Understanding Ecosystem Strategy: Why Partner-Led Beats Direct Sales

Microsoft generates roughly $8 in partner revenue for every $1 of its own revenue—a 1:8 ratio that translates to over $1 trillion in annual partner ecosystem value. Salesforce's ratio is about 1:6. AWS sits around 1:5. Why not bring those capabilities in-house and capture the revenue directly? Why give partners ~80% of the value when you could keep it all? The answer reveals something fundamental about how software businesses scale—how you can make more doing less and why the companies that figured this out early went on to dominate enterprise technology.

Chapter 1: The Paradox of Scale

Software companies face a choice: own every customer interaction or multiply reach through others. For decades, the assumption was control equals efficiency. Build sales teams, layer in management, own the relationship. Revenue grows with headcount. Add reps, add pipeline, add quota.

The problem typically shows up around $100M in ARR. Growth starts requiring more input for the same output. You're hiring 100 reps to grow 20% instead of 50%. CAC climbs while deal sizes stagnate. The sales org becomes a cost center that happens to generate revenue, not an engine that compounds it.

Direct sales hits a ceiling not because it stops working, but because it stops scaling efficiently. Three things degrade simultaneously:

Early reps sold to customers with urgent pain and fast decision cycles. As markets mature, reps work harder for the same result. They're calling on laggards, fighting procurement, dealing with longer cycles. Conversion drops. To maintain growth, quotas rise. A rep who carried $1M now carries $1.5M. Top performers hit it, but the middle struggles. Turnover spikes. At any moment, 30-40% of your team is ramping.

Meanwhile, CAC spirals. Inbound demand dries up. You shift to outbound—more SDRs, more ads, more events. Each marginal customer costs more because you've captured the low-hanging fruit. Competitors flood in with similar products. Differentiation fades. Discounting becomes standard.

The breaking point: growth decelerates but headcount costs don't. You're adding reps to maintain percentage growth, not accelerate it. Investors start asking why your magic number keeps dropping. The answer is always structural—direct sales adds capacity, it doesn't multiply it.

Partner ecosystems invert this. Instead of hiring reps, you activate organizations that already have distribution, credibility, and customer relationships. A systems integrator doesn't just represent one quota—they represent dozens of customer touchpoints, existing trust networks, and complementary services. When you enable 50 partners, you're not adding 50 reps; you're adding 50 companies with their own teams, practices, and install bases.

The exponential part isn't just volume—it's compounding. A direct rep closes a deal and moves on. An SI closes a deal, then brings in five more customers where they're already doing infrastructure work. An ISV integration creates a permanent channel into every account using that ISV. A marketplace listing runs 24/7 with zero incremental cost. Each relationship doesn't just add capacity; it multiplies surface area.

This is the paradox: the thing that feels secure (control) caps your ceiling. The thing that feels risky (partner autonomy) unlocks compounding growth.

Chapter 2: The Evolution of Leverage: Six Eras

Partner strategy has been solving distribution bottlenecks for forty years. Each era introduced a new form of leverage that stacked on top of the previous ones.

1980s-90s: Geographic Bottleneck → Logistical Leverage

IBM, Compaq, and Cisco couldn't afford global presence. VARs handled physical distribution—warehousing, regional delivery, local support. Partners absorbed infrastructure cost. Cisco pioneered the certification model: tiered programs (Silver, Gold, Premier) based on technical training and sales performance. Partners invested in Cisco certifications because businesses demanded them. Cisco engineered demand for its own ecosystem.

1990s-2000s: Implementation Bottleneck → Delivery Leverage

Oracle, SAP, and Microsoft shifted to software licensing. Complex enterprise deployments required armies of consultants. System integrators—Accenture, Deloitte, IBM Global Services—became implementation engines. Vendors licensed software; partners handled deployment. Professional services became a channel multiplier. Gross margins stayed high; delivery capacity became infinite.

Mid-2000s: Innovation Bottleneck → Platform Leverage

Salesforce AppExchange (2005) changed the model. Partners weren't reselling or implementing—they were building on top of the platform. Every new app expanded functionality and attracted more customers. Innovation became distributed. ISVs built features Salesforce didn't have to. The platform became more valuable with every app, and apps brought customers who had to buy Salesforce to use them.

