# Devin vs SuperAnnotate Devin records 2,549,346 monthly visits with +1% month-over-month growth and Paid pricing. SuperAnnotate records 386,034 monthly visits with +37.4% growth and Free Trial, Contact for Pricing pricing. | Metric | [Devin](https://openaitools.app/tool/devin) | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | | :--- | :--- | :--- | | Website URL | https://devin.ai | https://www.superannotate.com | | Categories | AI Code Assistant, AI Agent, AI Developer Tools | AI Workflow, AI Developer Tools, AI Project Management | | Pricing Model | Paid | Free Trial, Contact for Pricing | | Monthly Visits | 2,549,346 | 386,034 | | MoM Growth Rate | +1% | +37.4% | | Primary Market | United States | United States | | Direct Visits | 1,386,401 | 248,258 | | Review Score | 4.3 | 4.6 | | Saved Count | 0 | 0 | | Supported Platforms | website | Website | ## Devin Devin sits at the agentic end of the AI coding spectrum. Instead of suggesting the next line, it takes a ticket-level goal - "add SAML SSO for enterprise customers" or "investigate this API latency spike" - explores the codebase across repositories, implements changes, runs tests, and explains the resulting pull request in plain language. It maintains a wiki-style map of the codebase so it can navigate unfamiliar services and keep documentation current as the code changes. A defining feature is that Devin works on its own machine. It provisions Windows, Mac, and Linux virtual machines as needed, builds the project, exercises it in a browser, and can record itself clicking through the feature it just built. Long-running work continues in the cloud when your laptop is closed, and large tasks can be split across parallel agents that Devin coordinates and reports on. For teams, Devin extends beyond one-off prompts. You can tag @Devin in Slack or Teams so it reads a thread and replies with a PR, set up automations that fix failing tests or patch vulnerable dependencies as they appear, and run security reviews. Pricing is seat-based and enterprise-oriented, with an Ultra tier for heavier autonomous usage, and Cognition has marketed large security-credit programs for enterprises. **Pricing & Plans:** Paid, enterprise-oriented. Devin is sold on a per-seat subscription with tiered plans (including higher-capacity tiers for heavier autonomous use) plus usage allowances; Cognition also runs credit programs and enterprise agreements. Because plans and credit packages change, check Cognition's official pricing page for current numbers. - **End-to-end autonomous coding:** Devin plans, writes code, runs tests, and opens pull requests across complex, multi-repository codebases without waiting for line-by-line approval. - **Its own virtual machines:** Devin provisions Windows, Mac, and Linux VMs, builds and runs the project, and tests in a real browser - even recording itself using the feature it built. - **Slack, Teams, and alert integration:** Mention @Devin in a thread to delegate a fix, or connect CI, PagerDuty, and issue trackers so it reacts to failures and alerts automatically. - **Parallel cloud agents:** Delegate a large task and Devin splits it across parallel cloud agents that keep working after you close your laptop, then reports back. - **Codebase wiki and automations:** Devin maintains a living map of the architecture and supports recurring automations such as dependency upgrades, test fixes, and security scans. **Strengths:** Handles ticket-level autonomous work rather than only suggesting code completions; Tests in its own browser and VMs, which catches issues that pure code generation misses; Deep workflow integration with Slack, Teams, CI, and alerting tools; Keeps working in parallel cloud agents even when your machine is off **Limitations:** Premium, seat-based pricing puts it out of reach for casual individual use; Complex or poorly specified tasks still need human review before merging; Maximum value depends on having a well-structured repo and clean test setup [Profile (Markdown)](https://openaitools.app/tool/devin.md) ## SuperAnnotate SuperAnnotate positions itself as end-to-end AI data infrastructure rather than a single-purpose labeling tool. The core of the product is a highly customizable annotation surface that handles image, video, text, and audio projects in the same workspace, so teams that train multimodal models do not have to stitch together several vendors or build internal review tooling from scratch. Around the editor sit data curation and exploration features, analytics for tracking throughput and inter-annotator agreement, and project management for organizing large distributed labeling workforces. Beyond the software layer, SuperAnnotate offers an orchestration engine with metered compute hours, plus access to its own data-operations specialists who can run high-volume or unusually complex annotation programs on the customer's behalf. Managed-service elements such as single sign-on, dedicated Slack channels, a named customer success manager, a dedicated solutions engineer, and AI DataOps consulting are gated behind higher tiers, which reflects the enterprise orientation of the platform. SuperAnnotate is commonly used upstream of model training and fine-tuning, which is why it markets results such as faster annotation cycles and higher model F1 scores. It also supports the data-centric workflows around RLHF, SFT, RAG evaluation, and dataset versioning that modern generative AI teams depend on. Because it handles security, permissions, and auditability at an enterprise grade, it tends to be adopted by organizations that need governance as much as speed. **Pricing & Plans:** SuperAnnotate publishes three main tiers: Starter, Pro, and Enterprise. Starter is described as an entry point for small projects, Pro targets scaled AI and MLOps workloads and is sold via a demo request, and Enterprise is quoted through sales for well-established, recurring, high-volume programs. Published plan