GitHub Copilot Shifts to Usage-Based Billing

GitHub Copilot makes you a faster engineer. Devin tries to be one. That's the sharpest way to describe the difference. Copilot lives in your IDE and suggests the next line. Devin gets a task, opens a shell, writes code, runs tests, reads errors, searches docs, and opens a pull request -- without you touching a keyboard in between. Cognition Labs launched Devin in March 2024 with a demo that went viral. A team of 10 people, 10 IOI gold medals between them, building what they called the "first AI software engineer." The benchmark number that circulated: Devin resolved 13.86% of real GitHub issues on SWE-Bench unassisted. The previous best was 1.96%. That's not a marginal improvement. That's a category shift. What does this mean practically? You can hand Devin a scoped ticket -- "add pagination to this endpoint with tests" -- and come back to a PR. The feedback loop runs inside Devin's environment, not through you. It's not magic. It struggles with ambiguous requirements, novel architectures, and anything requiring product judgment. And you should absolutely review what it produces. But the workflow shift is real: from writing code to reviewing code. Day 1 of my #45DayDevinChallenge. Starting with the fundamentals before going deep on prompting, Playbooks, integrations, and the parts that actually matter in production. Refer in detail Medium post on the topic : https://lnkd.in/gJm2ddrB What's your experience with autonomous agents vs. copilot-style tools -- and which has actually changed how you work? #DevinAI #SoftwareEngineering #AIAgents

For the past 6 months, GitHub Copilot Pro has been my daily AI coding partner. From inline code completion to Agentic Mode, it completely changed how I work-faster debugging, better code suggestions, smarter workflows, and less context switching. Honestly, for pricing vs performance, I always felt Copilot Pro was one of the best agentic AI tools available for developers. But on April 20, my subscription suddenly stopped. Since then, every time I try to resubscribe, it only shows: “Temporarily Unavailable.” At first, it was confusing. Later, GitHub announced major changes: → New sign-ups paused → Usage limits tightened → Model availability changed → Moving from request-based plans to usage-based billing from June 1, 2026 As a developer, this change feels big. Earlier, with request-based pricing, we focused only on solving problems. We didn’t need to think about how many tokens were being consumed behind the scenes. Now, every request, every response, and even cached tokens matter. That changes the developer mindset. Instead of just asking the best question, we may start thinking: “Is this prompt worth the token cost?” That creates friction. Yes, I understand agentic workflows are expensive and long-running sessions consume huge compute resources. Sustainability matters. But one reason Copilot felt powerful was because it let developers focus on building - not on tracking token usage. Copilot wasn’t just a tool for me. It became part of my daily engineering workflow. I still believe GitHub Copilot is one of the strongest AI coding assistants in the market. I just hope this transition becomes smoother, more transparent, and more developer-friendly-because great developer tools should reduce cognitive load, not add more. Did anyone else face the same “Temporarily Unavailable” issue with Copilot Pro? #GitHubCopilot #CopilotPro #AI #SoftwareDevelopment #DeveloperExperience #AgenticAI #VSCode #GitHub #Programming #Developers #Tech

Cursor vs GitHub After 6 months of deep evaluation across multiple engineering teams, the developer experience gap is wider than expected. SETUP & ONBOARDING: Cursor wins decisively here. Download, authenticate, and you're coding with AI in under 5 minutes. GitHub requires VS Code setup, extension management, and often wrestling with authentication flows that can take 20-30 minutes for new team members. DOCUMENTATION QUALITY: GitHub Copilot benefits from Microsoft's enterprise documentation machine - comprehensive but sometimes overwhelming. Cursor's docs are leaner, more example-driven, and get developers to their "aha moment" faster. SDK & INTEGRATION: This is where it gets interesting. Copilot's tight VS Code integration means familiar keybindings and workflows. But Cursor's purpose-built environment offers features like AI-powered refactoring and codebase-wide context that feel genuinely next-generation. DEVELOPER HAPPINESS: Our internal surveys show 73% preference for Cursor among developers who've used both for 30+ days. The key differentiator? Less friction between thought and code. The surprising insight: tool switching costs are lower than we assumed. Most teams can evaluate both in a sprint. Which tool has transformed your team's velocity the most? See the full comparison: https://lnkd.in/e2fGGryV #Cursor #GitHubCopilot #DeveloperExperience

