LLMs in Low-Code/No-Code Platforms (Mendix, Retool, Power Platform & Bubble)

Table of Contents

• 🏗 Mendix – AI Co-Pilot for Low‑Code

• 🛠 Retool – LLM Integration in Internal Apps

• 🤖 Microsoft Power Platform – Generative AI in the Enterprise

• 🫧 Bubble – No-Code Meets Generative AI

• 💡 Cost-Effective Strategies for Startups, Enterprises, and Small Teams

🏗 Mendix – AI Co-Pilot for Low‑Code

Mendix, a Siemens low-code platform, has integrated AI to accelerate application development. Mendix Assist acts as a co-pilot in the IDE, using machine learning to suggest microflow logic and next steps as developers build apps. This AI assistant was trained on vast amounts of app project data to predict around 90% of a developer’s actions, effectively automating routine logic creation (Build an AI app with Retool to write better sales outreach emails | Retool Blog | Cache), Mendix Assist thus behaves like an LLM-driven code completion tool, offering contextual suggestions in real-time (for example, suggesting how to connect two modules or transform data in a workflow). These features streamline development by reducing manual effort and human error in logic design.

Beyond the development co-pilot, Mendix enables prompt-driven functionality within the apps themselves. Developers can integrate external LLMs via connectors or REST APIs to add generative AI capabilities to their Mendix applications. For instance, one can plug in OpenAI’s API to build a chatbot in a Mendix app or to perform on-the-fly text analysis. In practice, Mendix apps in production have used LLMs for tasks like automatically generating report summaries and guiding end-users through complex forms via a conversational assistant. Architecturally, this is achieved by calling the LLM’s API from a Mendix microflow (an orchestrated workflow in Mendix) and handling the response within the app’s logic. Mendix’s platform ensures such calls are encapsulated as reusable actions, so teams can securely manage API keys and re-use the integration across multiple apps.

Mendix’s LLM integration strategy thus has two layers: an AI-augmented developer experience (co-pilot suggestions in Studio Pro) and runtime AI services within apps (via connectors to services like GPT-4). This dual approach helps enterprises build intelligent applications faster. While specific case studies from 2024 are less public for Mendix, the trend is clear – Mendix is leveraging generative AI to boost developer productivity and to enable new app capabilities in areas like customer support (e.g. automated chat responders) and data insights (natural language queries on business data). These enhancements align with the wider industry adoption of generative AI (over 28% of people were using genAI for content creation by (Build an AI app with Retool to write better sales outreach emails | Retool Blog | Cache)3, indicating Mendix’s commitment to staying at the forefront of low-code AI integration.

🛠 Retool – LLM Integration in Internal Apps

Retool is a popular low-code platform for building internal tools, and it has embraced LLMs as first-class citizens in its ecosystem. In 2024 Retool introduced Retool AI features that let developers easily incorporate generative AI into apps and workflows. Concretely, Retool provides pre-built AI Actions – essentially blocks that call out to an LLM – which can be added to an app without custom API integration (Retool | Automate and speed up business processes with AI)0, These actions support multiple providers (OpenAI’s GPT series, Anthropic’s Claude, Azure’s OpenAI service, etc.) or even custom models, giving teams flexibility in choosing providers 0, For example, a developer can drag an “AI Complete Text” action into a Retool workflow and configure a prompt; at runtime, this might call GPT-4 to generate a text output which can then be used in the app (such as drafting an email or summarizing a customer feedback ticket).

Retool has also integrated a Vector Store (Retool Vectors) to enable Retrieval-Augmented Generation (RAG) with ease. Developers can embed their business data (from databases or docs) into vector embeddings within Retool, and then, with “one-click RAG,” attach those vectors to LLM prompts 9, This means a Retool app can answer questions using company-specific data by retrieving relevant facts and feeding them into the LLM, all with minimal setup. For instance, an internal support app could use Retool Vectors to store product manuals and let an LLM-powered assistant query that knowledge base to help support agents 5,

Capabilities and Use-Cases: Retool’s LLM integration unlocks a range of use cases:

• Code Generation: Inside Retool’s editor, you can use an AI action to generate SQL queries or JavaScript code snippets from natural language (Retool | Automate and speed up business processes with AI)6, This speeds up writing complex queries or transformations.