2010s: Procurement Bottleneck → Transaction Leverage

AWS Marketplace (2012) and Azure Marketplace flipped the channel model. Cloud vendors didn't need partners to resell—there was nothing physical. They needed partners to drive consumption. Marketplaces handled procurement friction while partners influenced workloads. Customers used committed cloud spend to buy third-party software. The marketplace turned procurement into pipeline.

Mid-2010s: Integration Bottleneck → Interoperability Leverage

Slack, GitLab, Stripe, and Twilio grew through API-first ecosystems. Being everywhere became the distribution strategy. Each integration created another route in. Developers using Jira saw GitLab as an option for source control. Developers using Kubernetes saw GitLab for CI/CD. The product became embedded in workflows, not sold through campaigns.

Late 2010s-Present: Enterprise Bottleneck → Influence Leverage

AWS Co-Sell, Microsoft MACC (Azure Consumption Commitment), and Google Cloud co-sell programs turned ISVs into enterprise growth engines. ISVs align with hyperscalers to get pulled into massive deals. GSIs like Accenture and Deloitte became kingmakers for cloud migrations, riding existing trust and billion-dollar spend commitments.

Each era didn't replace the last—it layered on top. Snowflake today uses VARs, SIs, marketplace presence, integrations, ISV partners, and hyperscaler co-sell simultaneously. Modern leverage is multiplicative, not additive.

Chapter 3: The Four Levers

Every ecosystem combines four core levers in different proportions based on product, market, and maturity.

Distribution – Geographic and Vertical Reach

Partners solve the coverage problem. Cisco used VARs to reach customers in markets where they couldn't afford presence. Those VARs had local relationships and procurement contracts. Cisco's job wasn't to cold-call businesses in Tokyo—it was to make sure local partners prioritized Cisco over competitors.

This extends beyond geography into verticals. A cybersecurity vendor can't hire specialists in healthcare, financial services, manufacturing, and retail. But Deloitte already has practices in all four. When Deloitte standardizes on a vendor for security audits, that vendor enters dozens of verticals without hiring industry experts.

Execution – Deployment at Scale

Partners decouple selling from delivering. Oracle, SAP, and Workday built empires by licensing software and letting Accenture, PwC, and Capgemini handle implementation. The vendor didn't need 10,000 consultants—just partner certification and quality control. Gross margins stayed high. Delivery capacity became infinite.

This works in cloud, too. AWS and Azure grew by letting consulting partners handle migrations. Moving a legacy data center to cloud is a six-month, $5M engagement. Cloud vendors can't staff that at scale. Instead, they train partners like Slalom and Cloudreach to lead migrations, then reward them through funding and co-sell incentives. Every migration creates long-term consumption.

Demand – Pipeline Generation

Partner-sourced pipeline is fundamentally different from direct-sourced. It's warmer, faster, and often pre-qualified because it comes through a trusted relationship.

Referral partners act as multipliers. A financial services consultancy refers clients to Snowflake because they need data warehousing for analytics projects. Those referrals close faster because the consultant remains involved post-sale. Snowflake didn't run ads to get that deal—the partner handed them the introduction.

Integrations create passive demand. When GitLab integrates with Jira, Slack, and Terraform, every user of those tools becomes a potential GitLab user. The integration isn't just a feature—it's a discovery mechanism. Developers see GitLab in existing workflows and adopt it because it's already there.

Co-marketing extends brand reach without extending budget. When Databricks co-hosts a webinar with AWS, both share the audience and leads. Databricks gets access to AWS's enterprise base. AWS gets credit for AI/ML thought leadership. Cost per lead drops because it's split; quality goes up because AWS's endorsement carries weight.

Trust – Credibility Transfer

In security, healthcare, financial services, and government, buying decisions aren't about features—they're about confidence. Trust collapses sales cycles and overcomes skepticism.

CrowdStrike didn't try to outspend Symantec on brand marketing. They armed MSSPs and incident response firms with their technology. When a company gets breached and calls in a trusted security partner, that partner deploys CrowdStrike. The customer wasn't evaluating five vendors—they were trusting an advisor who already made the choice.

GSIs carry trust at scale. When Accenture recommends Workday over Oracle, it's not just a product endorsement—it's a relationship endorsement. Accenture has billion-dollar contracts and decades of history with these customers. Their recommendation carries weight no direct sales rep can replicate.

