Devin vs Tembo: Which AI Coding Assistant Should You Choose?

AI coding assistants have come a long way from the days of basic autocomplete. We're now looking at sophisticated systems that can understand entire codebases, implement complex features, and even ship production-ready code.

Two names that keep coming up in conversations are Devin from Cognition Labs and Tembo. If you're trying to figure out which one might work better for your team, you've come to the right place. Let's dive in and break down what each brings to the table.

So, What Exactly Are We Talking About Here?

Devin AI: The "First AI Software Engineer"

Devin's got some serious marketing chops – they call it "the first AI software engineer." And honestly? The numbers back up some of that hype. This thing is designed to be an autonomous coding agent that works alongside your engineering team.

What really caught my attention is their SWE-bench performance: they're correctly resolving 13.86% of real-world GitHub issues end-to-end. That might not sound like much, but the previous best was 1.96%. That's a pretty significant jump.

Tembo: Your Tireless AI Teammate

Now, Tembo takes a different approach. Think of it less as a replacement developer and more like that teammate who never sleeps, never gets tired, and is always looking out for ways to improve your codebase.

The big difference? Tembo is constantly monitoring your applications, spotting issues before they become problems, and proactively creating pull requests to fix things. It's like having someone who's always doing code maintenance and optimization work in the background.

What Can These Things Actually Do?

Devin's Toolkit

Devin comes loaded with everything you'd expect from a skilled developer. It excels at planning and reasoning, breaking down complex tasks and thinking through solutions step by step. With access to a complete development environment—including shell access, a code editor, and a browser—Devin can operate much like a human engineer, independently handling tasks from start to finish.

Collaboration is another of Devin's strengths. It can work directly with your team members, picking up tasks you assign and communicating progress along the way. Whether you tag it in Slack or assign it a ticket, Devin is designed to integrate smoothly into your workflow, managing repository operations like cloning repos, scanning codebases, and finding the files it needs to get the job done.

Additionally, Devin handles the visual and maintenance aspects of development. It can create deployment previews and generate images for projects, making it easier to review changes before they go live. When it comes to bug fixing, Devin is proactive—creating pull requests, tracking progress in detail, and ensuring that issues are resolved efficiently and transparently.

Tembo's Superpowers

Tembo's got a more specialized focus, but it goes deep. Its real strength lies in database optimization—Tembo will proactively find slow queries, missing indexes, and database bloat, ensuring your PostgreSQL databases are always running at peak performance. But it doesn't stop there: Tembo also excels at codebase analysis, digging through your entire project to uncover technical debt and security issues before they become real problems.

When it comes to workflow integration, Tembo is a powerhouse. It processes tickets from tools like Linear, Jira, or GitHub and turns them into working code, while its 24/7 monitoring hooks into Sentry to instantly convert production errors into actionable pull requests. Tembo learns your team's coding style and conventions, so its contributions feel native to your codebase, and it works seamlessly with platforms like GitHub, GitLab, Linear, Jira, and Sentry—basically, whatever your team is already using.

Flexibility is another of Tembo's superpowers. You can choose from different AI models, such as Claude 3.5 or 3.7, and even bring your own API keys if you prefer. Tembo is always on the lookout for issues—whether it's a failed CI/CD pipeline or a production error, it's already working on a fix. Plus, it gets smarter over time, continuously improving based on feedback from your pull requests.

How Do You Actually Work With These Things?

Working with Devin

Devin is pretty straightforward – it lives in your Slack workspace. Tag @Devin in Slack to give it tasks, get progress updates through Slack, and it can pick up Linear tickets or GitHub issues. It creates PRs and responds to comments independently, and works asynchronously (which is nice when you want to focus on other things).

Working with Tembo

Tembo integrates into your existing workflow instead of creating a new one. It connects to all your existing tools automatically, detects and fixes errors before you even know they exist, handles ticket prioritization and converts them to PRs, provides a dashboard where you can see everything it's been working on, and you can create custom tasks directly in Tembo.

The Good, The Bad, and The Reality Check

What Devin Does Really Well

Devin is great for delegating complex tasks. It handles complete development tasks without much hand-holding, has access to a full development environment, and can run multiple agents in parallel.

