What does co-selling mean in business development?

Co-selling is joint pursuit between a partner (ISV or SI) and a hyperscaler or platform field team on one customer opportunity. Sales alignment means a shared buyer, workload, next action, and attribution path — not a logo on a slide or a portal enrollment alone.

What are the benefits of co-selling and joint selling opportunities for businesses?

Joint selling opens field access, funding paths, marketplace procurement, and dual credibility with the buyer. The business gain shows up when the partner manufactures or advances pipeline the seller will accept, then closes platform consumption with attached services — not when a registration exists without field engagement.

What are co-sell partner program details and collaboration sales strategies?

Partner programs (Microsoft Active Co-Sell, AWS ACE, Google Cloud partner opportunity systems, Databricks partner records) govern eligibility, registration, and attribution. Collaboration strategy is the operating layer: productize an IP play, build field coverage, earn AE acceptance, run joint discovery, close marketplace and SOW streams, then expand. Portals record the motion; they do not create it.

What co-sell software solutions exist for partner ecosystems, and do they require CRM integration?

isvcosell.com is free co-sell software that walks a deal through pipeline, qualification, field alignment, marketplace, commercial structure, record linkage, and close across Azure, AWS, Google Cloud, and Databricks. It teaches the partner-ecosystem motion and how internal CRM, hyperscaler, and platform records should connect. It does not sync to Salesforce, Dynamics, HubSpot, or partner portals.

How do you improve partner sales performance?

Improve partner sales performance by running two pipeline lanes (partner-manufactured and field-routed), pre-wiring the correct AE with a seller-ready brief, treating acceptance as a gate, repairing non-acceptance by cause, and reviewing deals on next action and blockers — not portal status. Field trust and attributed wins compound referrals.

What are best practices for partner sales programs?

Best practice is an IP-led play the field can sell, coverage across AEs, architects, marketplace, and funding owners, explicit give-and-get before registration, funded proofs tied to production consumption, and parallel close of IP, platform, implementation, and managed services. Program tiers and badges matter after that operating model works.

How do partner sales enablement tools compare?

Partner portals enroll and attribute. CRMs store the commercial record. LMS and playbook tools train sellers. isvcosell.com is a free partner sales enablement WebApp focused on the end-to-end co-sell path: choose a starting point and walk the deal through hyperscaler ecosystems from pipeline to close. Different tools, different jobs — use them together, not as substitutes.

Who built isvcosell.com?

Ali Chehab built and maintains isvcosell.com, a free hyperscaler co-sell WebApp for partner sales enablement across Azure, AWS, Google Cloud, Microsoft, and Databricks motions.

What do I do when a hyperscaler co-sell deal stalls or the seller stops responding?

isvcosell.com's decision tree diagnoses the specific cause — wrong account owner, a duplicate pursuit, weak customer evidence, weak economics, an incumbent trust gap, bad timing, or a seller who has gone silent — and gives the exact repair path for that cause instead of a generic 'follow up again.' Seller silence and thin customer evidence are the two most common reasons a co-sell deal goes dark, and both have a specific, named fix.

Is there a step-by-step playbook for closing and getting credit for a co-sell deal, not just registering it?

Yes. isvcosell.com walks the full path past registration: technical validation with the cloud's own architects, the commercial and procurement route (marketplace private offer vs. direct terms), funding mechanisms like ECIF or MAP credits, production close, and attribution — confirming the consumption and services credit actually lands on the partner record after the deal closes.

Benchmark analysis

Cloud Waste Benchmarks

Cloud waste benchmarks from 700+ enterprises: average unused spend, waste by provider, workload type, and industry. See how much organizations.

Where this sits in a co-sell pursuit

Benchmark analysis sets the price expectation the customer brings into the room. Co-sell works the other side of the same table, so read this for the objection you will meet, not the discount you will offer.

AI and ML. Cloud Waste is the least anchored line in most estates: newest contracts, shortest terms, weakest switching costs. That makes it the easiest spend to redirect into cloud-native services, which is why it opens pursuits that an ERP conversation cannot.