comparison highlights compute orchestration allowances of roughly 1K compute hours on Starter, 2.5K on Pro, and 10K on Enterprise, with SSO, dedicated Slack channels, a customer success manager, a solutions engineer, and AI DataOps consulting reserved for higher tiers. Exact prices are not listed publicly, so you should contact the vendor for a quote. - **Customizable multimodal editor:** One configurable annotation surface for image, video, text, and audio projects, so multimodal datasets can be labeled without switching tools. - **Data curation and exploration:** Search, filter, and inspect datasets to find the examples that will most improve a model, then route them into targeted annotation or review queues. - **Analytics and insights:** Dashboards for annotation volume, reviewer agreement, and project progress, plus advanced analytics on upper tiers for program-level reporting. - **Compute orchestration:** Metered orchestration capacity for running annotation, evaluation, and data-processing workloads, scaled by plan from roughly 1K to 10K compute hours. - **Managed Data Operations:** Optional access to SuperAnnotate's own annotation experts and AI DataOps consultants to run large or specialized data programs end to end. **Strengths:** Unified editor removes the need to maintain separate tools per modality; Rich project management and quality analytics suited to large distributed teams; Enterprise-grade governance features such as SSO and dedicated support channels; Managed data-operations services let you outsource complex or high-volume programs **Limitations:** No public list price, so budgeting requires a sales conversation; Advanced analytics, SSO, and dedicated support are locked to higher tiers; The breadth of features can be heavy for individuals or very small teams; Compute orchestration capacity is metered and may require planning for large jobs [Profile (Markdown)](https://openaitools.app/tool/super-annotate.md) ## Frequently Asked Questions ### How do Devin and SuperAnnotate compare in monthly traffic? Devin has 2,549,346 monthly visits, while SuperAnnotate has 386,034. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Devin has a month-over-month growth rate of +1%; SuperAnnotate has +37.4%. ### How does pricing compare? Devin: Paid. Paid, enterprise-oriented. Devin is sold on a per-seat subscription with tiered plans (including higher-capacity tiers for heavier autonomous use) plus usage allowances; Cognition also runs credit programs and enterprise agreements. Because plans and credit packages change, check Cognition's official pricing page for current numbers. SuperAnnotate: Free Trial, Contact for Pricing. SuperAnnotate publishes three main tiers: Starter, Pro, and Enterprise. Starter is described as an entry point for small projects, Pro targets scaled AI and MLOps workloads and is sold via a demo request, and Enterprise is quoted through sales for well-established, recurring, high-volume programs. Published plan comparison highlights compute orchestration allowances of roughly 1K compute hours on Starter, 2.5K on Pro, and 10K on Enterprise, with SSO, dedicated Slack channels, a customer success manager, a solutions engineer, and AI DataOps consulting reserved for higher tiers. Exact prices are not listed publicly, so you should contact the vendor for a quote. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Cursor](https://openaitools.app/tool/cursor) | 23,297,751 | +8.4% | Freemium | An AI-native code editor providing advanced multi-file code generation and editing, though less fully autonomous end-to-end than Devin. | [.md](https://openaitools.app/tool/cursor.md) | | 2 | [Manus](https://openaitools.app/tool/manus) | 19,195,779 | -1.7% | Free Trial | A general autonomous agent platform executing multi-step goals and browsers, suitable for broader web and coding workflows. | [.md](https://openaitools.app/tool/manus.md) | | 3 | [Google Antigravity](https://openaitools.app/tool/google-antigravity) | 16,054,109 | +8.1% | Freemium | Google's agentic development platform running parallel AI coding agents across an IDE and CLI workspace. | [.md](https://openaitools.app/tool/google-antigravity.md) | | 4 | [Replit](https://openaitools.app/tool/replit) | 10,016,437 | -7.7% | Freemium, Paid | A cloud development platform featuring an AI agent that builds, runs, and deploys full-stack apps automatically. | [.md](https://openaitools.app/tool/replit.md) | | 5 | [Qoder](https://openaitools.app/tool/qoder) | 5,145,040 | +81.8% | Freemium, Free Trial, Free | An agentic coding platform and IDE plugin that reads entire codebases to execute complex multi-file tasks. | [.md](https://openaitools.app/tool/qoder.md) | | 6 | [Kiro](https://openaitools.app/tool/kiro) | 3,330,476 | +4.6% | Freemium | An AWS spec-driven AI IDE and agent platform that plans, builds, reviews, and verifies software engineering work. | [.md](https://openaitools.app/tool/kiro.md) | | 7 | [Command Code](https://openaitools.app/tool/command-code) | 2,358,177 | -12.1% | Freemium, Contact for Pricing | The coding agent that learns your coding taste | [.md](https://openaitools.app/tool/command-code.md) | | 8 | [Orca](https://openaitools.app/tool/orca) | 1,025,891 | +1% | Free, Open Source, Enterprise on request | Run many coding agents in parallel, each in its own worktree | [.md](https://openaitools.app/tool/orca.md) | | 9 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Strong enterprise data labeling and agentic automation, but offers a distinct collaborative interface and pricing structure. | [.md](https://openaitools.app/tool/labelbox.md) | | 10 | [Trae](https://openaitools.app/tool/trae) | 702,366 | -7.8% | Freemium | An AI-powered IDE and coding agent that indexes entire repositories to plan, build, and test software. | [.md](https://openaitools.app/tool/trae.md) | --- Source: https://openaitools.app/compare/devin-vs-super-annotate More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)