Who We Are

Plano Piloto is an independent technology–commercial advisory that helps companies spend less and buy better from the major cloud and enterprise software vendors.

We work only for the client — we don’t resell, we don’t take vendor commissions, and we are focused on protect your P&L.


Our team is made of former senior executives and sales leaders from several of these vendors, so we know how pricing, discounting, approvals, quarter-end pressure and “must-carry” products actually work. We use that inside view to structure renewals earlier, remove unnecessary scope and licenses, and enter negotiations with numbers and terms the vendor will recognize.

 

We typically support CIOs, CFOs, Procurement and FinOps/CloudOps leaders who need to:

  • prepare for a renewal, audit or expansion with one of the big platforms;

  • bring cloud/SaaS run-rate back to budget;

  • standardize governance so savings don’t evaporate in 6–12 months;

  • negotiate based on usage and commercial data, not on vendor urgency.

How we deliver and work



How we deliver and work

How we deliver and work


We begin by collecting what matters — contracts, current quotes, invoices/billing reports, usage and commit data, plus your renewal calendar. In the first 1–2 weeks we turn that into a clean, finance-aligned baseline and show where the money is: what should be reduced, what can be postponed, and what must be renegotiated.

 

Next, we build the commercial plan: what to buy, what to remove, how to structure the commit, and which protections to ask for (portability, exit, audit scope, support, Marketplace). Because our consultants came from these vendors, we know the approval path, quarter/end-of-year pressure, and what they can actually say “yes” to.

 

We then deliver negotiation material your team can run: target rates, give/get, redlines, exec brief and a savings model. We can join the key calls, but the kit is built so CIO, CFO or Procurement can execute.

 

After the deal, we stay on governance — validating savings with Finance, keeping the renewal calendar current, and putting guardrails so Engineering/Business Units don’t grow the spend back in 6–12 months.

 

All under NDA and with no vendor/reseller conflicts.

Examples and Specifics by Vendor

Examples and Specifics by Vendor

Examples and Specifics by Vendor

We tailor the work to the vendor. Each platform prices, measures usage, and negotiates differently, so we don’t run a single “cloud optimization” playbook. For AWS we go deep on CUR, Savings Plans and EDP/PPA; for Azure we focus on MACC, Marketplace eligibility and network/Log Analytics; for GCP it’s billing export, CUDs and NAT/logging; for SAP we center on FUEs and scope freeze; for Salesforce and ServiceNow we start from entitlements and real usage; for Adobe and Workday we align to their native consumption and PEPM models. Then we build the commercial/renewal strategy around those vendor-specific levers.

(01)

AWS

We start from the AWS Cost & Usage Report (CUR) and your payer/member-account structure, then separate: (1) on-demand spend that could be covered by Savings Plans or Reserved Instances, (2) current SP/RI coverage and unused capacity by instance family and Region, and (3) data-transfer and observability/storage patterns. We model 1- and 3-year Savings Plans and RIs exactly as AWS defines them, so Finance can validate the savings. We also flag typical cost drivers: gp2 volumes that should be gp3, long CloudWatch retention, high KMS/API call volume, and inter-AZ or inter-region traffic. On the commercial side, we build a negotiation pack around AWS Enterprise Discount Program (EDP) or Private Pricing Agreement (PPA) targets, Marketplace-eligible spend, support-tier right-sizing, and, when justified, migration/egress credits phrased in a way AWS can approve.

(01)

AWS

We start from the AWS Cost & Usage Report (CUR) and your payer/member-account structure, then separate: (1) on-demand spend that could be covered by Savings Plans or Reserved Instances, (2) current SP/RI coverage and unused capacity by instance family and Region, and (3) data-transfer and observability/storage patterns. We model 1- and 3-year Savings Plans and RIs exactly as AWS defines them, so Finance can validate the savings. We also flag typical cost drivers: gp2 volumes that should be gp3, long CloudWatch retention, high KMS/API call volume, and inter-AZ or inter-region traffic. On the commercial side, we build a negotiation pack around AWS Enterprise Discount Program (EDP) or Private Pricing Agreement (PPA) targets, Marketplace-eligible spend, support-tier right-sizing, and, when justified, migration/egress credits phrased in a way AWS can approve.