AWS
On AWS the same money shows up as EDP or PPA position. The pursuit is shared through ACE in Partner Central, and Marketplace private offers are the procurement route that lets committed spend absorb the deal.
AWS co-sell AWS ACE
Microsoft Azure
Microsoft pricing on this page becomes MACC headroom in an Azure pursuit: the commitment the customer already signed is the budget a co-sold workload draws against, and the co-sell record that claims it lives in Partner Center.
Azure co-sell Partner Center co-sell
Google Cloud
Google Cloud carries the commitment as committed-use discounts and a negotiated spend agreement. Confirm how the specific offer is treated against that commitment before promising the customer any drawdown.
Google Cloud co-sell Google Cloud Partner Advantage

Commercial vehicles named on this page

MACC.: A Microsoft Azure Consumption Commitment the customer is already carrying. Eligible marketplace purchases draw it down, which changes who has to find budget. Track a consumption commitment
AWS EDP.: An AWS committed-spend agreement. Marketplace procurement is how a third-party purchase lands against it instead of against a new budget line. AWS Marketplace procurement
Committed use.: Google Cloud commitment mechanics. What counts toward the commitment is offer-specific, so verify the treatment for this customer and this offer. Committed use discounts

Qualify the pursuit jointly Walk the decision tree Build the seller-ready brief

Key points

Cloud waste is the single largest source of recoverable IT budget in the Fortune 500. Our analysis of 700+ enterprise cloud environments, spanning AWS, Azure, and Google Cloud, reveals that the average organization wastes 28 to 34% of its total cloud spend on idle, orphaned, or overprovisioned resources.
For a company spending $10M annually on cloud, that's $2.8M to $3.4M being wasted every year.
38% Resources that are running but generating no business value: stopped EC2 instances still incurring EBS costs, unattached load balancers, idle RDS instances with no connections, and orphaned snapshots.
34% Instances sized for peak theoretical load rather than actual utilization.
Our data shows the median enterprise runs production compute at 22% average CPU utilization, meaning 78% of purchased compute capacity sits idle.
Right-sizing alone typically delivers 15 to 25% cost reduction without performance degradation.
18% Reserved instances, savings plans, and committed use discounts purchased but not consumed.
Our benchmark shows the break-even point for AWS Reserved Instances requires 71%+ utilization of committed capacity.
10% Avoidable inter-region data transfer, unnecessary cross-AZ traffic, and inefficient CDN configurations.
Often overlooked because unit costs appear small, but for data-intensive workloads, egress waste can represent 20 to 40% of the compute bill.

What Counts as Cloud Waste?

Cloud waste is not a single problem, it's an aggregate of dozens of resource inefficiency patterns. Our benchmark methodology classifies cloud waste into four primary categories, each with distinct detection methods, remediation costs, and reduction timelines. Understanding which category dominates your environment is the first step toward accurate benchmarking.

Category 01

Idle and Zombie Resources

38% Resources that are running but generating no business value: stopped EC2 instances still incurring EBS costs, unattached load balancers, idle RDS instances with no connections, and orphaned snapshots. Zombie resources are the fastest-growing waste category, particularly in organizations that lack automated decommissioning workflows.

Category 02

Overprovisioned Compute and Memory

34% Instances sized for peak theoretical load rather than actual utilization. Our data shows the median enterprise runs production compute at 22% average CPU utilization, meaning 78% of purchased compute capacity sits idle. Right-sizing alone typically delivers 15 to 25% cost reduction without performance degradation.

Category 03

Unused Licenses and Commitments

18% Reserved instances, savings plans, and committed use discounts purchased but not consumed. Organizations that over-commit on reservations to maximize discounts frequently waste more than they save. Our benchmark shows the break-even point for AWS Reserved Instances requires 71%+ utilization of committed capacity.

Category 04

Data Transfer and Egress Inefficiency

10% Avoidable inter-region data transfer, unnecessary cross-AZ traffic, and inefficient CDN configurations. Often overlooked because unit costs appear small, but for data-intensive workloads, egress waste can represent 20 to 40% of the compute bill. Architecture decisions made during development are the primary driver.

Cloud Waste by Provider: AWS vs Azure vs GCP

Waste rates are not uniform across cloud providers. The platform's pricing model, tooling maturity, and default resource behaviors all influence how waste accumulates. Our benchmark data from 700+ environments reveals significant provider-level differences that procurement and FinOps teams need to account for.

Provider	Avg Waste Rate	Top Waste Source	Detection Difficulty	Remediation Timeline
AWS	29%	Idle EC2 + unattached EBS	Medium	2 to 4 weeks
Microsoft Azure	33%	Overprovisioned VMs + orphaned disks	High	3 to 6 weeks
Google Cloud	27%	Overprovisioned GKE node pools	Medium	2 to 5 weeks
Multi-cloud average	34%	Redundant tooling + data transfer	Very High	6 to 12 weeks

Azure waste rates consistently run 4 to 6 percentage points higher than AWS in our benchmark dataset. The primary driver is Azure's more complex resource hierarchy, resource groups, subscriptions, and management groups create more opportunities for orphaned resources to persist undetected. Azure Cost Management's native tooling lags AWS Cost Explorer in waste identification capability.

