Aider vs Devin: Comparing Coding Workspaces and File Storage Patterns

In developer evaluations, Aider achieved a score of 26.3% on SWE-bench Lite and 18.9% on the main SWE-bench dataset, while Devin achieved a state-of-the-art score of 13.9% on the main dataset at the time of their historical head-to-head comparison [Aider LLM Leaderboards 2024]. This difference highlights a core divergence in architectural design rather than just raw model capability. Aider is a local, terminal-based AI pair programmer that writes directly to your Git repository, whereas Devin is a fully autonomous AI software engineer operating within a sandboxed cloud environment. Because of these distinct structures, the way each tool reads, writes, and stores files differs. Choosing between them requires analyzing how they interact with your filesystem, manage dependencies, and coordinate with developers.

Aider operates directly on your local machine and local filesystem. It functions inside your terminal and relies on local environment configurations, using your local CPU, memory, and directories. When you prompt Aider to make changes, it edits the files in your project directory directly. It does not run your code in a remote sandbox or isolate its execution from your system. Aider uses Git as its version control and tracking mechanism, committing each of its edits directly to your local branch. This keeps you in complete control of the development process. You can inspect every change, run tests locally, and decide when to commit or push to remote repositories. The local-first design means there is zero lag from uploading files to a cloud, and you do not have to worry about remote environment setup or cloud sync lag.

Devin AI, on the other hand, operates within a secure, containerized cloud workspace. Devin is built by Cognition as an autonomous agent that runs its own virtual machine. When you give Devin a task, it spins up an isolated Devbox in the cloud. This environment includes its own operating system, package manager, web browser, and terminal. Devin writes and executes code inside this secure container, protecting your local host system from untrusted code execution or configuration pollution. Devin can research issues on the web, install dependencies, run test suites, and debug errors autonomously. The agent works independently on a task, updating you with its progress, and you do not need to keep your local machine running while it executes. However, this remote cloud setup introduces new requirements for moving files between your local system and the agent.

Does Aider run in a sandbox? No, Aider does not natively run in a sandbox. It operates directly in your local project folder. It has direct access to the files and shell commands on your machine. This gives Aider access to all your local tools, databases, and services, making it flexible for local development. However, because it has shell access, it runs commands with your user permissions. If you ask it to run a script, it executes directly on your system. Developers who want sandboxed isolation with Aider must configure their own Docker containers or run Aider inside a virtual machine manually. Aider does not provide built-in OS-level containerization out of the box, placing the security and environment isolation responsibility on the user.

How workspace state is preserved across runs is a major difference between local and remote agents. In a local workflow, Aider relies on your local disk. Files are written directly to your directory, so they are persistent. When you close your terminal, the files remain on your disk, and your Git history records every modification. Aider uses a session-based context. When you start a new chat, you specify which files to add to the conversation, and Aider reads those files from your disk. Aider does not maintain a remote database of your project files. It does not require a cloud storage service to keep its work. This local-first storage model is reliable because your files never leave your machine, and you manage persistence using standard Git workflows.

Devin AI workspaces are containerized virtual machines in the cloud, which introduces an ephemeral storage model. Devin maintains project history and timelines in the cloud during a task, but the underlying sandbox VMs are created for specific runs. While Devin's workspace is persistent during the active lifecycle of a task, files that are not committed to Git or exported to a durable repository can be lost when the VM shuts down or when a session is archived. Volatile assets like build logs, test coverage reports, package caches, and database files are generated inside the virtual machine's local disk. Because these assets are not part of the source code, developers do not commit them to Git. When the Devin session terminates, these intermediate files disappear, making it difficult to review build outputs or troubleshoot errors after the fact.

To prevent data loss from ephemeral cloud sandboxes, developers need a durable workspace layer. Standard options include copying files to local hard drives via SFTP, uploading artifacts to Amazon S3 buckets, or using consumer cloud storage like Google Drive or Dropbox. However, these storage systems are disconnected from the agent's runtime. Developers must write custom upload scripts or manually move files after each session, which introduces sync lag and complicates collaboration.

A team can use Fastio shared workspaces to maintain a persistent record of agent inputs, outputs, and build reports. Fastio acts as a collaborative storage layer where humans and agents work in the same workspaces. Once you enable Intelligence Mode, Fastio automatically indexes all imported files, making them searchable and queryable. Developers can configure Devin to write build artifacts, logs, and reports directly to a persistent Fastio workspace. This setup ensures that all intermediate outputs remain accessible to the entire team, even after the remote sandbox VM is terminated.

Managing file access and scopes is critical when working with AI coding agents. Because Aider runs locally, it reads and writes files directly through standard OS system calls. Aider does not need to sync files or handle network transfers to edit your project. It uses a repository map to analyze your codebase. This map is generated using ctags, which indexes class definitions, function signatures, and file structures. The map is sent to the LLM alongside your prompt, giving the model context about your entire repository without uploading the files themselves. This local access is fast and secure, as your source code remains on your machine and only the relevant code snippets are sent to the LLM API.