(01)

AWS

We start from the AWS Cost & Usage Report (CUR) and your payer/member-account structure, then separate: (1) on-demand spend that could be covered by Savings Plans or Reserved Instances, (2) current SP/RI coverage and unused capacity by instance family and Region, and (3) data-transfer and observability/storage patterns. We model 1- and 3-year Savings Plans and RIs exactly as AWS defines them, so Finance can validate the savings. We also flag typical cost drivers: gp2 volumes that should be gp3, long CloudWatch retention, high KMS/API call volume, and inter-AZ or inter-region traffic. On the commercial side, we build a negotiation pack around AWS Enterprise Discount Program (EDP) or Private Pricing Agreement (PPA) targets, Marketplace-eligible spend, support-tier right-sizing, and, when justified, migration/egress credits phrased in a way AWS can approve.

(02)

Microsoft Azure

We ingest MCA/EA exports and your Microsoft Azure Consumption Commitment (MACC) details exactly as Microsoft defines them — what counts, term, remaining commitment, marketplace eligibility. We profile network and platform costs where Azure often grows: NAT Gateway, Private Link, inter-region traffic, plus Log Analytics and Azure Monitor ingest/retention. We then build a MACC burn-down plan using Marketplace-eligible software to avoid underconsumption at the end of the period. We model Reservations vs Savings Plans and stack Azure Hybrid Benefit when available. Commercial asks typically include egress/migration credits, Unified/Support downshift, explicit MACC accounting in the order, and rate protection in the renewal.

(02)

Microsoft Azure

We ingest MCA/EA exports and your Microsoft Azure Consumption Commitment (MACC) details exactly as Microsoft defines them — what counts, term, remaining commitment, marketplace eligibility. We profile network and platform costs where Azure often grows: NAT Gateway, Private Link, inter-region traffic, plus Log Analytics and Azure Monitor ingest/retention. We then build a MACC burn-down plan using Marketplace-eligible software to avoid underconsumption at the end of the period. We model Reservations vs Savings Plans and stack Azure Hybrid Benefit when available. Commercial asks typically include egress/migration credits, Unified/Support downshift, explicit MACC accounting in the order, and rate protection in the renewal.

(02)

Microsoft Azure

We ingest MCA/EA exports and your Microsoft Azure Consumption Commitment (MACC) details exactly as Microsoft defines them — what counts, term, remaining commitment, marketplace eligibility. We profile network and platform costs where Azure often grows: NAT Gateway, Private Link, inter-region traffic, plus Log Analytics and Azure Monitor ingest/retention. We then build a MACC burn-down plan using Marketplace-eligible software to avoid underconsumption at the end of the period. We model Reservations vs Savings Plans and stack Azure Hybrid Benefit when available. Commercial asks typically include egress/migration credits, Unified/Support downshift, explicit MACC accounting in the order, and rate protection in the renewal.

(03)

Google Cloud (GCP)

We load the Billing export into BigQuery and profile: inter-zone and inter-region traffic, Cloud NAT (per-gateway hour and per-GB), Private Service Connect endpoints, and Cloud Logging metering, which often becomes a bill driver after the free tier. We classify workloads into three buckets: stay on on-demand plus Sustained Use Discounts (SUDs), move to spend-based Committed Use Discounts (CUDs), or move to resource-based CUDs. Where Marketplace spend counts to the commit, we map your ISVs to “commit burners” so you don’t leave money on the table. BigQuery gets its own drill-down: TB scanned vs result size, whether to move to Editions, and partitioning/cluster fixes. In the negotiation memo we usually add migration/egress credit language and support-tier benchmarking.

(03)

Google Cloud (GCP)

We load the Billing export into BigQuery and profile: inter-zone and inter-region traffic, Cloud NAT (per-gateway hour and per-GB), Private Service Connect endpoints, and Cloud Logging metering, which often becomes a bill driver after the free tier. We classify workloads into three buckets: stay on on-demand plus Sustained Use Discounts (SUDs), move to spend-based Committed Use Discounts (CUDs), or move to resource-based CUDs. Where Marketplace spend counts to the commit, we map your ISVs to “commit burners” so you don’t leave money on the table. BigQuery gets its own drill-down: TB scanned vs result size, whether to move to Editions, and partitioning/cluster fixes. In the negotiation memo we usually add migration/egress credit language and support-tier benchmarking.

(03)

Google Cloud (GCP)

We load the Billing export into BigQuery and profile: inter-zone and inter-region traffic, Cloud NAT (per-gateway hour and per-GB), Private Service Connect endpoints, and Cloud Logging metering, which often becomes a bill driver after the free tier. We classify workloads into three buckets: stay on on-demand plus Sustained Use Discounts (SUDs), move to spend-based Committed Use Discounts (CUDs), or move to resource-based CUDs. Where Marketplace spend counts to the commit, we map your ISVs to “commit burners” so you don’t leave money on the table. BigQuery gets its own drill-down: TB scanned vs result size, whether to move to Editions, and partitioning/cluster fixes. In the negotiation memo we usually add migration/egress credit language and support-tier benchmarking.

