The way software is developed is undergoing a fundamental transformation. At innFactory AI and innFactory, we have consciously decided not to treat AI-assisted software development as an experiment, but to establish it as an integral part of our daily work. Alongside established tools like GitHub Copilot, AI Foundry, and Vertex AI, one tool has particularly stood out: OpenCode – an open-source AI coding agent that is fundamentally changing software development.
In this article, we share our experiences with OpenCode, explain why we prefer this tool over other solutions, and provide an honest insight into the opportunities and challenges of AI-assisted development.
What is OpenCode?
OpenCode is an open-source AI coding agent that far exceeds the boundaries of traditional code completion. With over 70,000 GitHub stars (as of 2026), the project has become one of the leading tools in AI-assisted software development. The MIT license guarantees complete transparency and free usage – a crucial factor for companies that value control and data privacy.
What particularly distinguishes OpenCode is its model-agnostic approach. Unlike proprietary solutions that bind developers to a specific AI provider, OpenCode supports over 75 different AI providers. From Claude to OpenAI and Gemini to Grok and local models – the choice lies with the developer. This flexibility makes it possible to use the optimal model for different tasks and not become dependent on a single provider.
OpenCode’s architecture is based on an intelligent agent system that not only suggests code but can independently perform complex development tasks. From problem analysis to implementation and multi-file refactoring – OpenCode works like an experienced developer who keeps multiple files in view simultaneously and understands architectural relationships.
How We Use OpenCode at innFactory
The introduction of OpenCode at innFactory was gradual and strategic. All developers on the team are progressively adopting the tool, with particular emphasis on internal training and the development of best practices. The insight came quickly: AI-assisted development requires a shift in thinking – from code writer to code architect.
In our daily work, we use different coding agents for different types of tasks. While one agent excels at analyzing complex codebases, another is particularly good at refactoring legacy code. A third agent specializes in implementing new features while considering existing architectural patterns. This specialization allows us to use the optimal assistant for each task.
The development process has fundamentally changed. Developers become sparring partners with the AI – they define requirements, set architectural guardrails, and review results. Manual coding is increasingly becoming the exception. Instead, our developers focus on quality assurance, architectural decisions, and solving complex domain problems.
A concrete example: Tobias Jonas, our Co-CEO, built the new innFactory AI website significantly with AI assistance. What would have previously meant weeks of manual work could be realized in a fraction of the time through the intelligent use of OpenCode – without compromising quality or maintainability. The AI took over repetitive tasks, boilerplate code, and initial implementations, while Tobias could focus on architecture, user experience, and business logic.
Advantages Over Other Tools
The market for AI-assisted development tools is diverse. To understand OpenCode’s positioning, a differentiated comparison with other leading solutions is worthwhile.
OpenCode vs. GitHub Copilot IDE Integration
GitHub Copilot has made AI-assisted development mainstream and is already standard in many development teams. However, while Copilot primarily functions as intelligent code completion in the IDE, OpenCode goes significantly further.
OpenCode doesn’t just execute individual code suggestions but complete multi-file refactorings. The tool understands project-wide relationships and can perform complex changes across dozens of files. The agentic workflows enable independent, multi-step tasks: the AI plans, implements, tests, and optimizes – all in one continuous process.
Another decisive advantage is OpenCode’s skill system. Developers can implement their own skills and thus adapt the tool to specific requirements. Whether special coding standards, proprietary frameworks, or industry-specific patterns – OpenCode can be extended and customized.
Additionally, OpenCode offers free choice of AI models. While Copilot relies on OpenAI models, OpenCode users can choose between dozens of models – depending on the task, costs, and data privacy requirements.
OpenCode vs. Claude Code
Claude Code from Anthropic has established itself as a powerful tool for code-related tasks. The quality of outputs is impressive – no wonder, as Claude is one of the most powerful large language models on the market.
However, the crucial difference lies in the philosophy: OpenCode is open source and community-driven, while Claude Code is a proprietary solution. With OpenCode, there’s no vendor lock-in – the software belongs to the community, not to a single company. Companies have full control over their data and can self-host OpenCode if needed.
Interestingly, code quality is comparable when both tools use the same model. OpenCode can use Claude models and then achieves similar results to Claude Code – but with additional flexibility and control. The transparency of the open-source approach also enables traceability of decisions and adaptation to specific compliance requirements.
