Make vs Latenode: 2025 No-Code Workflow Automation Showdown

Introduction

The automation landscape has shifted dramatically. In 2024, the goal was simply connecting App A to App B; in 2025, the focus is on building autonomous "Agentic" workflows that can reason, scrape, and decide. This shift has forced power users to re-evaluate their stacks, leading many to the Make vs Latenode debate.

While Make (formerly Integromat) remains a dominant force for visual integration, its "per-operation" pricing model often punishes efficiency when scaling. Enter Latenode: a challenger platform designed for the AI era, prioritizing compute time over operation counts and offering native code flexibility. In this comparison, we will dissect which tool offers the best value, power, and scalability for modern automation needs.

The 2025 Automation Landscape: Operations vs. Intelligence

For years, the industry standard metric for billing has been the "operation" or "task." Every time data moves from one node to another, you pay. However, as workflows evolve from linear pipes into complex, looping AI agents, this model is becoming prohibitively expensive.

Users are increasingly looking for platforms that handle high-volume data and AI reasoning natively. The friction points are clear:

Operation Counting: Complex scenarios with iterators burn through credits rapidly.
Billing Fragmentation: Users are tired of managing separate bills for their automation tool, OpenAI, Anthropic, and scraping services.
Visual Spaghetti: Large workflows in purely visual editors become difficult to manage and debug.

Latenode positions itself here not just as an alternative, but as a paradigm shift—moving from counting steps to measuring compute power.

Pricing Models Compared: The Cost of Scaling

The most critical differentiator between these platforms is how they define "work." Make utilizes an operations-based model, whereas Latenode utilizes a time-based compute model.

The "Per-Operation" Trap (Make):
In Make, if you retrieve 1,000 rows from a database and iterate through them to update a CRM, that is generally counted as 1,000+ operations. A single run of this workflow could consume 10% of a basic monthly plan.

The "Compute Time" Advantage (Latenode):
Latenode charges a "credit" for every 30 seconds of execution time, regardless of how many steps occur within that window. If that same 1,000-row update takes 5 seconds to process via a script, it costs a fraction of a single credit (roughly $0.0019 on the Start plan). This fundamental difference makes scaling data-heavy workflows significantly cheaper.

For a visual breakdown of how these models diverge in real-world scenarios, you can watch our analysis of the top differences between Make.com and Latenode.

Understanding Latenode's Credit System

Latenode's model is designed for efficiency. A standard HTTP request or AI analysis often finishes in milliseconds. Because you are billed for the aggregate load rather than the step count, high-volume polling triggers or complex loops don't incur the heavy penalties found in other tools.

This structure allows for massive scalability on lower-tier plans. To understand specifically how does latenode's pricing model save costs for local package management and heavy data processing, it helps to view the platform as a serverless infrastructure rather than just a connector tool.

Hidden Costs: Native AI vs. BYO Key

Cost comparison isn't just about the platform subscription—it's about the total cost of ownership (TCO).

Make: You pay your monthly subscription fee plus your own usage fees for OpenAI or Anthropic API keys.
Latenode: Access to 400+ AI models is included. You can use GPT-4, Claude 3.5 Sonnet, or DeepSeek without connecting an external wallet, as the cost is covered by your subscription credits.

AI Capabilities: Integrations vs. Native Infrastructure

Make has adapted to the AI boom by adding robust connectors (modules) for major AI providers. It acts as an orchestrator—sending data out to an AI and waiting for a response. This works well for simple tasks but can feel disjointed for building autonomous agents.

Latenode treats AI as infrastructure. The platform is architected to host multi-agent systems where memory (RAG) and context are managed natively within the environment.

Building AI Agents Without API Keys

Latenode's "Universal AI Node" simplifies the development stack. Instead of managing rate limits and authentication for five different AI providers, you simply select your desired model from a dropdown menu. The platform handles the routing and availability.

For developers looking to build sophisticated agents that can remember past interactions or reference uploaded documents, Latenode provides a complete setup guide for AI agents using frameworks like LangChain directly within the node structure.

Latenode's AI Copilot Feature

While Make has introduced AI assistants to help design workflows, Latenode's AI Copilot goes a step further by bridging the gap to code. You can ask the Copilot to "Create a JavaScript node that filters this JSON array for customers who spent over $500 and format it for Slack."

The Copilot writes, debugs, and inserts the functional code instantly. This allows non-coders to leverage the efficiency of low-code modules without learning syntax manually.