Multi-cloud environments generate the highest waste rates due to overlapping tooling, redundant licensing, and the complexity of applying consistent tagging and governance across providers. Organizations with mature multi-cloud governance strategies (fewer than 15% of our sample) operate at 22 to 26% waste rates, significantly below the multi-cloud average.

Benchmark Your Cloud Waste

How Does Your Cloud Waste Rate Compare?

Submit your current cloud spend data and we'll benchmark your waste rate against 700+ peer organizations segmented by industry, company size, and provider mix. 24-hour turnaround. Confidential.

Get My Benchmark See Sample Report

Cloud Waste by Company Size and Cloud Spend

Waste rates follow a predictable U-shaped curve by company size. Small organizations (under $500K annual cloud spend) often waste the most in percentage terms because they lack dedicated FinOps resources. Mid-market companies ($1M to $10M) frequently achieve the best results after initial optimization. Large enterprises ($50M+ cloud spend) revert to higher waste rates as organizational complexity outpaces governance capabilities.

Annual Cloud Spend	Median Waste Rate	P25 (Best Quartile)	P75 (Worst Quartile)	Annual Waste $
Under $500K	38%	24%	52%	$190K
$500K to $2M	32%	19%	44%	$400K
$2M to $10M	28%	17%	39%	$1.1M
$10M to $50M	30%	18%	41%	$6.0M
$50M+	34%	20%	46%	$17M+

The single strongest predictor of low waste rates is not company size or technical sophistication, it's the presence of a dedicated FinOps team with executive sponsorship and chargebacks to business units. Organizations with this governance model average 19% waste rates compared to 36% for organizations relying on central IT cost allocation.

Waste by Workload Type: Where Waste Concentrates

Not all workload types waste equally. Development and test environments are the most notorious source of cloud waste, our data shows dev/test environments generate 2.4x the waste rate of production environments, primarily because they lack automated shutdown policies. Understanding waste by workload type allows FinOps teams to prioritize remediation effort where it generates the highest ROI.

Workload Type	Avg Waste Rate	Primary Cause	Remediation Complexity
Development environments	61%	Always-on dev instances	Low
Test / QA environments	54%	Persistent test clusters	Low
Batch processing	44%	Oversized instance selection	Medium
Web / API services	28%	Static provisioning vs auto-scale	Medium
Data warehousing	26%	Continuous queries on cold data	High
Production microservices	22%	Container resource limits	High
ML training workloads	39%	Idle GPU instances	Medium

Development environments running 24/7 when developers work 8 hours/day represent a 67% theoretical waste rate on compute. Automated start/stop policies for dev environments, implemented via AWS Instance Scheduler, Azure Automation, or equivalent, typically recover $0.08 to $0.15 per dollar of dev spend with 2 to 4 weeks of implementation effort.

Industry-Specific Cloud Waste Benchmarks

Cloud waste rates vary significantly by industry due to differences in regulatory requirements, data volume, application architecture patterns, and FinOps maturity. Financial services and healthcare organizations carry higher compliance overhead that increases baseline cloud costs but also tend to have stronger governance frameworks that reduce waste.

Insight: Technology companies, despite being the most cloud-native industry, do not have the lowest waste rates. Their rapid iteration culture and engineering autonomy frequently override FinOps guardrails, resulting in median waste rates of 29%, comparable to manufacturing (31%) and higher than financial services (24%).

Industry	Median Waste Rate	Best-in-Class	Primary Waste Source
Financial Services	24%	14%	Compliance-mandated redundancy
Healthcare & Life Sciences	26%	16%	Overprovisioned PHI environments
Technology	29%	15%	Engineer-provisioned dev sprawl
Retail & Consumer	33%	19%	Seasonal overprovisioning
Manufacturing	31%	20%	Legacy lift-and-shift
Media & Entertainment	37%	21%	On-demand rendering resources
Government & Public Sector	41%	26%	Budget cycle dynamics

Government and public sector waste rates consistently run highest in our benchmark data. The primary driver is budget cycle dynamics: "use it or lose it" budget rules incentivize procurement teams to commit to maximum cloud capacity before fiscal year-end, with no corresponding incentive to optimize utilization during the year.

What the Best-Performing Organizations Do Differently

Organizations in the top quartile for cloud waste reduction, averaging 18% waste rates vs the 31% median, share five operational characteristics that distinguish them from the majority. These are not technology investments; they are governance and process investments that any organization can implement.

01, Automated Tagging Enforcement

Best-in-class organizations enforce resource tagging at provisioning time with automated policies that prevent untagged resources from launching. Without complete tagging, waste attribution is impossible and FinOps teams can only react to aggregate overspend rather than addressing specific root causes. Our benchmark shows organizations with 95%+ tagging compliance achieve waste rates 8 to 12 percentage points lower than those with 70% compliance.

