Cline vs Devin: Local VS Code Extension vs Autonomous Cloud Agent

The 2025 Stack Overflow Developer Survey reveals that 81% of developers have concerns about data security and privacy when deploying AI agents [Stack Overflow Survey 2025]. The choice between Cline and Devin represents a core divergence in architectural design and deployment models to address these security concerns.

Cline is an open-source, local-first coding assistant that runs as an extension directly inside your Integrated Development Environment (IDE) like VS Code. By executing tasks locally, Cline interacts with your files and shell environments on your local machine. Because it operates within your local security perimeter, your source code and development environment remain entirely under your control. The code is Apache-2.0 licensed, providing full visibility into what the extension does.

Cline executes shell commands by interacting directly with your local system processor. This means the agent has access to your shell configurations (such as .bashrc or .zshrc), local environment paths, and command-line tools like git, npm, cargo, or pip. The agent uses your local system credentials and tools to run build processes, run tests, and manage dependencies, making it look and feel like a local pair programmer working on your machine.

Devin, created by Cognition AI, operates under a hosted, autonomous cloud model. When you assign a task to Devin, it spins up a remote, sandboxed Virtual Machine (VM) running in the cloud. It downloads your project, sets up the runtime environment, runs tests, and completes the work independently in that remote sandbox. While this cloud sandboxing eliminates the need for manual configuration on your local machine, it requires uploading your codebase, environment files, and credentials to a third-party managed environment.

This architectural difference determines how the two agents approach data privacy. With Cline, developers can choose to run local LLMs using runtime environments like Ollama or LM Studio, ensuring that no code leaves their hardware. In contrast, Devin requires the transmission of project context and inputs to cloud-based servers, which can be a compliance barrier for organizations handling proprietary code under strict confidentiality agreements.

The user interface and level of autonomy differ between the local-first extension and the cloud assistant.

Cline uses a human-in-the-loop authorization approach. Because Cline executes shell commands and edits files directly on your local system, it asks for explicit user approval before executing any write operations, running terminal commands, or making web requests. This prevents the agent from running destructive commands like recursive file deletions or untrusted shell installations without developer supervision. The user interface lives directly inside VS Code as a sidebar panel, showing file diffs in real-time as the agent edits code, which matches the standard development flow.

Devin is designed as an autonomous engineer. You describe a high-level task, such as fixing a bug or building a feature, and the agent plans and executes the entire workflow. It traverses directories, edits code, runs test suites, and opens pull requests inside its remote VM. Devin's primary user interface is a web dashboard showing its reasoning path, active files, and an integrated browser. You can monitor its progress in a web browser dashboard, viewing its steps and terminal output, but the agent does not require you to approve every single command during execution.

The two platforms use different billing structures, affecting both tool-calling costs and model selection.

Cline uses a Bring Your Own Key (BYOK) model. The extension itself is free to install, but you connect it to your preferred model provider. You pay for the API tokens consumed by the agent. Cline is model-agnostic, meaning you can connect it to frontier models like Claude 3.5 Sonnet, OpenAI GPT-4o, Gemini 1.5 Pro, or open-weights models like DeepSeek-V3 and Qwen2.5-Coder. Running an agent loop can consume millions of tokens during multi-file refactoring, so your API costs can scale quickly during complex sessions. Alternatively, users can opt for ClinePass at $9.99 monthly or use their usage-based console billing to manage spending.

Devin uses a subscription pricing model. Cognitive AI transitioned Devin to a self-serve tier starting at a Free plan with limited quotas, a Pro plan at $20 monthly containing a base usage quota, a Max plan at $200 monthly, and a Teams plan starting at $80 monthly [Devin Pricing Page]. Organizations requiring custom setups can use enterprise agreements billed on Agent Compute Units (ACUs). An ACU measures the computational resources consumed by Devin, including model inference tokens, virtual machine runtime, and network bandwidth. If a task exceeds the subscription quota, users buy additional capacity on-demand.

Using local models through Ollama or LM Studio represents the lowest financial barrier for Cline. By running a local model on your own hardware, you eliminate API token charges. However, local models require sufficient GPU resources and may struggle with the complex tool-calling and code logic that frontier cloud models handle easily. Developers using local models must configure context settings carefully (often increasing the default context window to at least 32k tokens) to prevent the agent from failing during long-running tasks.