How does Devin AI access local files? Devin AI runs in a remote cloud sandbox and cannot access your local filesystem directly. To access local files, you must use the Devin CLI or Desktop application to sync your local folders with Devin's remote VM. You can also configure read and write permissions in your project's .devin/config.json file, or upload files directly via Cognition's Attachment API. This configuration file allows you to define read and write scopes, listing which folders Devin can access. For example, you can set read_scopes to your source code directory and write_scopes to a specific output folder. This ensures that Devin operates within your intended boundaries, even when running in autonomous mode where shell commands are auto-approved.

If you need to send external datasets or document assets to Devin, you can use the Attachment API. You upload the file through the Devin web interface or API, which generates a secure URL. You then reference this URL in your prompt as ATTACHMENT:"file_url" on a single line, prompting Devin to download the file into its sandbox. While this works for single files, transferring large datasets or multi-folder projects can be slow due to network bandwidth and VM configuration steps.

To simplify transfers, teams can use Fastio's URL Import feature. Fastio allows agents and humans to import directories from Google Drive, Dropbox, OneDrive, or Box using secure OAuth connections. It also supports importing files directly from public web URLs without local I/O. Once files are imported into a shared workspace, they are indexed and ready for use. Agents can access these files via Fastio's Model Context Protocol (MCP) server, which exposes Streamable HTTP at /mcp and legacy SSE at /sse for direct reads and writes. This eliminates the need to upload files manually or write custom sync scripts.

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Coordinating file access becomes complex when running multiple agents or collaborating with human developers. Aider is designed as a single-developer pair programmer. It runs in a single terminal session and modifies files one at a time. If you try to run multiple instances of Aider concurrently in the same repository, they can overwrite each other's edits, leading to Git conflicts, syntax errors, and race conditions. Aider does not have a coordination layer to manage concurrent file access or synchronize changes between different sessions. Developers must manage concurrent edits manually, ensuring that they checkout separate branches and merge changes sequentially.

Devin AI can run multiple subagents or execute parallel programming tasks in its cloud system. However, coordinating these agents requires a central workspace where they can read and write files without interfering with each other. If one subagent is writing test reports while another is editing source code, they must have clear directory boundaries and access controls to prevent file corruption.

Fastio provides the shared workspace architecture needed for multi-agent teams. Rather than treating storage as a simple file dump, Fastio organizes files into shared org-owned workspaces with granular permission controls. You can set permissions at the organization, workspace, folder, or file level, ensuring that each agent has access only to the files it needs. Every file in a Fastio workspace maintains a complete, per-file version history. If an agent overwrites a file or writes corrupt data, you can restore prior versions. This makes concurrent agent work auditable and easy to roll back.

Fastio also includes Collaborative Notes, which support real-time co-editing for both humans and agents. An agent can read a project outline in a Note, update a checklist as it completes tasks, and see edits made by human team members in real time. Fastio features an event-driven workflow engine that supports triggers like manual runs, scheduled cron jobs, and webhooks. Developers can build automated pipelines where an event (like an upload) triggers an approval step, routing the agent's work to a human teammate before it is published.

The financial and resource models of Aider and Devin are very different, representing the choice between open-source flexibility and managed SaaS convenience. Can you use your own API keys with Aider? Yes, Aider is open-source and uses a Bring-Your-Own-Key (BYOK) model. Aider does not charge a subscription fee. You install the CLI tool for free and configure it to use your own API keys. Aider supports all major LLMs, including Anthropic's Claude Sonnet, OpenAI's GPT-4o, Google's Gemini Pro, and local open-weights models running via Ollama. You pay the LLM providers directly for the tokens you consume. This gives you complete control over your API billing, allowing you to optimize costs by selecting cheaper models or using local hardware.

Devin AI is a commercial software-as-a-service (SaaS) platform. Cognition charges a monthly subscription fee for access to Devin, which includes the cloud sandboxes, web interface, and LLM API usage. Devin runs on Cognition's hosted infrastructure, using custom models trained by Cognition alongside public models. The monthly subscription fee covers all compute costs, web browsing, and model calls within the platform, making it a predictable expense for businesses. However, it is a closed ecosystem, meaning you cannot easily run Devin on your own local hardware or swap in custom local models.

When choosing between these models, teams must evaluate their infrastructure costs. Aider is cost-effective for individual developers who already have API keys and prefer to run tools locally. Devin is designed for teams that want to delegate entire engineering tickets to a cloud agent and do not want to manage local compute resources or API keys.

Fastio supports this ecosystem with usage-based credits, moving away from seat-based subscription models. Creating a user account on Fastio is free, but doing work requires an organization on a paid subscription. Plans include Starter ($29/mo), Business ($99/mo), and Growth ($299/mo), and every organization starts with a 14-day free trial that requires a credit card. Human users can grant long-lived scoped API-key access to agents, or use PKCE login to authenticate agents without sharing passwords. Fastio is compatible with any LLM, allowing you to connect local Aider sessions or remote Devin instances to the same persistent workspace, giving your team a single source of truth for all project files.