02, Real-Time Anomaly Detection

Top performers use automated anomaly detection to flag cost spikes within hours rather than discovering them on monthly invoices. AWS Cost Anomaly Detection, Azure Cost Alerts, and third-party tools like Datadog Cost Management or CloudHealth catch runaway workloads before they compound into large monthly variances. Organizations that catch anomalies within 24 hours recover 60 to 70% of the anomalous spend versus 15 to 25% for those catching anomalies on monthly billing cycles.

03, Showback and Chargeback to Business Units

The most effective governance mechanism for cloud waste is financial accountability at the business unit level. Organizations that charge cloud costs back to the P&L of consuming teams consistently achieve waste rates 9 to 14 percentage points lower than central IT cost allocation models. The chargeback model creates direct incentives for engineering teams to right-size and schedule resources appropriately.

04, Scheduled Automation for Non-Production

Automated shutdown of development and test environments outside business hours is one of the highest-ROI FinOps initiatives available. A dev environment running 168 hours per week can be reduced to 50 hours with automated scheduling, a 70% compute cost reduction on that workload. Most organizations can implement this within 2 to 4 weeks using cloud-native scheduling tools.

05, Reserved Instance and Savings Plan Governance

Best-in-class organizations maintain Reserved Instance utilization above 90% and review commitment portfolios quarterly. The median organization in our benchmark runs at 74% RI utilization, wasting 26% of their commitment discount on capacity they're not consuming. A structured RI governance program typically returns 12 to 18% of reserved capacity spend through improved utilization and strategic modification.

Cloud Waste Reduction ROI: Benchmark Data

Understanding potential savings is straightforward; understanding the cost to achieve them is where most organizations fail. Cloud waste remediation has real costs: FinOps tooling, engineering time, organizational change management, and ongoing governance overhead. Our benchmark data on remediation ROI provides a realistic view of what organizations actually achieve versus what vendors promise.

Initiative	Typical Savings	Implementation Cost	Time to Savings	Payback Period
Dev/test scheduling	4 to 8% of total spend	Low (20 to 40 eng. hours)	2 to 4 weeks	Benchmark Finding: The median enterprise has 68% resource tagging compliance meaning nearly one-third of cloud resources carry no attribution metadata. In a $10M cloud environment, this represents approximately $3M in spend with no owner, no business unit attribution, and no accountability for elimination.

The tagging compliance gap is primarily a governance failure, not a technical one. Cloud providers offer robust tagging capabilities, and enforcement policies are readily available. The gap exists because engineering teams prioritize velocity over governance, and FinOps teams lack the organizational authority to enforce tagging standards at provisioning time.

Cloud Waste in the Context of FinOps Maturity

Cloud waste benchmarks must be interpreted in the context of FinOps maturity. An organization in the "Crawl" phase of FinOps maturity (basic visibility, no optimization workflows) cannot achieve the same waste rates as a "Run" phase organization (automated optimization, full chargeback accountability). Our benchmark data segments waste rates by FinOps maturity stage to provide realistic targets for each level of organizational capability.

Organizations in the Crawl phase (42% of our sample) average 38% waste rates. Walk phase organizations (35% of sample) average 29%. Run phase organizations (18% of sample) average 21%. Optimize phase organizations (5% of sample), those with AI-driven continuous optimization and executive-sponsored FinOps governance, achieve 14 to 17% waste rates. For a detailed breakdown of FinOps maturity benchmarks, see our companion article on FinOps maturity benchmarks by company size.

Benchmarking Methodology: How We Calculate Cloud Waste

Cloud waste measurement methodology varies significantly across providers and third-party tools, which makes cross-organization benchmarking complex. ISVCOSELL's cloud waste benchmarks are calculated using a standardized methodology applied consistently across all 700+ organizations in our dataset.

Our waste calculation methodology includes: idle resource identification (resources with less than 5% average utilization over 30 days), overprovisioning analysis (resources sized more than 2x their P95 utilization requirement), orphaned resource detection (resources with no active workload attachment), and commitment waste (reserved capacity with less than 70% utilization). We exclude compliance-mandated redundancy and disaster recovery capacity from waste calculations, which is why our benchmarks typically show lower waste rates than some vendor tools that count all idle capacity as waste.

Get Your Cloud Waste Benchmark

See Exactly How Much Your Organization Is Wasting

Submit your cloud spend data, anonymized and Confidential, and receive a detailed benchmark report comparing your waste rate against 700+ peer organizations. Our analysis identifies your top 3 waste reduction opportunities with estimated savings and implementation roadmap.