the rise of ecosystem led product growth
how integrations, platforms, and ai driven workflows are becoming the real growth engine for modern startups
this article breaks down how ecosystem led product growth works in practice, why it is accelerating in the ai era, and how the fastest growing startups are using integrations, marketplaces, and partner platforms as their primary distribution channel. instead of competing for attention, these companies are embedding themselves into workflows, creating compounding growth loops that traditional marketing can no longer match.
the result is a new kind of growth strategy where software scales through networks, not campaigns, and where being connected matters more than being discovered.
the collapse of traditional growth math
for most of the last two decades, software growth was governed by a relatively stable equation. products acquired users through a small set of scalable channels, primarily paid advertising, search engine optimization, and outbound sales, and those users converted, retained, and expanded in ways that could be modeled with reasonable accuracy. founders could forecast growth by increasing spend, adding content, or expanding sales teams, and the primary constraint was execution, not channel viability.
that equation has now structurally broken, especially in markets where product market fit is getting harder in the ai era.
the first pressure comes from supply. the cost of producing software has collapsed due to cloud infrastructure, open source libraries, and now generative ai. what once required teams of engineers and months of development can now be assembled by small teams or even individuals in days. as a result, the number of competing products in almost every category has exploded. more products are now fighting for the same finite pool of user attention, which means any growth channel that relies on capturing attention becomes increasingly competitive and therefore increasingly expensive.
this is why customer acquisition cost has risen across both consumer and b2b saas. when ten companies are bidding for the same keyword, ad prices remain manageable. when a thousand companies are doing so, the auction becomes brutal. the same dynamic applies to social media feeds, app stores, and even organic search. every distribution surface that depends on visibility rather than embeddedness turns into a zero sum game.
at the same time, conversion efficiency has declined. modern users are more skeptical, more overloaded, and more likely to trial multiple tools before committing. feature differentiation, which once provided clear positioning, has been flattened by rapid imitation and now by ai generated software. when two competing products can both claim similar functionality, the marginal benefit of choosing one over the other becomes small, which pushes buying decisions toward price or brand, neither of which is favorable to most startups.
retention has also weakened in this environment. when switching costs are low and alternatives are abundant, churn becomes structurally higher. a user who can migrate data or workflows in a few clicks has no economic reason to remain loyal unless something external binds them to the product.
this combination of rising acquisition costs, declining conversion efficiency, and falling retention creates a compounding problem. to maintain the same growth rate, companies must spend more to acquire users who stay for less time and generate less lifetime value. this is why so many saas businesses appear to be growing in users but not in economic quality, with revenue, margins, and cash flow lagging far behind.
traditional growth channels fail here because they operate outside the user’s actual workflow. ads, content, and sales are interruptions. they compete for attention, not for necessity. in a world where attention is the most saturated resource, interruption based growth scales poorly.
what has not become saturated, however, is dependency.
when a product becomes part of how other software works, it no longer has to fight for attention. it is invoked, called, and embedded. that is the economic opening through which ecosystem led product growth emerges.
what ecosystem led product growth means
ecosystem led product growth is not a branding exercise and it is not a partnerships team with a slide deck. it is a measurable distribution system where a meaningful share of new users, active usage, and revenue is generated through other software products rather than through direct marketing channels.
in practical terms, a product is experiencing ecosystem led growth when new users arrive because they encountered the product inside another tool, activated it through an integration, or were required to use it as part of an existing workflow. the acquisition event happens inside software, and not on a landing page. this distinction matters because it changes both the economics and the durability of growth.
consider how this works inside large product platforms.
shopify does not grow only because merchants search for ecommerce software. a significant share of merchant engagement and retention is driven by apps in the shopify ecosystem. when a merchant installs apps for payments, shipping, marketing, analytics, or inventory, shopify becomes embedded into more parts of their business. those apps also bring in new merchants because developers build tools that attract niche audiences and then pull them into the shopify platform. growth flows through the ecosystem rather than through shopify’s own marketing alone.
the same dynamic exists inside salesforce. the appexchange generates billions of dollars in partner revenue and tens of thousands of enterprise workflows. companies adopt salesforce because it connects to the tools they already use, and once adopted, they expand usage by adding applications that deepen their dependency on the platform. salesforce does not have to sell every feature directly because its partners sell functionality on top of it, and that partner activity becomes a permanent growth channel.