(04)

Oracle

We read Oracle Universal Credits and match them with current consumption, then layer Oracle Support Rewards on top, using the official reward percentages so Finance can trace the math. We baseline egress (first 10 TB free, then per-region rates) and check whether traffic can be kept in-region via Service Gateway instead of hairpinning through NAT. We also look at your on-prem Oracle support bill to show the real, combined TCO. On the commercial side we push for credit-expiry protections, Marketplace within the allowed cap, clear treatment of Support Rewards in the order, and SLA/credit triggers.

(04)

Oracle

We read Oracle Universal Credits and match them with current consumption, then layer Oracle Support Rewards on top, using the official reward percentages so Finance can trace the math. We baseline egress (first 10 TB free, then per-region rates) and check whether traffic can be kept in-region via Service Gateway instead of hairpinning through NAT. We also look at your on-prem Oracle support bill to show the real, combined TCO. On the commercial side we push for credit-expiry protections, Marketplace within the allowed cap, clear treatment of Support Rewards in the order, and SLA/credit triggers.

(04)

Oracle

We read Oracle Universal Credits and match them with current consumption, then layer Oracle Support Rewards on top, using the official reward percentages so Finance can trace the math. We baseline egress (first 10 TB free, then per-region rates) and check whether traffic can be kept in-region via Service Gateway instead of hairpinning through NAT. We also look at your on-prem Oracle support bill to show the real, combined TCO. On the commercial side we push for credit-expiry protections, Marketplace within the allowed cap, clear treatment of Support Rewards in the order, and SLA/credit triggers.

(05)

SAP

We work with SAP’s own constructs: Full Use Equivalents (FUEs), role catalogs, standard services vs Cloud Application Services (CAS), non-production tiers, and DR/memory extensions. We reconcile what SAP says is in the bundle with what you actually run and list CAS items that can generate extra billing if not frozen in the renewal. We also model overlap if you are transitioning from perpetual. The negotiation memo normally carries renewal-uplift caps, early-exit math, scope-freeze language, and SLA credit triggers. That gives Procurement concrete items to trade when SAP pushes for longer terms or more users.

(05)

SAP

We work with SAP’s own constructs: Full Use Equivalents (FUEs), role catalogs, standard services vs Cloud Application Services (CAS), non-production tiers, and DR/memory extensions. We reconcile what SAP says is in the bundle with what you actually run and list CAS items that can generate extra billing if not frozen in the renewal. We also model overlap if you are transitioning from perpetual. The negotiation memo normally carries renewal-uplift caps, early-exit math, scope-freeze language, and SLA credit triggers. That gives Procurement concrete items to trade when SAP pushes for longer terms or more users.

(05)

SAP

We work with SAP’s own constructs: Full Use Equivalents (FUEs), role catalogs, standard services vs Cloud Application Services (CAS), non-production tiers, and DR/memory extensions. We reconcile what SAP says is in the bundle with what you actually run and list CAS items that can generate extra billing if not frozen in the renewal. We also model overlap if you are transitioning from perpetual. The negotiation memo normally carries renewal-uplift caps, early-exit math, scope-freeze language, and SLA credit triggers. That gives Procurement concrete items to trade when SAP pushes for longer terms or more users.

(06)

Salesforce

We read Oracle Universal Credits and match them with current consumption, then layer Oracle Support Rewards on top, using the official reward percentages so Finance can trace the math. We baseline egress (first 10 TB free, then per-region rates) and check whether traffic can be kept in-region via Service Gateway instead of hairpinning through NAT. We also look at your on-prem Oracle support bill to show the real, combined TCO. On the commercial side we push for credit-expiry protections, Marketplace within the allowed cap, clear treatment of Support Rewards in the order, and SLA/credit triggers.

(06)

Salesforce

We read Oracle Universal Credits and match them with current consumption, then layer Oracle Support Rewards on top, using the official reward percentages so Finance can trace the math. We baseline egress (first 10 TB free, then per-region rates) and check whether traffic can be kept in-region via Service Gateway instead of hairpinning through NAT. We also look at your on-prem Oracle support bill to show the real, combined TCO. On the commercial side we push for credit-expiry protections, Marketplace within the allowed cap, clear treatment of Support Rewards in the order, and SLA/credit triggers.