When developers work in teams, local directories and isolated cloud virtual machines can create coordination silos. If one developer runs Cline locally, the resulting code edits and logs remain on their local machine. If another developer uses Devin, the output is isolated within their remote cloud session. Git repositories are the standard coordination layer for code, but they do not handle transient test data, generated document exports, or shared reference contexts efficiently.

To bridge this gap, teams use shared workspaces that serve as a coordination layer for both human developers and autonomous agents. Instead of relying on local drives or raw cloud buckets, a structured workspace platform like Fastio allows team members and agents to collaborate on the same file collections.

For example, developers can configure their local Cline agents to read and write files directly within a folder that stays synced with a Fastio workspace. Fastio is an intelligent workspace platform designed for agentic teams, offering usage-based credits instead of per-seat pricing. Creating an account is free, and organizations run on a paid subscription starting with a 14-day free trial that requires a credit card. The organization plans are structured at $29 monthly for Solo, $99 monthly for Business, and $299 monthly for Growth.

By pairing Cline or Devin outputs with Fastio workspaces, teams ensure that all generated files, test artifacts, and agent logs are preserved. The workspace provides a granular permission system (spanning organization, workspace, folder, and file levels) to restrict what files an agent can read or edit. It also maintains a complete per-file version history, allowing developers to restore previous versions if an agent introduces errors during a coding loop. These shared workspaces allow multiple agents (or a mix of people and agents) to work on files concurrently without overwriting each other's updates.

Coordinate Cline and Devin workflows in one workspace

Set up shared Fastio workspaces to persist local Cline edits or cloud Devin outputs, track file histories, and run automated approvals. Starts with a 14-day free trial.

Start 14-Day Trial

Software engineering often involves reading documentation, specification files, API designs, and compliance requirements. Local extensions like Cline must read these reference documents from the local filesystem or make web searches. Devin downloads documentation or browses the web from its virtual machine.

When dealing with large volumes of unstructured documentation, you can use Fastio's Intelligence Mode to index files automatically. When enabled on a workspace, Intelligence Mode automatically indexes all uploaded files (including PDFs, Word documents, images, and presentations) for semantic search and Retrieval-Augmented Generation (RAG).

Rather than forcing an agent to search and ingest entire directories of text, the agent connects to the workspace using the Fastio MCP Server. Fastio exposes its consolidated MCP toolset through Streamable HTTP at /mcp and legacy SSE at /sse. This allows the agent to execute a hybrid search, combining exact full-text matching with semantic retrieval, to find specific code requirements or API details. The search results include citations back to the source files, ensuring the agent's work remains grounded.

For extracting structured metrics or structured data from files (such as pull request metrics, test coverage percentages, or API endpoint schemas), teams can use Metadata Views. Metadata Views turn unstructured documents into a live, queryable database. Users describe the columns they want extracted in plain English, and the AI designs a typed schema (such as Text, Integer, Decimal, Boolean, URL, JSON, or Date & Time), matches the documents, and populates a filterable spreadsheet. You can learn more about structured extraction at /product/document-data-extraction/. This enables both developers and agents to query structured data without manual entry or complex scripting.

When a coding agent completes a task, the next challenge is transferring the output to production or handing it off to another team member.

With Devin, the agent works autonomously and can open a Pull Request directly in your Git repository. Developers review the PR using standard code review tools. With Cline, since the files are written directly to your local workspace, you can immediately review, run, and commit the changes from your local editor.

For team-based workflows that require structured sign-offs and reviews before publishing, teams can use Fastio's workflow engine. The workflow engine allows teams to build automated pipelines as a directed acyclic graph (DAG) of steps. An agent can deposit its completed files into a Fastio workspace, which triggers a review and approvals workflow. The files are routed to human reviewers for sign-off, and the entire process is recorded in Fastio's append-only audit log. The audit log serves as an immutable chain of custody, ensuring that all agent actions, approvals, and comments are tracked.

If an agent builds a workspace for a client, it can use the ownership transfer feature. An agent signs up for a free user account, configures the necessary workspaces and shares, and then hands off ownership to the client via a transfer link. The client then sets up their paid subscription and starts their 14-day free trial. This ensures a clean separation of billing and administration while allowing the agent to perform the initial setup. Developers can also use Collaborative Notes, which support real-time co-editing for people and agents in the workspace, ensuring that documentation and logs remain accurate during handoffs.