slack provides a more product centric version of this effect. teams often start using slack because of messaging, but long term retention and expansion are driven by integrations with tools like google drive, jira, github, and internal systems. every integration increases the number of daily reasons a team has to open slack. over time, slack stops being a chat app and becomes the operating layer for work. that transition is what converts usage into durable growth.
figma shows how ecosystems work even without a formal marketplace. plugins, community files, and integrations allow designers to extend figma into many specialized workflows. teams that use multiple plugins collaborate more deeply and store more of their work inside figma, which increases switching costs and makes figma the default design environment across organizations.
what these examples have in common is that growth comes from being connected. the product becomes more valuable not only because of what it does internally, but because of what it enables externally. each integration, plugin, or partner creates another entry point into the product and another reason for users to remain.
this is what separates ecosystem led product growth from traditional product led growth. product led growth relies on users discovering, trying, and adopting a product on their own. ecosystem led growth relies on products being pulled into workflows that already exist. one fights for attention. the other inherits attention.
the economic difference is profound. when growth flows through ecosystems, acquisition costs fall because distribution is shared. retention rises because switching requires breaking multiple connections. expansion becomes easier because new use cases are added by partners rather than built by the core team.
in a market where software is easy to build and hard to differentiate, ecosystems become the real competitive advantage. they turn products from isolated tools into networked infrastructure, and infrastructure, once adopted, is rarely replaced.
why ecosystems and retention move together
one of the clearest signals that ecosystem led product growth is real rather than rhetorical is the way it shows up in retention and expansion metrics across software companies. products that are deeply connected to other tools consistently retain users at higher rates and expand revenue faster than products that operate in isolation.
this relationship is not accidental, but structural.
a user who only interacts with a product through its own interface has a single dependency. if a competitor offers similar functionality at a lower price or with a better interface, switching is straightforward. data can be exported, workflows can be recreated, and the product can be replaced with relatively low friction. this is why standalone tools experience higher churn and require constant reacquisition spend to maintain growth.
a user who interacts with a product through multiple connected systems has a network of dependencies. their data flows into other tools, their processes rely on integrations, and their team builds habits around these connections. replacing the product now requires breaking not one relationship, but many. every integration becomes an additional layer of switching cost, and those layers compound.
this is why companies like shopify, salesforce, and slack see dramatically lower churn among customers who adopt multiple ecosystem components. a merchant who uses shopify payments, shipping, inventory, and marketing apps is not just using shopify. they are running their entire business through it. a sales team that connects salesforce to email, billing, support, and analytics is no longer choosing a crm. they are choosing the spine of their core operations.
this effect extends into ai.
ai systems do not operate in isolation. they require tools to read data, take actions, and complete workflows. an ai assistant that can only generate text is limited. an ai assistant that can connect to email, documents, calendars, databases, and payment systems becomes an operational agent.
this is where google’s ecosystem becomes decisive. google does not just have a large language model. it has gmail, docs, sheets, calendar, drive, chrome, android, and search, all wired into the daily workflows of billions of users. when an ai system can natively access these surfaces, it gains immediate distribution and immediate relevance. users do not have to adopt a new platform. ai appears inside the platforms they already use.
this is why google’s position in the ai race is not defined by model quality alone. it is defined by ecosystem reach. an ai assistant embedded in gmail and docs is called thousands of times per day by default. an ai assistant that lives on a separate website must be remembered, visited, and chosen.
the same logic applies to every software category. ai amplifies the value of ecosystems because it turns integrations into intelligence. the more places a product can read from and write to, the more often it is invoked. usage becomes automatic rather than deliberate.
from a growth perspective, this creates a powerful loop. integrations drive more usage. more usage deepens dependency. deeper dependency reduces churn. lower churn increases lifetime value. higher lifetime value makes every acquisition channel more profitable.
this is why ecosystem led product growth shows up first in retention and expansion before it ever appears in top line user growth. it changes the quality of customers before it changes the quantity, and over time, quality compounds into scale.