These levers aren't mutually exclusive—they're multiplicative. Snowflake uses distribution (global SIs), execution (partner-led deployments), demand (marketplace and ISV integrations), and trust (GSI endorsements) simultaneously. Each lever amplifies the others.

Chapter 4: The Hidden Economics

The traditional objection to partner models is margin erosion: "Why give away 20-30% when we could keep it all by selling direct?" This looks at the wrong variable. The real math isn't margin per deal—it's efficiency per dollar of go-to-market investment.

CAC Inversion

A direct sales rep costs $150K all-in and carries a $1M quota. If they hit 100% attainment, CAC is 15% of first-year revenue. But reality is messier: ramp time, turnover, quota attainment variance, and pipeline generation costs push true CAC to 25-40% for most SaaS companies.

A partner-influenced deal looks expensive—you're paying 20-30% in incentives. But you're not paying recruiter fees, six months of ramp, base salary during unproductive quarters, SDR costs, or management overhead. Your sales force becomes variable cost, not fixed cost. You're paying for performance, not payroll.

Partner-sourced deals often close faster because they come pre-qualified. A systems integrator isn't introducing you to a random prospect—they're bringing a client with an active project, approved budget, and trusted relationship. These deals skip early-stage nurturing and convert at higher rates. When you factor in velocity and conversion, partner CAC is often half of direct CAC.

LTV Multiplication

Partner-led growth doesn't just lower acquisition cost—it increases lifetime value.

Partners drive consumption. In cloud and usage-based models, landing the account is only the beginning. Growth comes from expansion—more workloads, more users, more features. Partners who deploy and manage the technology continuously discover new use cases. A consulting firm migrating a customer to AWS doesn't stop at the initial workload—they bring over additional apps, spin up dev/test environments, implement disaster recovery. Each addition increases monthly spend.

Partners reduce churn through stickiness. When a systems integrator builds a customer's entire data pipeline on Snowflake, switching becomes a multi-million-dollar re-implementation. The partner has invested thousands of hours configuring, optimizing, and training. They're not going to rip that out for a 15% price difference.

Partners extend into adjacent demand. A customer buys Salesforce for CRM. A partner integrates it with marketing automation, customer service, and analytics. Suddenly the customer is using five Salesforce clouds instead of one. The original deal was $50K/year; three years later it's $300K/year. That expansion didn't require the sales team—it required a partner who saw the full picture.

The margin trade-off is a mirage. Yes, you're paying 20-30% in partner incentives. But you're getting 50% lower CAC, 2-3x higher LTV, infinite capacity, global reach, and faster time-to-close.

A $100M direct sales organization might generate $500M in ARR with 30% gross sales and marketing costs, high churn, and limited expansion. That same $100M invested in partner enablement—co-sell programs, MDF, training, incentives—can influence $2B in pipeline with 15% blended CAC, lower churn, and continuous expansion.

The hidden math: margin per deal is a vanity metric. What matters is total addressable growth per dollar invested in go-to-market. In that calculation, partners win.

Chapter 5: Where Ecosystems Fail

Most partner programs fail in predictable ways. The failure modes are structural and often self-inflicted.

Channel Conflict

You build a partner program, recruit partners, train them—then your direct sales team undercuts them. A partner brings a deal to 30%, spends weeks building the relationship, and your AE swoops in, claims the opportunity, and closes it direct. The partner gets scrubbed out of the CRM. They make nothing. They never bring you another deal.

This happens because incentives aren't aligned. Sales leadership is measured on direct bookings. Reps are comp'd on closed-won revenue regardless of source. Partner-sourced deals still count as "their" pipeline. Why share credit?

The downstream damage is severe. Partners stop investing. They de-prioritize your product in favor of vendors who respect deal registration. Your ecosystem program becomes a ghost town—partners stay in for marketing benefits but never actually sell.

The fix requires structural change: deal registration must be binding and enforced. Partner-sourced deals must comp differently for direct reps. Sales leadership must be measured on total influenced revenue, not just direct. Some companies separate quota entirely—partner-sourced vs. direct—or use dedicated partner AEs who don't carry a direct number.

Over-Engineering Control

Vendors love control. They want visibility into every deal. Partners must forecast accurately, submit opportunity registrations, co-sell with reps, use approved messaging, report on deployments, and attend quarterly business reviews. The intent is alignment. The execution kills leverage.

