Most AI-first startups are overpaying on their LLM bill by 40-75%.
Not because of bad engineering. Because the priority was always shipping, not optimising. We audit your AI spend, find the waste, and give you a prioritised roadmap to fix it.
Your AI bill is growing faster than your revenue.
Where the money goes
- •Defaulting to GPT-4o or Claude Sonnet for every task, including ones a budget model handles just as well
- •No prompt caching — paying full price for the same system prompt on every single call
- •Full conversation history sent on every agent step — token count grows with every message
- •No gateway or observability — no idea which feature is responsible for which cost
- •Async jobs running at real-time prices — missing the Batch API's flat 50% discount
- •Indic language tokenisation overhead — Hindi and regional language inputs cost 3-5x more tokens than the English equivalent
What it means for your business
- •AI spend grows proportionally with usage, not with value delivered
- •Unit economics story breaks in investor conversations
- •No FinOps discipline — costs spike again after you fix them once
- •Engineering time wasted on billing surprises instead of product
A 2-4 week engagement. From a confusing AI bill to a team that owns its costs.
Three phases with clear outputs. You always know where you are and what comes next.
Access & Discovery
We get read-only access to your API billing dashboard and meet the engineering team. We map every LLM touchpoint — which models, which features, which call volumes — and tag them by cost impact. No assumptions, no guesses.
Output
Full LLM cost map by feature, model, and environment.
Opportunities & Implementation
We produce a findings report with every opportunity prioritised by savings and implementation effort. Then we work through the roadmap with your team — implement quick wins together, and coach them through the larger structural changes. The knowledge stays inside the company.
Output
Prioritised savings roadmap · Quick wins implemented · Team coached through key changes.
Governance
We put in place the structure that stops costs from creeping back up: per-feature cost attribution, budget alerting thresholds, model selection criteria, and a spending review rhythm your team can run without us. No ongoing retainer — the engagement ends with a team that owns its costs independently.
Output
LLM FinOps governance playbook · Cost attribution framework · Budget alerting configured and handed off.
Everything you need to own your LLM costs independently.
- LLM cost map by feature, model, and call volume (Phase 1)
- Prioritised findings with savings per item (Critical / High / Medium / Low)
- Quick wins implemented with your team
- 2-4 week implementation roadmap
- LLM FinOps governance playbook
- Model selection criteria and routing recommendations
- Budget alerting configured and handed off
Typical ROI: 5-15x the engagement cost in first-year savings.
Built for AI-first seed and Series A founders.
Your OpenAI or Anthropic bill has crossed ₹2L/month and you are not sure what is driving it. You are approaching a Series A and need clean AI unit economics. You do not have a dedicated ML or FinOps person.
You are scaling fast and the AI bill is growing faster than revenue. It is the first time you have done a structured LLM cost review. Investors are starting to ask about your AI infrastructure margin.
Works across OpenAI, Anthropic, Google Gemini, Mistral, and self-hosted models. Multi-provider setups supported.
Numbers from real engagements.
"We did not realise we were paying full price for the same system prompt 50,000 times a day. Turning on prompt caching took one afternoon and cut our Anthropic bill by 60%."
— CTO, Seed-stage SaaS company
Led by someone who has actually done this.
Nishant Nagwani is a technology executive with 15+ years of experience scaling software systems across fintech, edtech, and enterprise SaaS. He has architected platforms serving millions of users and delivered 40–50% cloud cost reductions for funded startups in the Indian and US markets.