marketplaces turn ecosystems into growth engines
an ecosystem becomes a true growth channel when it stops being a collection of integrations and starts behaving like a market. this shift happens when third party developers, partners, and companies can build, distribute, and monetize on top of a platform in a way that is economically meaningful to them and structurally beneficial to the platform.
this is why marketplaces sit at the center of ecosystem led product growth.
the shopify app store, the salesforce appexchange, the atlassian marketplace, the figma community, and the wordpress plugin ecosystem all exhibit the same pattern. independent developers build tools because there is a large installed base of users. users adopt those tools because they extend the core product into specialized workflows. the platform grows because both sides are reinforcing each other.
this creates a two sided compounding loop that no traditional marketing channel can match. every new developer increases the range of use cases the platform can serve. every new user increases the economic opportunity for developers. growth emerges from the interaction between the two rather than from the platform’s own promotional efforts.
the economic data behind this is striking. in platforms like salesforce and shopify, partner ecosystems generate billions of dollars in annual revenue. more importantly, customers who adopt ecosystem products spend more on the core platform and stay longer. the platform does not need to build every feature because the market builds it on their behalf, while also funding itself through customer demand. see below stats for example:
shopify’s app ecosystem drove 32% of new merchant growth in 2025, with 8,000+ apps generating $1B+ in partner revenue.
salesforce appexchange powered $12.4B in partner revenue in fy2025 (up 20% yoy) and delivers 7-10x higher retention for customers using 5+ apps.
slack reports teams with 10+ integrations have 25% lower churn and 2x higher engagement.
this is what makes marketplaces such powerful growth assets. they convert what would normally be internal product development into external economic activity. instead of hiring engineers to build niche functionality, the platform creates rules, apis, and distribution surfaces that allow the ecosystem to fill those gaps. the result is faster product expansion, broader market coverage, and lower marginal cost of innovation.
from a growth perspective, this changes how distribution works. when a new app launches on a marketplace, it brings its own users. a developer building an invoicing plugin for shopify markets that plugin to accountants and small businesses, many of whom become shopify merchants as a side effect. a salesforce partner building a vertical crm extension brings in customers from that vertical who might never have adopted salesforce on their own.
this is acquisition without advertising.
the platform does not pay for these users. it simply provides the economic infrastructure that makes it rational for others to bring them.
in the ai era, this dynamic becomes even stronger. ai powered tools are highly specialized and often require access to multiple data sources. marketplaces provide both the distribution and the integration surface that allow these tools to exist. a developer can build an ai tool for legal analysis, marketing automation, or customer support and immediately reach users through an existing platform rather than trying to create a new audience from scratch.
this is why marketplaces are not a feature. they are a growth engine. they turn ecosystems into self expanding systems where every new participant increases the value and reach of the whole.
when platforms reach this stage, growth no longer depends on how much the company spends on marketing. it depends on how much economic activity flows through the network.
how ai converts ecosystems into default distribution infrastructure
ai systems do not discover software in the way humans do. a human encounters a product through search, advertising, social feeds, or recommendations, evaluates it through a user interface, and then decides whether to adopt it. an ai system, by contrast, operates through invocation. it executes tasks by calling software endpoints that have already been connected, authorized, and made available within its operating environment.
this difference has direct implications for how software grows.
in a traditional product led or marketing led model, growth is driven by visibility and persuasion. a company must continuously expose potential users to its product and convince them to try it. in an ai mediated model, growth is driven by availability and compatibility. a product that exposes stable apis, supports standardized data formats, and integrates into widely used platforms becomes callable by agents. a product that does not meet these conditions is not evaluated, compared, or chosen. it is simply absent.
this creates a binary distribution layer. software that is integrated into major ecosystems becomes part of the operational surface area that ai systems can use. software that is not integrated becomes unreachable, regardless of its quality or pricing.
this is why large platform ecosystems take on a new strategic role in the ai era. google, for example, controls a dense network of consumer and enterprise surfaces, including gmail, google docs, sheets, calendar, drive, chrome, android, and search. when an ai assistant has native access to these systems, it can observe context, retrieve information, and execute actions without requiring users to move between applications. the assistant’s usefulness is therefore not primarily a function of its language model, but of the breadth and depth of the ecosystem it can operate within.