Partners run their own businesses. They have 10-20 vendor relationships, hundreds of customers, and limited time. If working with you requires three approval layers and two weeks of back-and-forth just to register a deal, you become low-priority. They'll work with vendors who make it easy: instant deal registration, self-service MDF, automated rebates, minimal red tape.

Over-control comes from applying direct-sales thinking to ecosystems. Direct sales teams need tight process because they're managing their own capacity. Ecosystems work by distributing capacity—you give up process control in exchange for scale.

The symptoms: long approval cycles, complex co-sell requirements partners ignore, extensive reporting demands partners don't fulfill, low engagement despite high recruitment. Partners treat you as a checkbox vendor—in the program but never prioritized.

The fix: simplify everything. Make deal registration instant. Make MDF self-service. Make rebates automated. Reduce co-sell requirements to optional. Measure partners on outcomes (revenue, consumption, retention), not process compliance. Trust scales. Micromanagement doesn't.

Misaligned Incentives

Partners optimize for whatever you pay them to optimize for. Design incentives poorly and they'll game the system.

Deal registration abuse: partners claim credit for deals they didn't influence. They see a prospect in your CRM, register it, and collect a referral fee even though the customer was already in late-stage discussions.

Margin extraction: partners force themselves into deals just to capture margin. The customer was buying direct, but the partner inserts themselves at the last minute, adds no value, and walks away with 20%.

Lazy co-sell: partner wants credit but does minimal work. They introduce you, disappear, and expect your team to close it while they collect 30% of economics.

Service dumping: partners sell your product but deliver terrible implementations. They underbid services to win the deal, cut corners on deployment, leave the customer frustrated. Customer churns within a year.

The fix requires incentive redesign: tier deal registration (full credit for partner-sourced, partial for influenced, none for late-stage claims). Reward outcomes, not activity—pay rebates based on consumption growth or retention. Quality-gate partners through certification requirements or customer satisfaction scores. Sunset non-performers.

Wrong Leverage Type

Not every partner motion fits every business model. Trying to force the wrong type creates friction, not scale.

Consumption-based SaaS running a VAR resale model: your product is cloud-delivered, usage-based, sold on annual contracts. But you're recruiting VARs who expect to buy inventory and mark it up. There's no inventory. There's no discount structure that makes sense. VARs don't have a business model around recurring subscriptions.

Horizontal platform using vertical-specific SIs: you built a general-purpose platform and you're trying to scale through systems integrators who specialize in healthcare or financial services. Those SIs build practices around compliance and vertical workflows. Your horizontal platform doesn't fit their vertical go-to-market.

Enterprise software with community/open-source motion: you're selling $500K deals to Global 2000, but trying to build an ecosystem of open-source contributors and small dev shops. The economics don't work. Open-source partners want free tools and community credits. Enterprise buyers want white-glove service and security audits. You're managing two incompatible ecosystems.

The fix: match leverage type to business model. If you're usage-based, focus on partners who drive consumption (SIs, MSPs, ISVs). If you're vertical-focused, build deep partnerships with vertical specialists, not generalists. If you're developer-first, invest in integrations and community—not traditional channel programs.

Chapter 6: Playbooks Across Technologies

Each technology category evolved its own ecosystem playbook based on product characteristics and distribution bottlenecks.

Hardware & Networking – Cisco

Networking gear is complex, regionally distributed, requires hands-on installation. Businesses needed local integrators who could design, deploy, and support infrastructure.

Cisco's playbook layered three types: logistical leverage (VARs handled distribution and warehousing), delivery leverage (VARs configured equipment and provided support), trust leverage (local IT consultants had existing relationships).

The genius was the certification model. Cisco created tiered partner programs based on technical certifications and sales performance. Partners invested in training engineers at their own expense because Cisco certification became a competitive differentiator. Businesses asked for "Cisco Certified" partners. Cisco engineered demand for its own ecosystem.

Infrastructure & Cloud – AWS

AWS inverted the channel model because there was no physical product and no complex on-prem deployment. Customers self-provisioned with a credit card. The bottleneck wasn't distribution—it was consumption.

AWS combined three levers: execution leverage (consulting partners handled cloud migrations), transaction leverage (AWS Marketplace became a procurement channel where ISVs listed software and customers bought using committed spend), influence leverage (AWS Partner Network created tiers based on customer success—partners earned rebates based on how much usage they influenced, not just deals registered).