OpenCode vs. Cursor and Other Commercial Solutions
Tools like Cursor have established themselves as popular commercial alternatives. They offer integrated development environments with AI functions and polished user interfaces. But here too, OpenCode shows significant advantages.
The cost efficiency is remarkable: with OpenCode, users only pay the pure API costs of the AI models used. There’s no commercial markup, no subscription fees for different tiers. For companies that use AI tools intensively, this can lead to significant savings.
The privacy-first approach enables local hosting and the use of self-hosted models. For companies with strict data privacy requirements or those working with sensitive customer data, this is an invaluable advantage. OpenCode can be operated completely on-premise – no code leaves your own infrastructure.
Multi-agent support and LSP integration for over 40 programming languages also make OpenCode a universally applicable tool. Whether Python, TypeScript, Rust, Go, or Java – OpenCode offers consistent support across different technology stacks.
Challenges and Realistic Assessment
Despite all the enthusiasm for OpenCode’s possibilities, honesty is required: AI-assisted development is not a panacea and brings its own challenges.
One of the biggest requirements is the broad knowledge developers need. To work effectively with OpenCode, you must be able to think like a system architect. It’s not enough to write code – you must understand system relationships, make architectural decisions, and assess the quality of AI-generated solutions. This requires experience and deep technical understanding.
Highly complex enterprise systems with decades of legacy code, complex dependencies, and specific domain knowledge pose challenges for OpenCode. While the tool provides valuable support here too, it’s not yet capable of fully autonomously understanding and modifying such systems. The human expert remains indispensable – but even here, OpenCode can make a strong contribution, such as in documentation, targeted refactoring, or implementing clearly defined sub-tasks.
The learning curve for adoption should not be underestimated. Developers must learn to communicate effectively with AI, formulate good prompts, and critically evaluate results. This transition requires time, training, and willingness to question established ways of working.
API Key Management and Cost Control
A critical aspect of professional use of AI tools is the management of API access and costs. At innFactory, we have established a well-thought-out system for this.
Our GitHub accounts are organized via Azure and Azure Billed Metering – an advantage of our Microsoft partnership. This enables central management and transparent billing. But pure billing isn’t enough: with intensive use by an entire development team, costs can quickly rise to unforeseen heights.
That’s why we developed AI Control – our own software for usage control and price monitoring. The system enables us to monitor API usage in real-time, set budget alerts, and provide detailed evaluations by projects, teams, and individual developers. This way, we maintain control over costs without restricting our developers’ creativity and productivity.
The investment is significant: we’re talking about a clearly four-figure amount per month for our company size. But the return on investment is measurable: the gained productivity, faster time-to-market, and higher code quality directly benefit our customers. Projects that previously took months can now be realized in weeks – with the same or even higher quality.
The Future of Software Development
OpenCode is more than a tool – it’s a harbinger of a fundamental change in software development. The role of the developer is transforming from code writer to architect and quality inspector.
In this new world, AI takes over repetitive and time-consuming tasks: boilerplate code, standard implementations, refactorings, tests. Developers focus on what humans do best: creative problem-solving, architectural decisions, understanding complex business requirements, and quality assurance.
The focus shifts from syntax to semantics, from “How do I implement this?” to “What should the system do and why?”. Developers no longer need to implement every detail manually but can focus on solving domain problems. AI becomes a powerful assistant that implements technical details while humans provide strategic direction.
A special aspect is democratization through open source. Tools like OpenCode make advanced AI assistance accessible to companies of all sizes – not just tech giants with unlimited budgets. Small and medium-sized enterprises in the DACH region can use the same tools as large corporations and thus compete internationally.
Conclusion: Pragmatic Optimism
At innFactory AI and innFactory, we have found in OpenCode a tool that supports our vision of modern software development. The combination of open-source philosophy, model-agnostic approach, and powerful agentic workflows makes OpenCode an indispensable part of our development toolkit.
But despite all our technology enthusiasm, we remain pragmatic: AI doesn’t replace the experienced developer but amplifies their capabilities. The best results come from the interplay of human expertise and machine efficiency.
For companies in the DACH region looking to modernize their software development, a close look at OpenCode is worthwhile. The investment in training and infrastructure pays off – not only in higher productivity but also in more motivated developers who can focus on interesting problems instead of repetitive tasks.
The future of software development has already begun. With tools like OpenCode, we are actively shaping this future – transparently, controlled, and with humans at the center.