• Text Generation & Summarization: Retool apps can offload writing tasks to AI – e.g. generating personalized sales outreach emails or summarizing long reports – so users don’t have to copy-paste data into external tools 1,

• Classification & Extraction: With AI actions, you can classify text or extract entities. For example, automatically labeling support tickets or pulling key values from contracts becomes straightforward 9,

• Multi-LLM Prompt Testing: Retool even allows comparing outputs from different LLM providers side-by-side. Developers can test a prompt on, say, GPT-4 and Claude simultaneously to see which fits their needs, then swap models seamlessly 7,

In production, many startups and teams have built AI-powered tools with Retool. A real example is CommandBar, whose team integrated Retool AI to generate personalized sales messages by combining CRM data (from Salesforce, Outreach, etc.) with GPT-based generation – saving their sales team hours every day 4, The Retool platform handles the heavy lifting: securely connecting to internal data, orchestrating LLM API calls, and providing audit logs and permission controls to ensure enterprise data security 2,

From an architectural perspective, Retool acts as the orchestrator between the user’s data and the LLM. The platform’s server will call the chosen LLM API (e.g., OpenAI’s endpoint) when an AI action is triggered, merge any retrieved vector context if used, and then return the LLM’s output to the app’s front-end. Because Retool is often self-hosted or cloud-hosted within a company’s environment, it can mediate these AI calls while respecting security constraints (no direct exposure of secrets to the client, etc.). This setup allows even resource-constrained internal tools teams to leverage powerful LLMs without building a whole pipeline from scratch – Retool provides the scaffolding out-of-the-box (Retool | Automate and speed up business processes with AI)2,

In summary, Retool’s integration of LLMs in 2024/2025 is mature and feature-rich: from UI components bound to AI actions, to data augmentation with vectors, it’s aimed at bringing GPT-like capabilities into everyday business apps quickly and safely.

🤖 Microsoft Power Platform – Generative AI in the Enterprise

Microsoft’s Power Platform (which includes Power Apps, Power Automate, Power Virtual Agents, etc.) has seen a major infusion of LLM-powered features branded under the “Copilot” moniker. In 2024 and 2025, these capabilities moved from preview to production, drastically expanding the no-code AI abilities for business users.

Copilot in Power Apps: Makers can describe an application idea in natural language and have Power Apps automatically generate a working app (complete with data schema, screens and basic logic). This is powered by GPT-4 under the hood, orchestrated via Azure OpenAI Service. For example, a user could type “Create a customer feedback app with a form for ratings and comments, and an admin page to summarize feedback,” and the Copilot will scaffold an app matching that description. The generated app isn’t final – makers can refine it – but the LLM does the heavy lifting of initial app design. Additionally, inside Power Apps, one can drop a Copilot chat component onto a canvas. This embeds an AI assistant within the app UI that end-users can interact with. Backed by an LLM, this assistant can answer questions about the app’s data. Notably, Microsoft implemented this such that the Copilot is aware of the app’s Dataverse data tables and business logic, so an end-user might ask “Show me open support tickets from VIP customers” and the Copilot will formulate the appropriate data query and respond, all in real-time. This essentially gives end-users a natural language query interface for any Power App (Power Apps Archive - Microsoft Power Platform Blog)33,

Copilot in Power Automate: Microsoft added an LLM-driven flow authoring experience. Users can simply write what they want to automate (e.g. “When a new lead is added in Dynamics 365, send an email to the sales team and post a message in Teams”) and Copilot will draft the workflow with the necessary triggers and actions. Under the hood, this uses a prompt-to-logic model (GPT-4) that has been specialized to understand Power Automate’s catalog of actions. This feature, often called “describe it to design it,” was in preview in 2023 and saw GA in 2024, making automation creation much faster for non-developers. Furthermore, Power Automate can leverage AI Builder’s text generation models (which are powered by Azure OpenAI) as steps in a flow. This means a flow could, for instance, take meeting notes from OneNote and use an AI Builder action to summarize the notes into bullet points (utilizing an LLM), then post those to Teams. All of this is configurable with no code.

Power Virtual Agents (PVA): The chatbot builder in Power Platform received an upgrade allowing it to use GPT-based generative answers and even accept a website or document as its knowledge source (in preview as “GPT-powered bots”). This dramatically simplifies creating a bot: give it an FAQ page or handbook, and the LLM will draw answers from that text for user queries. Microsoft’s Copilot Studio and autonomous agents (released in early 2025) take this further, letting organizations compose more sophisticated multi-turn agents that can perform actions (via Power Automate) based on conversation – all guided by LLMs for language understanding (Power Apps Archive - Microsoft Power Platform Blog)53,

From an architecture standpoint, Microsoft’s integration uses Azure OpenAI Service, meaning the data and prompts flow through Microsoft’s enterprise-grade service rather than directly to a third-party. This is crucial for enterprise adoption, as it offers data residency, compliance, and security controls. For example, the Data Explorer Copilot in model-driven Power Apps (released 2025) lets users ask questions in natural language to filter and find records. When a user asks something like “Which customers from Canada purchased product X last year?”, the system sends that query to an LLM along with a schema of the data and security-filtered records. The LLM returns a filtered query result or an explanation, which the app then displays 33, The LLM is effectively translating user intent into a query (and possibly executing it), all within the governed environment of Power Platform.