Developer Experience: No-Code vs. Low-Code Flexibility

Every no-code developer eventually hits a wall: a missing integration, a weird data format, or a complex logic requirement that would take 20 visual nodes to solve.

JavaScript and NPM Package Support

Make offers limited options for custom logic, often requiring users to string together multiple text parsers and array aggregators. Latenode offers a full Node.js environment.

If you need to use a specific cryptography library or a niche SDK, you can import standard NPM packages directly into your workflow. This capability allows you to build an integration with any app even if a native "connector" doesn't exist yet, simply by utilizing HTTP requests or standard libraries in a code node.

Headless Browser Capabilities

One of Latenode's most distinct features is the built-in Headless Browser. In Make, scraping data from a dynamic website usually requires paying for a third-party service like Apify or ScrapingBee.

Latenode allows you to automate web tasks without APIs natively. You can instruct the workflow to launch a browser instance, navigate to a URL, click buttons, and extract text—all within the same credit-based pricing model.

This is particularly powerful for headless browsers for web scraping, enabling you to monitor competitor pricing or grab data from legacy internal tools that lack modern APIs.

Feature Comparison Table

To help visualize the trade-offs, here is a direct comparison of the platform architectures. For a broader context on market costs, we've also conducted a deep pricing analysis for workflow automation.

Feature	Make (Integromat)	Latenode
Pricing Model	Per Operation (Action)	Compute Time (30s block)
AI Integration	Connector (BYO Key)	Native (Included Credits)
Code Support	Restricted / Custom Apps	Full Node.js + NPM Support
Web Scraping	Requires 3rd Party Apps	Native Headless Browser
Wait Steps	Can consume resources	Efficient webhook/sleep handling
Looping Costs	Expensive (1 credit per item)	Cheap (Time-based processing)

Migration Assessment: Should You Switch?

Make is an incredible platform with a massive library of pre-built modules. It is not always the right choice to migrate, but for specific use cases, the ROI of switching is undeniable.

Stay on Make if:

You have legacy workflows running smoothly without high costs.
You rely heavily on a specific, niche app integration that Make supports natively, and you are uncomfortable using HTTP modules.

Switch to Latenode if:

Your Make bill is increasing due to operation counts on simple loops.
You are building AI agents and want to avoid managing external API keys.
You need to scrape data or interact with websites directly.
You want the power to master real-time data integration using custom code logic that replaces dozens of visual nodes.

Moving from Scenarios to Nodes

Transitioning is straightforward because both platforms share the "visual canvas" DNA. Latenode's linear and branching logic will feel familiar to Make users. The primary mental shift is realizing you don't need to "save" operations—you can be as granular as you like, because you are only paying for the seconds the server works.

Frequently Asked Questions

Is Latenode cheaper than Make?

Generally, yes, especially for heavy workflows. Because Latenode charges for compute time rather than steps, a loop processing 500 data items costs fractions of a cent on Latenode, whereas it would consume 500+ operations on Make.

Can I use Latenode without knowing how to code?

Absolutely. The visual builder covers the vast majority of automation needs. For complex requirements, the built-in AI Copilot can write the necessary JavaScript for you, effectively bridging the no-code to low-code gap.

Do I need my own OpenAI API key for Latenode?

No. Latenode acts as the provider. You can access models like GPT-4 and Claude directly using your subscription credits. However, you still have the option to use your own keys if you prefer custom billing arrangements with AI providers.

Does Latenode have as many integrations as Make?

While Make has a larger library of pre-built "logo" buttons, Latenode connects to any service with a REST API via its HTTP component or AI-generated integration code. This gives it virtually unlimited connectivity, even if a dedicated icon doesn't exist yet.

How does the free plan compare?

Latenode offers a generous free tier that allows access to all core features, including the headless browser and AI models. The credit allocation is sufficient for building and testing complex scenarios before needing to upgrade for higher volume.

Conclusion

In the Make vs Latenode comparison, the winner depends on your trajectory. Make remains a standard for purely visual integration across a vast ecosystem of apps. However, Latenode has emerged as the modern choice for 2025, specifically engineered for the demands of AI automation and high-volume data processing.

By shifting from "operation counting" to "compute time" and embedding a full coding environment within a visual builder, Latenode solves the scalability and cost issues that plague scaling businesses. If you are ready to stop rationing your operations and start building intelligent agents, the transition is easier than you think.