5 Low Code Trends in 2025 Developers Need to Watch

The low code space has changed a lot in the last few years. The market has shifted from a few large, expensive platforms to many flexible, open-source platforms. This has given developers even better tools, reduced vendor lock-in, and delivered better business outcomes by further closing the gap with traditional apps. In 2025, we expect the low code space to change even more.

2025 is the year we'll see a significant increase in adoption, as well as development paradigm shifts in the low code space, as more companies adopt low code as the foundation for their enterprise AI applications. Platforms that are positioned to support emerging AI use cases will thrive, while those that don't may struggle to compete.

This post discusses the most important trends that we predict in 2025 for low code and AI, and how developers and businesses can make sure they're positioned to take full advantage of new AI technologies.

Up to this point, we’ve seen adoption of some very promising coding assistants like GitHub Copilot, but we’ve yet to see full-fledged IDEs that leverage AI for most of the heavy lifting. As the accuracy of AI models continues to improve and the rate of improvement continues to accelerate, we expect to see much deeper integration and reliance on these models to write code.

We may even see the successful development of a full application using only AI prompts — something that has been repeatedly attempted but not yet successful, as far as I'm aware (if you are aware of a fully fledged, 100% AI written app, please send me a message. I'd love to take a look).

This trend will affect low-code platforms as developers will come to expect AI-assisted software development. That’s why we’ve started introducing AI copilot features like our new custom widget assistant.

Traditional applications have allowed users to interact with data mainly using forms and buttons. In the last few years, after AI has emerged on the scene, people have integrated chat functionality to many existing applications. However, most AI chat interfaces have been relatively simplistic and essentially just another feature.

We believe this is drastically underestimating the potential of AI-powered apps. In 2025, we expect the focus of web applications to shift from static pages with static inputs to more dynamic pages generated according to the prompts that the user provides. This leap will be akin to the leap in interactivity seen when JavaScript changed web pages from simple, static HTML documents to the rich media experiences we know today.

Chat boxes will not just be an afterthought; they will be the main driver of interaction between users and web applications. Combined with the evolving ability for AI to write code, instead of having a static prebuilt UI, users will ask the LLM questions and tell it how they want the data presented, then the UI will be dynamically created and populated with the data that users want to see presented in graphs, charts, text, or images.

Low-code platforms are already adapting to this new way of building applications, and we at Appsmith are going farther: we intend to get ahead of the curve on this by making AI assistants the major focus of our low-code platform as early as possible in 2025.

In order to power Prediction #2, entirely new architectures will need to be built on the back end of applications, specifically including RAG and AI agents. Right now, low code is largely lacking in support for this infrastructure. Certain features that are common today within low-code platforms (like workflows) make it possible to build these services in some cases, but it is still very clunky.

We predict that in 2025 low-code platforms will put a lot of focus on integrating RAG pipelines that work with external and internal data sources for context, natively supported vector databases, and autonomous AI agents that can be customized to perform specific tasks on behalf of users.

As AI becomes more integral within applications, we also predict that low code will become even more important to verify the accuracy of AI output. There will always be a need for human verification of AI responses to improve models, so it’s important to be able to quickly spin up custom applications to automate the logistics surrounding human validation of responses. Low code is ideal for this, as it allows developers to rapidly build, iterate, and adapt applications.

In 2025, we will be building on Appsmith's existing AI functionality with plans for key features to support even more reliable AI applications such as RAG, agentic AI, human-in-the-loop (HITL) verification, and LLM response reporting and monitoring in testing and production.

Large language models (LLMs) are the technology behind modern AI. The best large language models will likely continue to only be available through third-party providers such as OpenAI and Anthropic, who have access to huge training resources, specialist engineers, and massive datacenters to power it all. This presents a security and privacy concern to businesses who want to leverage these public tools using sensitive data such as proprietary information and the legally protected, personally identifiable information (PII) of their users.

Over the last few years, enterprises settled for sending private information off premise and trusting that companies like OpenAI and Anthropic would not retain it or use it to train their models. Ultimately, large AI companies have pledged not to train on this private data, which is great, but promises can be broken. This means that going forward, and knowing what we know about how these models function, protecting sensitive data must become a central part of every AI application.

Starting in 2025, we expect to see more enterprises relying on a mix of large, commercial LLMs and self-hosted, open-source LLMs to better manage the performance vs. security tradeoff. Even if these open-source models aren't as good as the cutting edge ones from OpenAI and Anthropic, they're reaching a point where they're good enough to use reliably in production for many applications. In many cases, the latest open-source models like Llama and Mistral are higher quality than older, closed-source models.

Enterprises will need tools to self-host and manage open-source LLMs so that they can secure their data and place stringent security/compliance requirements on their AI models. Self-hosted low-code platforms provide the framework for this and enterprise managed hosting makes it even easier to implement.

We also expect more experimentation with permission-aware LLMs that understand which user they’re interacting with and which data they have access to. This problem is currently unsolved, but it is such a high priority for so many large stakeholders that an AI-native solution is highly likely to emerge in the next twelve months.

Low code is popular due to the obvious benefits it offers: reduction in development resources, faster development times, and turn-key infrastructure. However, as businesses have come to rely on these benefits, some of the most popular low-code platforms have raised their prices, knowing full well that their largest customers are largely locked in with them by now and can either pay up or tear down all of their infrastructure and start again somewhere else.

For example, OutSystems has tripled its prices in the last year, which is pushing many businesses to seek alternatives that offer them more control over the apps they build and their critical data. We believe that open-source low-code platforms are positioned to be some of the most attractive replacements, since they side-step the vendor lock-in problem entirely. For the first time ever, Gartner has mentioned Appsmith as an open-source alternative.

Appsmith always has been and always will be an open-source platform, and we're continuing to experiment with ways to keep Appsmith open source for everyone while still keeping the lights on. We also continue to be 100% firm that our users should never be locked in and must be able to retain control over everything they build in Appsmith. All applications built on commercial tiers are fully compatible with the open-source version of Appsmith.

In the last few years, there has been a lot of wild talk about how AI will change software forever. Even though much of this was overblown and hasn't come true, these technologies are having a real effect, and they must be considered when planning your IT infrastructure and investments.

AI won’t just be an extra feature to throw onto a platform for marketing purposes like it largely is now — it will become an essential force multiplier, especially as each industry, company, department, and employee gains access to these tools and discovers new ways to apply them. When AI starts changing how your industry does things, you want to be right in its path, waiting with the right tools to rapidly adopt it to stay ahead of your competitors.

We’re working every day to make sure Appsmith is one of those key tools. You can try Appsmith’s free-forever, cloud hosted version or reach out to see how Appsmith could power your enterprise custom applications.