Production Use Cases: By 2024, early adopters in enterprises were using Power Platform’s generative AI to speed up development of line-of-business apps. For instance, a large retailer used Power Apps Copilot to generate an inventory inspection app from a high-level spec in minutes, which previously would have taken days of manual work. In customer service departments, Power Virtual Agents with GPT have been deployed to handle common customer inquiries by drawing answers from product documentation – reducing call center load without extensive manual bot programming. Another concrete example is the Data Exploration Agent in Power Apps which enables business analysts to query CRM data by asking questions instead of writing filters, accelerating insights discovery (Power Apps Archive - Microsoft Power Platform Blog)33, These Copilot features illustrate how LLMs plug into the Power Platform at a fundamental level: they turn natural language into the “low-code” expressions (like Power Fx formulas or flow definitions), effectively acting as a new layer of abstraction on top of the platform’s components.

Microsoft’s approach shows the power of LLMs to democratize development even further: a citizen developer can build more complex solutions by collaborating with an AI that understands their intentions. It also showcases the importance of responsible AI: features like filtering prompts, ground-ing responses on business data, and allowing IT admins to manage Copilot usage are all in place to ensure the AI is helpful and not harmful.

🫧 Bubble – No-Code Meets Generative AI

Bubble, a no-code web app builder, has a slightly different approach to LLM integration. As of 2024/2025, Bubble doesn’t have a built-in AI copilot in its editor like Microsoft or Mendix, but it provides the flexibility to integrate any AI service through APIs and plugins. This has led the Bubble community to create numerous solutions that bring GPT-3/4 into Bubble apps without writing code. For example, Bubble’s plugin marketplace offers connectors to OpenAI’s GPT API that can be installed with a click. Once added, a Bubble developer (who doesn’t write traditional code) can configure workflows that send a prompt to the OpenAI API and receive the result – all through Bubble’s visual workflow editor. This means you can easily add features like “generate a blog post draft from an outline” or “chat with an AI support agent” into a Bubble app by invoking an LLM via a plugin.

A typical architecture for using an LLM in Bubble is: the Bubble app (running in the browser) makes a call to Bubble’s server (using the API connector or a plugin action), which then sends the request to the LLM’s API endpoint (like OpenAI). The response (e.g., the generated text) is returned to Bubble and can be shown in the UI or stored in the database. Bubble handles the authentication and formatting, so the app creator just needs to supply the API key and define when to call the AI (such as on a button click or when a page loads). Because of this straightforward integration, many startup founders in 2024 chose Bubble to build MVPs for AI-driven app ideas – they could focus on UI/UX while outsourcing the intelligence to GPT-4.

Use Cases in Bubble: Without writing code, creators built things like:

• AI Content Generation SaaS: Entrepreneurs used Bubble plus GPT-3 to launch products that generate marketing copy or social media content. The user enters some keywords, the Bubble app calls the GPT API, and the generated copy is displayed, all within a polished no-code frontend.

• Chatbot and Virtual Assistant Apps: Bubble apps have been created that let end-users chat with an AI to get recommendations (for travel plans, shopping, etc.). Bubble provides the web interface, user management, etc., while the LLM provides the conversational logic and answers.

• Data Analysis Tools: Some Bubble builders integrated LLMs to analyze uploaded data or text. For example, an app where a user uploads a PDF contract and the AI (via an API call) highlights key points or potential issues for them.

One production example (circa 2024) is an educational app built on Bubble that tutors students by having them chat with historical figures. The developer used a Bubble plugin to call an LLM with prompts engineered to mimic the style and knowledge of, say, Albert Einstein or Shakespeare, and the responses were then displayed in a chat interface built entirely with Bubble’s visual components. This kind of application shows the power of combining Bubble’s no-code frontend and user management with an LLM’s generative capabilities.

While Bubble may not have a native “Copilot” in its editor, the company has been improving support for AI integration. They have published guides on how to securely store API keys and manage costs when making frequent AI calls, which is crucial for no-code developers who might not be aware of API usage pitfalls. Moreover, Bubble’s logic can incorporate conditional rules, so app makers often add safeguards like rate-limiting (to control API cost) or prompt sanitization (to avoid inappropriate outputs) as part of their workflows.

In essence, Bubble demonstrates the flexibility of no-code platforms to ride the AI wave – even without in-house LLM features, the ecosystem empowers creators to plug in the best AI models for their needs. As generative AI APIs became widely available in 2024, Bubble developers were quick to embed them in all manner of applications, showing that with a bit of creativity, no-code tools can deliver AI-driven products indistinguishable from fully coded ones.

