Yesterday, Cline’s founder Saoud Rizwan announced a $27 million Series A funding round. The number wasn’t what stood out. The investors: Emergence, Pace, 1984 Ventures, were not unusual for a startup at this stage. What raised eyebrows was the install base: 2.7 million developers, most of them acquired without a marketing budget.
Cline is often described as an “open-source Cursor,” shorthand for a growing category of developer tools that integrate large language models into code editing environments. Cursor, Windsurf, Claude Code, Copilot, Solver: the list grows longer each month. But while those competitors fight for market share through subsidized pricing, aggressive model orchestration, or raw autocomplete speed, Cline has taken a slower, stranger route.
To the casual observer, Cline’s main differentiation appears to be its pricing model: no subscription, and no inference markup. But pricing was never the product. What Cline is really building is an operating system for agentic work. and the clearest glimpse yet of what post-autocomplete programming might look like.
The Illusion of Transparency
In July, Cursor revised its $20 monthly plan to include limits on previously “unlimited” model access. Confused users complained of silent usage caps and opaque billing rules. Windsurf faced scrutiny over its code collection practices. In developer forums, one theme echoed: “I don’t know what I’m paying for, or what they’re doing with my code.”
Cline positioned itself as the antidote. Open-source from the start, its client exposes model calls, context windows, and exact API costs. Developers supply their own API keys and pay providers like OpenAI or Anthropic directly. Cline adds no margin, only orchestration.
According to Rizwan, this was an ethical and strategic choice.“We capture zero margin on AI usage,” he told Forbes. “We’re purely just directing the inference.”
This approach gained traction among hobbyists as well as enterprises, where proprietary code security and billing control are non-negotiable. Samsung and SAP are already using Cline. Many of them started by forking it.
But transparency is only the visible part of the strategy. What it enables is more important.
Runtime, Not Feature
Cline isn’t an autocomplete engine. It doesn’t interject mid-keystroke or generate snippets inline. Instead, users interact with it in a separate panel. They assign tasks. The system reads the codebase, plans its actions, and executes them, often invoking terminal commands or editing multiple files at once.
This task-based structure reflects a shift. Instead of treating the model as a souped-up code assistant, Cline treats it as a delegated worker. To structure this interaction, it introduced two modes: “Plan” and “Act.” In Plan mode, the agent gathers context and suggests an approach. In Act mode, it carries out the plan autonomously, unless told otherwise.
“You give Cline a task and it just goes off and does it,” Rizwan said in a recent episode of the Latent Space podcast.
It’s a simple abstraction with far-reaching consequences. It turns Cline into a runtime. While competitors optimize for perceived helpfulness, more autocomplete, faster responses, Cline is optimizing for behavioral change: training developers to think in terms of delegation, orchestration, and modular tasks.
Nik Pash, Cline’s Head of AI, puts it more directly: “Cline is the infrastructure layer for agents.”
The Real Platform: MCP
If Cline were just a transparent interface to models, it would be a useful utility. What pushes it closer to an operating system is its integration with a protocol called MCP or Model Context Protocol.
Developed by Anthropic, MCP allows AI agents to connect to external services via standard APIs. Think: GitHub pull requests, Sentry stack traces, Slack messages, even Ableton music compositions, all callable by an LLM agent.
Cline was an early adopter. Then it went further: building the first MCP marketplace. Today, it lists over 150 MCP “servers,” plug-ins that expose toolsets to agents. Some are open-source libraries. Others are commercial. One, Magic MCP, charges for access to premium UI components. It was built entirely for use by agents inside Cline.
This creates a feedback loop. The more developers use Cline to automate parts of their workflow, the more incentive there is to build and monetize agent-compatible tools. In that loop, Cline isn’t a product: it’s the OS.
Proof in the Forking
Open-source projects are often forked. But few reach Cline’s scale. There are over 10,000 forks, including derivatives used at Fortune 500 companies and startups alike. Roo Code, another agent-based coding tool, is a direct fork. Claude Code borrows the same plan-act structure.
Rather than resist the fragmentation, Cline embraces it. “There are fork wars,” said Pash. “Ten thousand forks and all you need is a knife.”
This is the Linux model: win not through UI polish or monetization, but by becoming the default kernel. Cline’s team has no plans to close-source or gate usage. They see forks not as threats, but as distribution.
“No regrets about being open-source,” Rizwan said. That openness also encourages contribution. Cline’s community maintains its MCP servers, helps debug new model integrations, and experiments with local agents that run entirely offline.
Beyond Autocomplete
To understand where this leads, it helps to look at how developers are already using the system. One created a workflow where Cline pulls a GitHub PR, analyzes the diff, reviews the code, and sends a summary message to Slack, without human intervention. Another built an entire conference slide deck using Cline, SlideDev, and voice memos.
Cline is turning out to be a programmable, inspectable, semi-autonomous agent runtime. And it is positioning itself for a future where developers spend less time writing syntax and more time defining workflows, architecture, and intent.
That shift is not unique to Cline. Copilot is becoming more autonomous. Cursor has added background agents. But most are bound to business models that treat inference as cost center and product as a thin wrapper.
Cline’s idea is different: that agents will need an execution layer, and that this layer will be open, developer-controlled, and composable. Its revenue will come from enterprise features like fleet management and access controls, not usage throttles or model markups.