(06)

Salesforce

We read Oracle Universal Credits and match them with current consumption, then layer Oracle Support Rewards on top, using the official reward percentages so Finance can trace the math. We baseline egress (first 10 TB free, then per-region rates) and check whether traffic can be kept in-region via Service Gateway instead of hairpinning through NAT. We also look at your on-prem Oracle support bill to show the real, combined TCO. On the commercial side we push for credit-expiry protections, Marketplace within the allowed cap, clear treatment of Support Rewards in the order, and SLA/credit triggers.

(07)

ServiceNow

We use ServiceNow’s own vocabulary: subscription units (SUs), licensable CIs, ITOM nodes, Integration Hub transaction entitlements, and persona-based access. We almost always find Fulfiller sprawl and Integration Hub overuse caused by very chatty flows. The fix is half technical (batching, debounce, CI cleanup) and half commercial (reduce the IH tier, clean ITOM node counts before renewal, document compliance). In the negotiation kit we include uplift caps, table/CI governance, Impact/Success right-sizing, and true-down rights.

(07)

ServiceNow

We use ServiceNow’s own vocabulary: subscription units (SUs), licensable CIs, ITOM nodes, Integration Hub transaction entitlements, and persona-based access. We almost always find Fulfiller sprawl and Integration Hub overuse caused by very chatty flows. The fix is half technical (batching, debounce, CI cleanup) and half commercial (reduce the IH tier, clean ITOM node counts before renewal, document compliance). In the negotiation kit we include uplift caps, table/CI governance, Impact/Success right-sizing, and true-down rights.

(07)

ServiceNow

We use ServiceNow’s own vocabulary: subscription units (SUs), licensable CIs, ITOM nodes, Integration Hub transaction entitlements, and persona-based access. We almost always find Fulfiller sprawl and Integration Hub overuse caused by very chatty flows. The fix is half technical (batching, debounce, CI cleanup) and half commercial (reduce the IH tier, clean ITOM node counts before renewal, document compliance). In the negotiation kit we include uplift caps, table/CI governance, Impact/Success right-sizing, and true-down rights.

(08)

Adobe

We align to Adobe’s meters — profiles/events/MAU, server calls, message volumes, and sandbox/tenant limits — because that is what Adobe enforces. We look for runaway telemetry, identity bloat, and Marketo contact growth, then propose frequency caps, dedupe, and offload patterns before we talk price. Commercial requests usually include tier/price holds, temporary overage relief, and support-percentage optimization.

(08)

Adobe

We align to Adobe’s meters — profiles/events/MAU, server calls, message volumes, and sandbox/tenant limits — because that is what Adobe enforces. We look for runaway telemetry, identity bloat, and Marketo contact growth, then propose frequency caps, dedupe, and offload patterns before we talk price. Commercial requests usually include tier/price holds, temporary overage relief, and support-percentage optimization.

(08)

Adobe

We align to Adobe’s meters — profiles/events/MAU, server calls, message volumes, and sandbox/tenant limits — because that is what Adobe enforces. We look for runaway telemetry, identity bloat, and Marketo contact growth, then propose frequency caps, dedupe, and offload patterns before we talk price. Commercial requests usually include tier/price holds, temporary overage relief, and support-percentage optimization.

(09)

Workday

We treat Workday as worker-based / PEPM and rebuild the subscription math from your order forms and active worker counts. We model headcount drops, tenant sprawl (extra sandbox or preview tenants), and the value actually obtained from Success Plans. At renewal we go in with asks around uplift caps, module and tenant flexibility, and right-sized Success services tied to real adoption. This makes the numbers transparent for Finance and removes surprises in future cycles.

(09)

Workday

We treat Workday as worker-based / PEPM and rebuild the subscription math from your order forms and active worker counts. We model headcount drops, tenant sprawl (extra sandbox or preview tenants), and the value actually obtained from Success Plans. At renewal we go in with asks around uplift caps, module and tenant flexibility, and right-sized Success services tied to real adoption. This makes the numbers transparent for Finance and removes surprises in future cycles.

(09)

Workday

We treat Workday as worker-based / PEPM and rebuild the subscription math from your order forms and active worker counts. We model headcount drops, tenant sprawl (extra sandbox or preview tenants), and the value actually obtained from Success Plans. At renewal we go in with asks around uplift caps, module and tenant flexibility, and right-sized Success services tied to real adoption. This makes the numbers transparent for Finance and removes surprises in future cycles.