💡 Cost-Effective Strategies for Startups, Enterprises, and Small Teams

Leveraging LLM integrations in low-code/no-code platforms can incur costs (API usage, infrastructure, etc.), so different organizations have developed strategies to balance cost and performance:

• Startups: Startups often need to iterate quickly on a limited budget. A common strategy is to begin with third-party API-based LLMs (like OpenAI’s GPT-3.5) using pay-as-you-go plans. For instance, a startup building on Bubble or Retool might use GPT-3.5 (which is cheaper) during development and only switch to GPT-4 for production-critical prompts. They also take advantage of the platform features to limit usage – e.g. setting up prompts to be as efficient as possible (to reduce token count) and caching AI results. Using Retool’s ability to swap out models (Retool | Automate and speed up business processes with AI)-, a startup can test which model gives the best value for money and switch with minimal changes. Some startups even employ local open-source LLMs for certain tasks to avoid API costs – for example, hosting a small Llama 2 model on a server for basic text manipulations, and calling OpenAI’s API only for the tasks that truly need a powerful model. This hybrid approach keeps costs down. Also, low-code platforms themselves often provide free tiers or credits for AI features (Microsoft, for example, included some AI Builder credits in certain licenses in 2024), which startups make sure to utilize fully.

• Large Enterprises: Enterprises usually have bigger budgets but also greater scale and governance needs. They often opt for enterprise plans or self-hosting options. In the context of these four platforms: an enterprise using Power Platform will likely use Azure OpenAI with a negotiated pricing plan (possibly even running a dedicated instance of the model in Azure for data isolation). They might fine-tune models on their proprietary data to improve accuracy – an upfront cost that can pay off in better efficiency later. Enterprises also integrate LLMs with internal data lakes; for example, using Retool Vectors or Microsoft’s Dataverse + Copilot to ensure the AI is answering with up-to-date business data rather than general knowledge , To control costs, enterprises employ rate limits and monitoring – Power Platform’s admin center allows monitoring AI usage across the tenant, and enterprise features of Retool allow setting who can execute AI actions and set limits , Because large companies can negotiate contracts, they might go with a fixed-cost model (capacity licenses) for unlimited use up to a cap, preventing surprise bills. Architecturally, some enterprises even deploy open-source LLMs on-premise for certain sensitive tasks (using platforms like Mendix to call those models internally) – this trades higher upfront infrastructure cost for lower variable usage cost and data control.

• Resource-Constrained Teams: This category includes small teams or non-profits, internal teams at a company that have limited budget for new tech, etc. These teams focus on maximizing value from free or low-cost tools. They might use the free tiers of OpenAI (or trial credits) to prototype an LLM feature in a Bubble app, and then only enable it for important use-cases. Many will prefer using GPT-3.5 over GPT-4 due to cost, and will cleverly structure prompts to get decent outputs in one shot (to avoid iterative calls). In Power Platform, a small team might leverage the AI features that come included with their existing licenses (for example, using Power Apps Copilot in preview which might be free to test) rather than paying for custom AI. Caching and throttling are key: if an AI response is not user-specific, they’ll cache it (for example, if an AI generates a generic product description in a Mendix app, store it so subsequent users don’t trigger a new API call). Furthermore, these teams often take advantage of community-shared prompts and solutions – e.g. using pre-built Bubble plugins that implement best-practice prompts – so they don’t waste tokens on trial and error. They also stay flexible with providers: if a cheaper LLM API emerges, no-code integrations can be switched relatively easily. In Retool’s case, since it supports multiple providers or even custom endpoints, a small team could switch from OpenAI to an open-source API (like huggingface or an internal model) to save costs (Retool | Automate and speed up business processes with AI),

Across all these scenarios, an underlying principle for cost-effectiveness is monitoring and optimization. Low-code platforms are beginning to include usage analytics for AI features. For example, Retool’s dashboards can show how many AI calls were made by an app, helping the team identify overuse. By staying informed (e.g. which prompts are longest or which feature is calling the AI most), teams can iterate on prompts or logic to cut unnecessary tokens. Another strategy is setting up fallback behavior: if the AI is too expensive or rate-limited at a moment, the app can fall back to a simpler rule-based response. This kind of tiered approach ensures continuity at minimal cost.

In conclusion, integrating LLMs into low-code/no-code platforms has become a game-changer for building intelligent software quickly. Each platform – Mendix, Retool, Power Platform, and Bubble – offers a unique blend of capabilities and integration patterns, from in-editor copilots to drag-and-drop AI actions and plugin ecosystems. Armed with these tools and mindful cost strategies, teams in 2024 and 2025 are shipping AI-enhanced applications faster than ever, without breaking the bank or needing an army of AI specialists. The combination of low-code and LLMs is proving to be a powerful leveling force, enabling both startups and enterprises to innovate with AI at an unprecedented pace.

Sources: Mendix & Siemens documentation; Retool official blog and AI products (Retool | Automate and speed up business processes with AI)-; Microsoft Power Platform announcements (Power Apps Archive - Microsoft Power Platform Blog)-; Bubble integration guides; and industry case studies.