Private corporate batches, live online cohorts and 1-on-1 mentoring in cloud cost allocation, rate and usage optimisation, forecasting and unit economics — taught by a practitioner who runs it in production.
20 years across DevOps, SRE and Security · 10,000+ engineers trained · Trained teams at JPMorgan Chase, Verizon, Nokia and the World Bank
Principal DevOps Engineer & Architect
Rajesh teaches FinOps from the billing data upward: querying cost and usage exports directly, building an allocation model that survives shared clusters and untagged resources, managing a commitment portfolio by coverage and utilisation, and separating rate levers from usage levers so savings are attributable. Sessions treat cost as an engineering signal alongside latency and error rate — instrumented, alerted on and owned by the team that creates it — and give equal time to the reporting and forecasting side, because a FinOps practice that engineering trusts but finance cannot reconcile does not last.
Twenty years across DevOps, SRE and Security, in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe and others. He has trained engineers at JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus — more than 10,000 people personally. He teaches what he runs, not what he reads.
You are booked with a named engineer, and that is who turns up. Marketplaces and larger providers rotate whoever is free, so the person who sold you the agenda is rarely the person teaching it.
The same trainer is available for the next engagement, which matters when a team builds on what it learned last time.
Your batch is assigned a named trainer before it starts, and that is who teaches it. See the full faculty.
Engagements are matched on the tool, not the calendar. For FinOps that means a trainer who has run it in production — cloud cost allocation, rate and usage optimisation, forecasting and unit economics — rather than whoever is free that week. You are told who is teaching before you commit, and that person is on the discovery call that shapes the agenda.
Where a batch is large enough to need a second trainer, the pairing is declared up front. The lead trainer stays accountable for the syllabus and the assessment either way.
Principal DevOps Engineer & Architect
Twenty years across DevOps, SRE and Security in principal and architect roles at PayPay, SoftwareAG, ServiceNow, JDA Software, Intuit, Adobe, IBM/Emptoris, Ness, MindTree and Accenture. He has trained more than 10,000 engineers personally, at organisations including JPMorgan Chase, Verizon, Nokia, the World Bank, VMware, Oracle, Mercedes-Benz and Airbus. He teaches what he runs, not what he reads.
Teams of 8–30
Custom agenda, your timezone, onsite or online, NDA-friendly.
Request a quoteIndividual engineers
A private instructor and a curriculum built around your goal.
₹99,999Individuals who want peers
Scheduled batch, max 8 to 10 hours of live instruction.
₹34,999A private batch starts with a discovery call. We look at the stack you actually run — the CI system, the cloud, the constraints — and map the agenda onto it, so examples use your topology rather than a generic one.
Delivery is onsite at your premises, live online, or hybrid, scheduled around your release calendar rather than ours. Batches run 8 to 30 engineers.
Every attendee leaves with recordings, slides, lab repositories and a completion certificate. You receive an attendance and assessment report. Invoicing supports PO and GST.
Talk to us about a private FinOps batchFinOps is the practice of managing variable cloud spend as an engineering property rather than as a finance report that arrives three weeks late. Cloud turned infrastructure from a capital purchase decided once a year into a per-second operating cost created by thousands of individual engineering decisions, and FinOps is the operating model that puts the people making those decisions in contact with what they cost. The FinOps Foundation describes it as three repeating phases — inform, optimise, operate — carried out jointly by engineering, finance and product rather than delegated to any one of them.
The mechanics start with billing data. AWS Cost and Usage Reports, Azure cost exports and GCP billing exports are line-item datasets, tens of millions of rows a month for a large estate, and the FOCUS specification now gives them a common schema. On top of that sits allocation: account and subscription hierarchy, a tagging policy that is actually enforced, and a defensible method for splitting the costs no tag can reach — shared clusters, data transfer, support charges, idle capacity and reserved-instance discount distribution.
Optimisation then splits cleanly into two levers that are often confused. Rate optimisation changes the price paid for the same consumption: reserved instances, savings plans, committed use discounts, spot capacity and negotiated agreements, managed as a portfolio with coverage and utilisation targets. Usage optimisation changes the consumption itself: rightsizing, autoscaling, scheduling non-production, storage lifecycle, egress-aware architecture and query-level costs on data platforms. Both are judged against unit economics — cost per customer, per transaction, per tenant, per model inference — because a bill that falls while revenue falls faster is not a win.