Where Devin Struggles

Devin sometimes misses the nuance that human intuition provides, is less granular when it comes to feedback and context, and can be unreliable with really complex coding challenges. There's always a risk of introducing security vulnerabilities.

Tembo's Strengths

Tembo takes a comprehensive approach to software engineering. It excels at database optimization (especially for PostgreSQL) and produces high-quality PRs that are consistently merged. Tembo integrates seamlessly with most tooling and its proactive monitoring means fewer surprises. Customizable rule files let you teach it your conventions, multiple AI-model support gives you flexibility, and you still benefit from real-time error detection and fixes.

Tembo's Challenges

Tembo works best when your team actually engages with the feedback process and performs better when you already have solid development practices.

When Should You Use What?

Go with Devin when...

• You're tackling large-scale migrations or refactoring
• You want to automate significant chunks of development work
• Your team prefers asynchronous task handling
• You need to process multiple related tasks in parallel

Choose Tembo when...

• You want continuous codebase maintenance and optimization
• Database performance is important to you (especially PostgreSQL)
• You're dealing with technical debt or security concerns
• You want proactive error detection and immediate fixes
• You have high volumes of routine feature development
• You're using Linear or Jira for task management
• You need tight CI/CD integration
• You want flexibility in AI model choices

Show Me The Numbers

Devin's Track Record

Those SWE-bench results I mentioned earlier are impressive, but the real-world case study with Nubank is what got my attention. They achieved a 12x improvement in engineering hours and 20x cost savings during a major migration. That's the kind of ROI that gets executive attention.

Tembo's Performance

Tembo's sweet spot is in the quality of work it produces. Teams report 40-70% faster development cycles for routine features, which frees up time for the complex, creative work that humans are better at. Plus, their database optimization work has shown substantial performance improvements in production PostgreSQL databases.

Let's Talk Money

Devin's Pricing

Pretty straightforward:

• Entry level at $20 (transitioning to pay-as-you-go)
• Team subscription at $500/month
• Pay-as-you-go uses Agent Compute Units at $2.25 each
• 15 minutes of work ≈ 1 ACU

Tembo's Approach

Tembo focuses more on ROI than upfront costs. The value comes from:

• Reduced engineering hours on maintenance
• Faster resolution of production issues
• Database performance improvements
• Accelerated feature development
• Continuous code quality improvement
• Fewer security vulnerabilities

The Key Differences (The Stuff That Actually Matters)

Here's what it really comes down to:

Scope: Devin wants to be your AI developer. Tembo wants to be your AI maintenance and optimization specialist.

How you interact: Devin lives in Slack. Tembo integrates with your existing tools.

Autonomy: Both are autonomous, but Devin focuses on complete task independence, while Tembo balances autonomy with configurability and feedback loops.

Specialization: Tembo excels at database work and integrations. Devin offers broader software engineering capabilities.

Integration philosophy: Tembo works within your existing processes. Devin creates a parallel workflow.

Customization: Tembo lets you teach it your conventions. Devin uses its proprietary approach.

What's Next for AI-Assisted Development?

Here's the thing – both Tembo and Devin represent a future where AI doesn't replace developers, it makes them more effective. The teams that win will be the ones that figure out how to collaborate with AI tools effectively.

Think about it: you could delegate routine maintenance, accelerate standard feature development, free up creative energy for complex problems, reduce technical debt continuously, and maintain higher code quality standards.

That's a pretty compelling future.

So, Which One Should You Choose?

It really depends on what you need:

Go with Devin if you want a comprehensive AI software engineer that can handle end-to-end development tasks with minimal supervision. It's especially good for large-scale projects like migrations or major refactoring.

Choose Tembo if your priority is autonomous software engineering with comprehensive tool integration, real-time error detection, database optimization, and seamless workflow integration. It's perfect if you want something that works within your existing processes.

Both tools show us where software development is heading. The question isn't whether to adopt AI coding assistants – it's how to integrate them most effectively into your development process.

As these tools continue to evolve, we can expect even better capabilities, improved reliability, and smoother integration with our existing workflows. The future of software engineering will be a collaborative partnership between human creativity and AI efficiency.

And honestly? That sounds pretty exciting.