastructure, or product priorities are creating a cost decision for your team, send a short brief to er.ashish029@gmail.com. We can start with the context and define the useful next conversation.
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