Cloud is now one of the largest controllable cost lines in most technology organisations, and the era of funding growth without questions has ended. Boards ask about cloud gross margin, private-equity owners ask for cost per customer, and CFOs increasingly want commitment decisions justified rather than renewed. That pressure lands on engineering, because almost every meaningful lever — instance families, autoscaling policy, data retention, architecture — is an engineering decision.
The technical picture has also become harder. Kubernetes made a single line item on the bill into shared capacity used by twenty teams, so allocation now requires its own instrumentation. Managed data platforms charge by query and by scan. GPU and inference capacity for AI workloads has become the fastest-growing and least understood cost in many estates, often bought under time pressure with no allocation model at all. Multi-cloud makes each of these problems appear three times in three different schemas.
The demand is for engineers and analysts who can work at the line-item level and still speak in business terms. Downloading a cost report is easy; building an allocation model people accept, running a commitment portfolio without stranding capital, proving that a rightsizing change is safe, and reporting a unit cost the finance team will sign off on is the work organisations struggle to staff.
Why cloud spend behaves differently from every other IT cost, and what an operating model has to do about it. The inform, optimise and operate phases, the personas involved, the capabilities that make up the practice, and an honest maturity assessment — including the common failure where FinOps becomes a reporting function nobody in engineering reads.
Topics: Variable spend, decentralised decisions and the accountability gap · The FinOps Framework: inform, optimise, operate · Personas — engineering, finance, product, procurement, leadership · Crawl, walk, run maturity and honest self-assessment · Where the practice sits organisationally: central team, embedded, or hybrid · Anti-patterns: cost police, one-off savings drives, dashboards nobody opens
Getting under the console. The structure of AWS Cost and Usage Reports, Azure cost exports and GCP billing exports; the difference between list, amortised, blended and effective cost; how discounts and credits appear; and building a queryable cost dataset with Athena, BigQuery or a warehouse so questions can be answered in minutes.
Topics: CUR, Azure cost exports and GCP billing export structure · Amortised versus unblended versus effective cost, and when each is correct · Credits, refunds, support charges, marketplace and private pricing · The FOCUS specification and normalising multi-cloud billing · Building a queryable cost dataset and partitioning it sensibly · Reconciling your dataset against the invoice, to the cent
Deciding who pays for what, and getting that decision accepted. Account and subscription hierarchy as the primary allocation boundary, tagging policy and enforcement, and the harder problem of shared cost — clusters, data transfer, observability, support and idle capacity — where the method matters more than the precision.
Topics: Hierarchy first: accounts, subscriptions, projects and management groups · Tagging policy, enforcement with policy as code, and remediating untagged spend · Shared cost methods: even split, proportional, weighted, and their political consequences · Showback versus chargeback, and choosing deliberately between them · Allocation coverage as a tracked metric · Publishing a cost model teams can predict and dispute
The hardest allocation problem in most estates: one bill, one cluster, many tenants. Splitting node cost across namespaces and workloads, the gap between resource requests and actual usage, idle and system overhead, and turning the resulting numbers into a rightsizing conversation rather than an argument.
Topics: Cost per namespace, workload, label and team · Requests versus usage, and who pays for the difference · Idle capacity, system overhead and control-plane cost · OpenCost and Kubecost — data model and limitations · Persistent volumes, load balancers and network cost inside a cluster · Right-sizing requests and limits with real utilisation data · Cluster consolidation, bin packing, spot node pools and karpenter-style provisioning
Commitment discounts treated as portfolio management. Reserved instances, savings plans and committed use discounts compared, coverage and utilisation as the two governing metrics, laddering to manage renewal risk, spot capacity and its interruption model, and the negotiation levers that sit above all of it.
Topics: Reserved instances, savings plans and committed use discounts compared · Coverage and utilisation targets, and why both are needed · Commitment laddering, expiry management and renewal risk · Break-even analysis and the cost of over-committing · Spot and preemptible capacity: interruption handling, diversification and suitable workloads · Enterprise agreements, private pricing and marketplace commitments · Who owns the buy decision and how it gets approved
Changing the workload rather than the price. Rightsizing with evidence instead of vendor recommendations, autoscaling and scheduling, storage tiering and lifecycle, egress-aware architecture, and the query-level costs of managed data platforms and AI inference that now dominate many bills.