The flywheel: more partners → more migrations → more workloads → higher lock-in → more partner demand. AWS engineered an ecosystem where growth fed growth.

DevOps & Platform – GitLab

GitLab's ecosystem was radically different: the integrations are the ecosystem. GitLab didn't build a traditional partner program with VARs and SIs. They turned the product into connective tissue that plugged into everything developers already used—Jira, Slack, Terraform, Kubernetes.

This was pure interoperability leverage. Every integration created a distribution channel. Developers using Jira for project management saw GitLab as an option for source control. The product became embedded in workflows, not sold through campaigns.

The open-source model amplified this. GitLab's community contributed integrations and extensions. Thousands of developers built on top of GitLab, expanding functionality without GitLab hiring product teams. The ecosystem wasn't partners selling GitLab—it was partners extending GitLab, which made the product more valuable, which attracted more users.

Cybersecurity – CrowdStrike

In cybersecurity, buyers don't optimize for features—they optimize for confidence. A breach is a career-ending event. CISOs choose vendors they trust, often on the recommendation of advisors who've proven themselves in crisis.

CrowdStrike's playbook exploited this through trust leverage: they armed MSSPs and incident response firms with their platform. When a company got breached and called in a trusted IR partner, that partner deployed CrowdStrike. The customer wasn't evaluating five vendors—they were trusting the expert in the room.

MSSPs ran CrowdStrike 24/7 in customer environments. The product became operationally embedded—switching would require retraining SOC teams, rewriting playbooks, migrating telemetry. The moat wasn't code; it was operational inertia.

GSI endorsements extended this. CrowdStrike partnered with Deloitte, Accenture, and Booz Allen Hamilton. These firms had billion-dollar contracts with Global 2000 and government. When they standardized on CrowdStrike for security practices, they brought CrowdStrike into accounts that would have taken years to penetrate directly.

Data & AI – Snowflake

Snowflake architected the product to create gravitational pull through partner integrations. Data platforms are only valuable if they connect to everything else: data sources, transformation tools, BI platforms, AI/ML frameworks.

The playbook combined four types: platform leverage (Snowflake Marketplace let data providers and analytics tools list integrations—each new listing made Snowflake more valuable), execution leverage (SIs built data engineering practices on Snowflake and handled migrations), interoperability leverage (Snowflake integrated with dbt, Tableau, Databricks, AWS, Azure, GCP—being multi-cloud meant fitting into any stack), influence leverage (partner program rewarded consumption growth—SIs earned rebates based on customer usage).

The gravity effect: more data in Snowflake → more tools integrate → more use cases → more customers → more partners build practices → more data in Snowflake. The ecosystem became self-reinforcing.

Enterprise Applications – Salesforce

Salesforce didn't just build an ecosystem—they built an economy. AppExchange (2005) turned partners into product creators, not resellers. ISVs built apps on Salesforce's platform, sold them to Salesforce customers, kept majority of revenue. Salesforce took a commission but gained something more valuable: network effects.

The playbook used platform leverage to create compounding: expand functionality without building (every app extended Salesforce's capabilities), attract new customers (apps brought customers to Salesforce—a company using a niche app built on Salesforce had to buy Salesforce), lock in existing customers (the more apps installed, the harder to leave), create partner revenue streams (ISVs made money selling apps, SIs made money implementing—both had economic incentives to grow Salesforce's footprint).

Salesforce became a platform, not a product. The CRM was the foundation, but the ecosystem was the moat. Competitors could copy features. They couldn't copy an economy of 10,000 partners with revenue tied to Salesforce's success.

Chapter 7: Distribution as Moat

Product advantage fades. Distribution advantage compounds. A better feature can be copied in six months. A network of 10,000 certified partners, integrated into every adjacent platform, embedded in every major enterprise—that takes a decade to replicate.

Product differentiation is compressing across every category. AI accelerates feature parity—what took 18 months now takes six. Open-source commoditizes infrastructure. Cloud platforms abstract away technical complexity. Product alone doesn't create defensibility anymore. Distribution does.

A startup can clone your features, but they can't clone your ecosystem. They can match your technology, but they can't match your integrations. They can undercut your price, but they can't undercut the trust a GSI has built with clients over 20 years. Distribution has always been a competitive advantage. It's becoming the competitive advantage—because it's the only one that takes time to build and can't be shortcut with capital or talent.