🔥🚀 AI CHEAT CODE #032 🔥🚀 💡 GitHub Copilot just went AGENTIC for code reviews — and most devs have NO IDEA how to use it yet! 🤯 GitHub's new agentic code review is NOW generally available — and it's a total game-changer for PRs! 🎯 ⚡ Here's how to unlock it RIGHT NOW: 🔍 Step 1: Open any Pull Request on GitHub 👥 Step 2: Click the "Reviewers" dropdown on your PR 🤖 Step 3: Select "Copilot" as a reviewer — that's it! ⏱️ Step 4: Wait ~30 seconds while Copilot reads your ENTIRE repo, traces cross-file dependencies, and builds architectural context 💬 Step 5: Get inline comments that understand the BIG PICTURE — not just the diff! 🆚 What's ACTUALLY different now? ❌ OLD Copilot review: Only looked at changed files ✅ NEW Agentic review: Reads directory structure, traces dependencies across files, understands full architecture before commenting! 💻 BONUS CLI Cheat Code: Run this from your terminal 👇 gh pr review --request-review copilot Or just type /review in any PR comment! 🪄 🎯 Pro Tips: 💎 Agentic reviews catch multi-file bugs the old review MISSED 📊 Already 60 MILLION+ reviews done — growing 10x since launch! 🏢 Works on: Copilot Pro, Pro+, Business & Enterprise ⚙️ Runs on GitHub Actions (one-time setup if you opted out of hosted runners) This is what AI-assisted development looks like in 2026 — not just autocomplete, but an intelligent agent that UNDERSTANDS your codebase! 🧠🔥 💬 Have you tried the new agentic Copilot code review yet? Drop a 🔥 if this changed your PR game! Save this post for your next code review! ⬇️ #AI #GitHub #GitHubCopilot #CodeReview #DevOps #Coding #Programming #SoftwareEngineering #TechNews #Automation #MachineLearning #ArtificialIntelligence #WebDevelopment #OpenSource #TechTrends #Developer #AgenticAI #ProductivityHacks #Innovation #CloudComputing

GitHub Copilot is moving to **usage-based billing starting June 1, 2026** — and this is something organizations should pay close attention to. At first glance, the model seems reasonable: each developer gets a monthly allowance of AI credits, and usage is tied to how much you actually consume. But when you dig deeper, a few concerns start to emerge 👇 🔹 **Unpredictable Costs** Unlike fixed subscription pricing, costs will now vary based on usage patterns. Complex workflows, longer conversations, and agentic features can quickly burn through credits — making it harder for organizations to forecast spend. 🔹 **Agentic AI = Higher Consumption** Modern development is moving toward agent-based workflows (code generation across files, automation, etc.). These are exactly the scenarios that consume *significantly more credits*. Teams adopting advanced AI capabilities may see a sharp rise in costs. 🔹 **Model Selection Becomes a Cost Decision** Engineering teams will now have to think not just about *what works best*, but *what is most cost-efficient*. This introduces a trade-off between performance and budget that didn’t exist before. 🔹 **Hidden Scaling Impact** A few developers experimenting casually? Minimal cost. A full engineering org using Copilot deeply across CI/CD, CLI, chat, and agents? That’s a very different financial story. 🔹 **Shift in Governance Needed** Organizations may now need: * Usage monitoring dashboards * Budget controls per team * Guidelines on when to use which models * Policies around agentic workflows 💭 The bigger question: Are we moving from “AI as a productivity tool” to “AI as a metered infrastructure cost”? For leadership, this isn’t just a billing change — it’s a **FinOps challenge in disguise**. Would love to hear how others are planning to manage this shift. Are you thinking of putting guardrails in place already? https://lnkd.in/dSfj3yce #GitHubCopilot #AIBilling #FinOps #CloudCostManagement #DeveloperProductivity #AIGovernance #SoftwareEngineering #TechLeadership #AgenticAI #AIAdoption #EngineeringManagement #CostOptimization

GitHub Copilot Launches New AI-Generated Software Framework for Developers 📌 GitHub Copilot unleashes a new AI-generated software framework, transforming dev workflows from snippets to full ecosystems - think encrypted vaults and remote shells. Vibe coding is no longer fantasy; it’s powering 41% of 2025 code, with giants like Snap using AI for over 65%. DevOps teams now wield agentic tools, GPU-accelerated SDKs, and context-rich models to rebuild systems faster - and smarter. 🔗 Read more: https://lnkd.in/djMtQtKC #Githubcopilot #Llm #Vibecoding #Softwareframework #Developertool