you can already see it in how google is repositioning gmail in the gemini era. ai inbox uses gemini to summarize long threads, surface urgent to‑dos, and answer questions like “who was the plumber that gave me a quote last year?” directly inside your inbox. instead of asking users to learn a new tool, google is pushing intelligence into a surface they already open dozens of times a day.
a similar dynamic exists in enterprise software. an ai agent connected to salesforce, jira, slack, and zendesk can perform end to end workflows such as updating customer records, filing support tickets, and coordinating engineering tasks. an ai agent that is not connected to these systems cannot complete those workflows, even if it has superior reasoning capabilities. connectivity becomes the binding constraint.
from a growth perspective, this shifts distribution from demand generation to infrastructure positioning. products that integrate into dominant ecosystems become part of the default toolchain that ai systems rely on. usage flows from being included in automated workflows rather than from being actively selected by humans.
this also creates compounding advantages. once a product is integrated into multiple ecosystems, it becomes more likely to be included in ai orchestrations, which increases its usage, which in turn makes it more valuable to integrate with, which leads to further integrations. this feedback loop mirrors the shift where your ai stack is your new product team, orchestrating tools instead of humans manually choosing apps.
in this environment, growth is no longer primarily a function of brand strength or marketing spend. it is a function of how deeply a product is embedded in the software graphs that ai systems traverse. the most widely used products will be the ones that are easiest for machines to call, and not the ones that are easiest for humans to remember.
why ecosystem depth becomes the primary competitive moat
in a market where software functionality can be replicated quickly and cheaply, durable advantage can no longer be explained by features, interfaces, or even data alone. it must be explained by structural position. ecosystem depth creates that structure by embedding a product into a network of dependencies that is costly to reproduce and difficult to unwind.
this can be formalized in terms of switching cost accumulation.
a standalone product imposes a single switching cost, which is the effort required to move data, retrain users, and reconfigure workflows. when a competing product offers comparable functionality, that cost is often low enough to be outweighed by price discounts, incremental features, or temporary incentives.
an ecosystem product imposes multiple, layered switching costs. data is not only stored in the product, but also synchronized with other tools. workflows are not only defined inside the product, but also orchestrated across systems. users are not only trained on the interface, but also on the interactions between tools. replacing the product therefore requires replacing an entire set of connections, not just one application.
this is why companies like salesforce, shopify, and microsoft dynamics experience high customer lifetime value despite constant competition. their customers do not merely adopt a piece of software. they adopt an operating environment. partners build around that environment. internal teams structure their processes around it. data accumulates inside it. the economic and operational cost of leaving rises with every new connection.
ai intensifies this effect. when ai agents rely on specific tools to read and write information, those tools become part of automated workflows. once a process is automated, it is rarely revisited. an ai system that generates invoices through one accounting api or updates leads through one crm api will continue to do so unless explicitly retrained or reconfigured. this turns technical integrations into long lived dependencies.
from a competitive standpoint, this creates a moat that is both technical and economic. a rival product must not only match functionality, but also replicate the web of integrations, permissions, and workflows that tie the incumbent into the customer’s operations. in mature ecosystems, this can mean reproducing hundreds or thousands of partner connections, each with its own incentives and customer base.
this is why ecosystems are so difficult to disrupt once they reach scale. a better product does not automatically win if it cannot occupy the same position in the network. in practice, the network is the product.
for founders, this implies that defensibility in the ai era is less about building the most impressive standalone tool and more about becoming a central node in a larger system. the deeper and more numerous the connections, the stronger the moat, because what customers are really buying is continuity of their entire workflow graph, not just a set of features.
how startups should design for ecosystem led product growth from day one
ecosystem led product growth is not something that can be layered on after a product has reached maturity, because the architectural and strategic decisions that determine whether a product can become part of an ecosystem are made at the very beginning. once a product’s data models, permissions, and workflows are locked into a closed design, integrating it into external systems becomes slow, brittle, and expensive, which in turn limits how much of the ecosystem it can realistically occupy.
from a technical perspective, this starts with how a product exposes its functionality. products designed for ecosystems provide stable, well documented apis that allow external systems to read and write core objects. they use standard data formats and authentication mechanisms so that integrations can be built and maintained without excessive friction. products that treat integrations as a secondary concern often end up with fragmented, inconsistent interfaces that make it difficult for partners and platforms to build reliable connections.
