The real cost of AI: tokens, models and who pays the bill
Why AI bills keep rising as token prices fall, how caching and model choice move the cost curve, and when a smaller or local model is the better call.
AI Advisor in Hyderabad · Founder, 256 Technologies
Dr. Hitesh
Sarda
I help leadership teams decide what AI to build, buy or skip, and prove it with the smallest pilot that works. 25+ years building production systems at Citi, Virtusa, Yash and ValueLabs; today I run 256 Technologies, an applied AI lab in Hyderabad.
- Twice a startup co-founder, and seven years building enterprise systems for a global bank in Frankfurt
- Author of Implementing AI Agents in Your Company, and DBA research on how 371 SaaS companies price AI features
- Profiled in ET Focus, Hyderabad
- Spoken at AWS ISV Ignite, IIM Udaipur and the Visakhapatnam Retail Summit
- Writing here since 2009, and on LinkedIn
How I can help
Book an advisory hour →AI advisory
One AI decision at a time: is the use case worth doing, should you build or buy, and what is the smallest pilot that proves it. Starts with a single hour.
Book an advisory hour →Fractional Head of AI
Senior AI leadership a few days a month: priorities, vendor and model choices, governance, the AI bill, and reporting to the board.
How it works →Leadership training
A six-week program in which your leaders use AI on their own real work and finish with a governance framework and a 90-day roadmap.
See the program →Speaking
Keynotes and workshops on what AI costs, how to deploy agents safely, and where advantage comes from when everyone has the same models.
Talks and formats →If you want to stop experimenting and start delivering real business results with this technology, this is where you start.
Shammik Gupta, CEO, INSORCE, on Implementing AI Agents in Your Company
Start here: guides
All guides →
Deploying AI agents safely: autonomy, permissions and blast radius
How much autonomy to give an AI agent, how to scope its permissions by blast radius, and how to keep a record of what it actually did in production.
Leading engineering teams that code with AI
What changes when AI writes most of the code: review becomes the bottleneck, technical debt costs more, and someone has to decide what the agent prefers.
AI strategy for leadership teams: pilots, build versus buy and moats
How to run an AI pilot that tells you the truth, when to build versus buy, where AI pays off first, and why the model you pick is rarely your advantage.
AI in retail and loyalty: when your next customer is an agent
What changes for retailers and loyalty programs when AI agents start shopping for people, and which parts of the customer relationship stay worth owning.
Free templates and checklists
All resources →
AI Readiness Assessment
Twenty-five questions that tell you whether one specific AI use case is ready to fund, across the business case, checking, data, ownership and risk.
Get the PDF
AI Use Case Prioritization Matrix
Score up to eight candidate AI use cases on the criteria that decide whether AI pays off, then write a one-page business case for the winner.
Get the PDF
AI Agent Governance Framework
A fill-in template for governing AI agents: an agent register, autonomy tiers, a blast-radius worksheet, a deployment checklist and audit log requirements.
Get the PDF
AI Vendor Evaluation Checklist
Questions to send an AI vendor before the demo, with what a good answer sounds like, a scoring sheet for three vendors, red flags and contract terms.
Get the PDF
Recent writing
Full archive →
Be more ambitious with software, but only where you can prove it works
Building software got cheap enough to justify projects you once rejected. What should decide which ones you fund now is whether you can prove the result is right.
Goldman limited coding agents by job title. Limit them by blast radius.
Goldman Sachs pulled coding agents from most traders and bankers. The control that works is narrower permissions per agent, and it lets you keep the demand.
The real price of an agent sandbox is the team that patches it
Replit went through 6,211 Linux kernel advisories in five months to keep agent code contained. Ask who does that work before you pick a sandbox vendor or decide to build.
AI just raised the interest rate on your technical debt
A crash bug sat unfixed for two years until AI turned it into remote code execution. Your list of accepted risks was priced for attackers who no longer exist.
Your coding agent now takes instructions from your software vendors
A desktop app updated itself and wrote startup instructions into its users' Codex and Claude Code. Agent configuration is now a procurement and governance question.
Your release date assumed bugs were hard to find
Postgres 19 slipped after AI found old bugs faster than maintainers could review the fixes. Plan your roadmap around verification capacity, not discovery.
Assume your platform vendor will lose your secrets
Railway, OpenCode and Northflank each told customers to rotate their secrets within two weeks. The useful question is what a stolen key costs you before you notice.
Run your next incident drill with the AI turned off
AI is absorbing the routine incidents that used to train on-call engineers. A timed drill with the AI off shows whether your team can still handle the one that matters.
New book · Free PDF
Implementing AI Agents in Your Company
A Blueprint for Innovation
A practical, hype-free playbook for the managers and operators who have to turn AI agents into real business outcomes. Read it free, no signup.
Have an AI decision to make?
Bring the problem to an advisory hour. You leave with the simplest next step, or an honest view that AI is not the answer yet.