One tool that quietly changed my daily workflow: GitHub Copilot. Not because it writes perfect code. But because it removes friction. Things that used to take minutes… Now take seconds. Writing boilerplate. Creating DTOs. Generating test cases. Handling repetitive logic. And that adds up. The real value of Copilot isn’t just speed. It’s momentum. You stay in flow longer. You switch context less. You explore ideas faster. But here’s what makes the difference: How you use it. Copilot is powerful when: 🔹 You know what you’re building 🔹 You can review and validate suggestions 🔹 You guide it with clear intent It’s not a shortcut for thinking. It’s a tool that amplifies it. The developers who benefit the most are not beginners… They’re the ones who already understand the fundamentals. Because they know what to accept. And what to reject. In the end, Copilot doesn’t make you a better engineer. But it can make a good engineer… significantly faster. How has GitHub Copilot changed your workflow? #GitHubCopilot #AI #SoftwareEngineering #Java #Developers #Productivity #Coding #Tech

GitHub Copilot went from: "We can't take new users." To: "Pay per use." That's not a pricing update. That's a signal. When a product is capacity-constrained, it means demand outran infrastructure. That's a good problem. But it also means the old pricing model, flat subscription, unlimited usage, stopped making sense. Because some users were using a little. And some were using everything. Usage-based billing fixes that. The heavy users pay more. The light users pay less. The economics align with the value actually being delivered. But here's the more interesting implication. When AI coding tools move to usage-based pricing, the conversation inside every engineering org shifts. It's no longer "do we have Copilot?" It's "how much are we actually using it — and is the output worth what we're paying?" That's a harder question. And a healthier one. The teams that use it constantly and ship faster will justify the cost easily. The teams that had it running in the background, barely touched, on a flat subscription? Now they have to reckon with whether AI actually changed how they work. Or just felt like it did. Usage-based pricing doesn't just change what you pay. It forces honesty about what you got. #GitHub #Copilot #AI #Engineering #FutureOfWork

🚀 I’ve completed GitHub Copilot Fundamentals – Part 2 of 2 by GitHub & Microsoft 🎉 🔗 Explore the learning path: https://lnkd.in/du9jJChK This comprehensive program (3+ hours, 6 modules) provided a deep dive into how AI-assisted development is reshaping the way we build, review, and maintain software. It goes far beyond basic autocomplete—focusing on real-world implementation, scalability, and responsible usage within teams and organizations. 🔍 What I Learned: 🧠 Advanced GitHub Copilot Capabilities Explored powerful features like Agent Mode, where Copilot can iteratively plan, generate, refactor, and improve code across an entire codebase—not just suggest snippets. ☁️ Copilot Cloud Agent Learned how to delegate development tasks to AI in a structured way, combining automation with human expertise to accelerate delivery while maintaining quality. 🔗 MCP Server Integration Gained hands-on understanding of GitHub MCP Server—enabling secure, scalable integration of GitHub features into AI tools like Copilot Chat, especially within environments like Visual Studio Code. 🔍 Smarter Code Reviews & PRs Discovered how Copilot enhances pull requests by identifying issues, suggesting improvements, and helping enforce coding standards—leading to faster and more reliable review cycles. 💻 Language-Specific Productivity (JavaScript & Python) Applied Copilot in real coding scenarios using JavaScript and Python, leveraging AI suggestions to write cleaner, faster, and more efficient code. 🔐 Responsible & Secure AI Usage Understood best practices for using AI tools in development environments—especially important for organizations adopting Copilot at scale. 🏢 Copilot for Individuals, Business & Enterprise Clarified the differences between various Copilot offerings and how they can be implemented effectively depending on team size and organizational needs. 🎯 Why This Matters: AI is no longer just an assistant—it’s becoming an integral part of the development lifecycle. This learning path strengthened my ability to: ✔️ Collaborate more effectively with AI tools ✔️ Increase development speed without compromising quality ✔️ Apply modern DevOps and AI-driven workflows ✔️ Build smarter, more scalable solutions 🎓 Proud to earn this certification from Microsoft and add it to my continuous learning journey! 🔗 Certificate: https://lnkd.in/d2-eR2DD #GitHub #GitHubCopilot #Microsoft #AI #DevOps #SoftwareEngineering #MachineLearning #Python #JavaScript #ContinuousLearning #Innovation