Topics: Rightsizing with utilisation evidence, and the reliability risk of getting it wrong · Autoscaling policy, scale-to-zero and non-production scheduling · Storage classes, lifecycle rules, snapshot sprawl and orphaned volumes · Data transfer and egress: the architecture decisions that create it · Managed data platform cost — scanned bytes, warehouse sizing, materialisation · GPU and inference cost: batching, quantisation, model selection and idle accelerators · Ranking opportunities by saving, effort and risk rather than by saving alone
Making the practice permanent. Forecasting and budgets that survive contact with growth, anomaly detection that fires before the invoice, unit economics that connect spend to the business, cost checks inside the engineering workflow, and the reporting cadence that keeps everyone in the same conversation.
Topics: Forecasting methods: trend, driver-based and commitment-aware · Budgets, thresholds and what actually happens when one is breached · Cost anomaly detection and routing alerts to the team that caused them · Unit economics: choosing the denominator and defending it · Cost in the engineering workflow — pull-request estimates, guardrail policies, architecture review · KPIs for the practice itself: allocation coverage, commitment coverage, forecast accuracy, savings realised · Monthly and quarterly rhythm with engineering, finance and product
We rebuild the agenda around the tools you actually run.
Request a custom agendaLoad a month of cost and usage export into a query engine, partition it, and answer allocation questions the console cannot — then reconcile the total against the published invoice.
Write and enforce a tagging policy, measure allocation coverage before and after, and design a defensible split for the shared spend that no tag will ever reach.
Allocate cluster cost to namespaces and teams with OpenCost, separate requested from used capacity, and produce a showback report a team lead would accept.
Analyse twelve months of usage, model reserved instance and savings plan options against it, and produce a laddered purchase plan with a stated break-even and risk position.
Join billing data to an application metric, publish a unit cost, and use it to evaluate an architecture change that lowers the unit cost while total spend grows.
Deliver allocation, a commitment position, a ranked optimisation backlog, a forecast and an anomaly alerting path — the artefacts a FinOps function needs to survive its first quarter.
Billed yearly at ₹9,996
Max 8 to 10 hours of live instruction
Full program, private instructor
8–30 engineers · custom agenda · onsite or online · PO and GST invoicing
Refunds. If we cancel or postpone a cohort, you get a full refund within 15 days. There is no money-back guarantee otherwise.
Terms. Course material remains licensed to the attendee. Read the terms.
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Certificate of completion
Very detailed explanation and has lots of patience in attending the questionnaire. Thanks again for your wonderful sessions.
Good discussion, helped us to understand different tools in SRE.
Got good lab sessions which kept the new DevOps tool learnings to the point and it helped a lot in my career.
I took Terraform training with the tutor named Mithilesh. I requested to tailor the course curriculum for my needs. He did an excellent job of showing me how to write the Terraform script per the instructions provided.
My experience with the AIOps training was positive. The course covered important topics in a structured way, and Rajesh Kumar explained the concepts patiently. I found the practical aspects particularly helpful because they made the technical content easier to understand.
I was looking to improve my understanding of AIOps, and this training helped me achieve that goal. Rajesh Kumar explained the subject in a structured and practical manner. The sessions on different AIOps concepts were informative.
| What matters | YouTube + blogs | Generic online course | Freelance marketplace | DevOpsSchool |
|---|---|---|---|---|
| Named practitioner | No | Rarely | Varies per booking | Yes — same trainer each time |
| Production experience | Unknown | Unknown | Unverified | 20 years, named employers |
| Custom agenda | No | No | Sometimes | Built from your stack |
| Onsite delivery | No | No | Sometimes | Yes |
| Lab environment | None | Sandbox that expires | Varies | Your own cloud — skill goes with you |
| Assessment | None | Quiz | Rarely | Assignments + capstone per module |
| Per-attendee certificates | No | Sometimes | Rarely | Yes |
| Corporate invoicing | No | Limited | Varies | PO and GST |
| Post-training support | None | Forum, time-limited | None | Lifetime forum access |
Tell us the team, the stack and the timeline. You'll get a straight answer, not a sales sequence.
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