The shift from product-led to ecosystem-led:

Product-led growth dominated the last decade. Build a great product, make it self-serve, let users discover value. Slack, Zoom, Figma grew through bottoms-up adoption, not top-down sales. The advantage was speed: no sales cycles, no enterprise bureaucracy, viral loops.

But PLG has a ceiling. It works for tools where individual users can adopt without permission. It struggles in complex, multi-stakeholder, compliance-heavy environments. At scale, PLG companies need traditional distribution—and they're retrofitting partner programs onto self-serve models.

The future is ecosystem-led growth — products designed from day one to be distributed through partners, not just by partners.

API-first architecture: products built to integrate, not just function standalone. Every feature is an API. Every workflow is embeddable. Distribution happens through connective tissue—ISVs plug you into their apps, SIs integrate you into their stacks, platforms list you in their marketplaces.

Partner data as product input: ecosystems generate signal. Partners see customer behavior and adoption patterns across hundreds of deployments. Companies that treat partner data as a product input iterate faster and build more relevant features.

Marketplace-native selling: the future of B2B procurement is marketplace transactions. AWS, Azure, GCP, Salesforce AppExchange, Snowflake Marketplace—buyers prefer consolidating spend with existing vendors. If you're not listed, you're not considered. Marketplace presence is table stakes.

Co-sell as core motion: the best enterprise deals aren't won by direct reps—they're won by alliances. AWS co-sell puts ISVs in front of AWS's enterprise team, who bring them into cloud migration deals. Microsoft MACC lets customers use existing Azure budgets to buy third-party software. If you're not aligned with hyperscalers and GSIs, you're not in the room when enterprises make buying decisions.

The strategic question every company should ask: if we were starting today, knowing what we know about buying behavior, distribution channels, and capital efficiency, would we build a 500-person direct sales team or a 50-partner ecosystem?

For most B2B software companies, the honest answer is ecosystem. The problem is legacy: they've already built the direct team, and unwinding it is politically and operationally painful. But new companies don't have that constraint. They can design for leverage from day one.

In Summary

Direct sales is arithmetic. Ecosystems are exponential.

Every era of partner strategy solved a bottleneck that direct sales couldn't. When software was physical, partners solved logistics. When deployments were complex, partners solved delivery. When platforms emerged, partners solved innovation. When cloud arrived, partners solved consumption. When integration became ubiquitous, partners solved connectivity. When enterprises consolidated, partners solved influence.

Each bottleneck was structural, not tactical. You couldn't brute-force past it with more headcount. You needed a different go-to-market architecture. The companies that recognized this early built leverage into their DNA. The companies that tried to optimize direct sales ran into diminishing returns.

Cisco didn't outspend competitors on sales teams—they outscaled them through certified VARs. Salesforce didn't build every feature—they let ISVs build on AppExchange. AWS didn't hire 10,000 migration consultants—they enabled partners to lead transitions. Snowflake didn't fight for every integration—they built a marketplace where partners did it. CrowdStrike didn't convince every CISO one-by-one—they armed trusted advisors who brought them into accounts.

These companies understood that leverage isn't about doing less—it's about multiplying impact through others. The best ecosystems don't replace effort; they amplify it. A single dollar invested in partner enablement can influence 10-20x that amount in pipeline. A single integration can drive thousands of adoptions. A single GSI endorsement can open doors that would take years of direct selling to crack.

The companies that design for leverage, not control, build the most defensible growth engines in modern software.

Because while features can be copied, ecosystems can't. A competitor can reverse-engineer your product, hire away your engineers, undercut your price. But they can't replicate 10 years of partner relationships, thousands of integrations, and trust networks built with GSIs and hyperscalers. Ecosystem advantage is the ultimate moat—it takes time, it compounds, and it's nearly impossible to shortcut.

The winners in the next decade won't be the companies with the best product at launch. They'll be the companies with the best distribution network five years later. They'll be the ones who layered multiple leverage types—distribution, execution, demand, trust—into a compounding flywheel. They'll be the ones who treated go-to-market as architecture, not tactics.

Leverage always wins. Not because it's easier—building an ecosystem is harder than hiring reps. But because it creates compounding returns that direct sales never can. Every partner enabled makes the next one more valuable. Every integration completed makes the next one easier. Every customer deployed creates switching costs for the next ten.

That’s why partner-led beats direct sales.