from a product perspective, ecosystem readiness requires thinking in terms of workflows rather than screens. a product that only optimizes for its own interface encourages users to complete tasks inside a single environment. a product that optimizes for ecosystems allows those tasks to be triggered, monitored, and completed across systems. this means designing features that can be called programmatically, not just clicked by humans, and structuring permissions so that external tools can safely perform meaningful actions.
from a growth perspective, startups need to identify where their users already spend time and build outward from those surfaces. if customers live in salesforce, integrations should prioritize crm workflows. if they live in shopify, ecommerce and payments should be first class citizens. if they live in slack or microsoft teams, collaboration and notifications should be native. this approach treats platforms as distribution channels, but ones that deliver users in the context of real work rather than through marketing impressions.
economic alignment is equally important. ecosystems only grow when partners have incentives to invest in them. marketplaces, revenue sharing, and usage based billing create reasons for third parties to build, promote, and support integrations. without these incentives, integrations remain thin and fragile, and the ecosystem fails to reach critical mass.
what emerges from this design approach is a product that grows because other products depend on it. each integration increases surface area. each partner adds a new acquisition path. each workflow that runs through the product increases switching costs. growth becomes a side effect of being embedded rather than a function of how much is spent on marketing.
for early stage companies, this changes the definition of product market fit. instead of asking whether users love the product, the more powerful question becomes whether other software needs it. when a product becomes necessary inside an ecosystem, it gains access to a stream of users that no advertising budget could sustainably buy.
frequently asked questions (faqs)
what is ecosystem led product growth?
ecosystem led product growth is a strategy where a product grows primarily through integrations, platforms, marketplaces, and partner software rather than through direct marketing channels like ads, seo, or outbound sales. users are acquired because the product is embedded inside workflows that already exist in other software.
how is ecosystem led growth different from product led growth?
product led growth focuses on individual users discovering, trying, and adopting a product through its own interface. ecosystem led growth focuses on products being pulled into existing systems, so adoption happens through integrations and workflows rather than through sign up pages.
why are product ecosystems more powerful than marketing?
marketing competes for attention, which is a scarce and expensive resource. ecosystems compete for dependency. when a product becomes part of how other software works, users keep it because replacing it would break their workflows, data flows, and automations.
how do integrations create growth?
each integration creates a new distribution surface. for example, when a product connects to salesforce, shopify, slack, or google workspace, it becomes visible and usable to millions of users inside those platforms. this turns platforms into ongoing acquisition channels.
why does ai make ecosystems more important?
ai agents can only use tools that are connected to them. products that expose apis and integrations become part of automated workflows. products that do not are invisible to ai systems, regardless of how good their features are.
how do you measure ecosystem-led growth?
track these kpis:
% new users from integrations (target: >30%)
net revenue retention (nrr) (>120% signals dependency)
integration adoption rate (# active connections/user)
partner-sourced revenue (marketplace cuts)
use mixpanel/amplitude utm tags like ?source=slack_integration.
conclusion
the rise of ecosystem led product growth is not a trend driven by marketing theory. it is a direct consequence of how software economics and ai are reshaping the structure of competition.
as the cost of building software collapses, feature based differentiation becomes fragile. as acquisition channels become saturated, attention becomes expensive. as ai systems take over more work, software stops being chosen by humans and starts being invoked by machines. in this environment, the only durable advantage a product can have is its position inside a network of other products.
ecosystems turn software into infrastructure. they create switching costs that are not enforced by contracts or lock in, but by dependency. they generate growth that is not purchased, but inherited. they allow platforms like google, salesforce, shopify, and microsoft to scale not because they have the best individual features, but because they sit at the center of thousands of workflows.
in 2026, this dynamic will be even more extreme. ai agents will not browse app stores, compare pricing pages, or read landing pages. they will execute workflows across connected systems. products that are part of the default ecosystem will be used by default. products that are not will struggle to be called at all.
the winners in the next decade of software will not be the companies that build the most impressive standalone apps. they will be the companies that become indispensable nodes in the software graph.
that is what ecosystem led product growth really means.