Comparison: Robomotion vs Make

Make (formerly Integromat, now backed by Celonis) is a polished, cloud-only no-code integration platform - an iPaaS - for connecting SaaS apps and APIs through a beautiful visual scenario builder. It has the largest app-connector catalog in this group (3,000-plus), an approachable drag-and-drop experience, and as of 2025 a strong on-canvas agentic layer (Make AI Agents and Make Grid). It is excellent for SaaS-to-SaaS workflow automation and fast to start with.

Robomotion is an Agentic Automation Platform that combines AI agents, code-running skills, and drag-and-drop RPA on one visual canvas, runs robots on Windows, macOS, Linux, and Raspberry Pi, deploys in cloud, hybrid, or fully on-prem, and runs on a Go concurrency engine that lets one flow process hundreds of requests in parallel.

The fundamental difference is what each platform can touch and where it runs. Make connects cloud APIs from inside its own cloud; it has no UI or desktop automation, no robots on your machines, and no self-hosted option. Robomotion does real RPA - it drives the browser, Windows, Java, and any on-screen UI (including apps with no API), on robots you run locally, on-prem, or in the cloud - and adds native agents with sandboxed code skills. If all you will ever need is to wire cloud apps together over APIs, Make does that well. But that is the narrow case: most real automation eventually has to touch software with no API, run on your own infrastructure, scale past what per-operation billing makes affordable, or give agents safe access to code and credentials. Robomotion does all of that and the SaaS-glue Make is known for, on one platform. For anything beyond simple cloud connectors, Robomotion is the stronger and more future-proof choice.

Why this comparison matters in 2026

In 2026 the line between "workflow automation" and "AI agents" has blurred for everyone. Make now builds and runs AI agents on the same canvas as its scenarios, and Robomotion puts agents on the same canvas as its RPA flows. So the pitches rhyme. The real question is not "does it have agents" - both do - but "what can the agent actually act on, and where does it run."

Make's answer is the cloud API world: 3,000-plus connectors, all executed inside Make's hosted infrastructure. Robomotion's answer is broader: cloud APIs plus the messy real world of browsers, desktops, legacy apps, and pixels, executed on robots you control across any operating system. This guide compares the two dimension by dimension, and it is honest about where Make leads.

Strategic vision and core philosophy

Make is an integration-first, no-code platform. Its purpose is to connect applications and move data between them with the least friction possible, expressed as visual "scenarios" of "modules." The philosophy is accessibility and breadth: make it beautiful, make it approachable for non-developers, and connect to everything via a vast catalog of maintained connectors plus a universal HTTP module. The 2025 to 2026 direction layers agentic AI on top of that integration fabric so agents can orchestrate across the same 3,000-plus apps.

Robomotion is an automation-first, agentic platform. Its purpose is to do the work on real systems, not just shuttle data between SaaS APIs. The philosophy is composition and reach: put an AI agent, free-form code skills, and deterministic RPA on one canvas; let the agent call your existing automations as tools; record any UI that has no API; and run all of it concurrently on robots that live wherever the work is.

Put simply: Make connects apps; Robomotion operates systems. Both now add agents, but they sit on very different foundations.

Platform architecture and deployment models

Make is a pure cloud SaaS. Everything runs inside Make's own infrastructure (AWS, in EU and US regions), and there is no on-premise or self-hosted option. The Enterprise plan runs in an isolated AWS environment, and the platform carries ISO 27001, SOC 2 Type II, and GDPR credentials. The building blocks are:

Scenarios - an automation workflow, a chain of modules that pass data from a trigger through actions.
Modules - individual steps, each a specific action in an app (watch for changes, search records, update fields).
Operations (now credits) - the billing unit, counted each time a module processes a bundle of data.
Logic tools - routers (branching), filters (conditions), iterators and aggregators (arrays), and error handlers.

It is elegant and zero-maintenance, but it means your automations only run where Make runs, and your data flows through Make's cloud.

Robomotion separates orchestration from execution, and the execution runs wherever you put it. The control plane (Designer plus Admin Console) manages flows, robots, schedules, triggers, queues, vaults, and agents; the work is executed by robots (the deskbot runtime) that connect outbound to the workspace. The same robot binary runs in cloud, hybrid, and fully on-prem configurations. For regulated industries, private networks, and data-residency requirements, the ability to keep both orchestration and execution entirely on your own infrastructure is a structural difference Make cannot match.

Execution model: where and how automations run

This is the heart of the comparison.

Make executes every scenario inside its own cloud. There is no concept of a robot on a machine. That is exactly what makes it effortless - nothing to install, nothing to maintain - but it is also the hard ceiling: Make can only reach what is reachable from its cloud over an API or webhook. It cannot click a button in a desktop application, log into a Windows program, read a screen, or drive a browser session on a real machine.

Robomotion executes on robots that you run. A robot is a single self-contained binary with full access to its environment - private networks, local files, installed applications, and the browser. It comes in three types: Development (build and debug), Production (unattended, always-on, for schedules, webhooks, and queues), and Application (a headless robot that runs a hired AI agent). Because the robot lives on a real machine, it can do everything Make does (call APIs, move data) and everything Make cannot (operate real software through its UI).

Platform independence and runtime environments

Make runs in Make's cloud only. Your team accesses it through a browser; there is no local runtime and no choice of operating system, because there is no execution on your side at all.

Robomotion robots run natively on Windows, macOS, Linux, and Raspberry Pi, with the same binary and the same flows everywhere. That enables headless Linux web and API automation, macOS-native tasks, Windows desktop automation, and even edge and IoT scenarios. Combined with cloud, on-prem, and hybrid deployment, Robomotion gives you control over exactly where each automation executes. Make trades that control for zero infrastructure - a fair trade for some teams, a dealbreaker for others.

Development experience

Make has arguably the most approachable visual builder in the category. The canvas is polished and friendly, modules snap together intuitively, routers and filters make branching visual, and non-developers can be productive quickly. The HTTP and webhook modules provide an escape hatch to any REST API. The trade-off is depth: complex logic can become unwieldy on the visual canvas, custom code is limited, and you are largely working within Make's hosted model and connector definitions.

Robomotion is also drag-and-drop, but it compiles to a TypeScript SDK underneath, so power users can read and edit the exact same flow as code. Three ways to build: drag nodes by hand, record a UI by point-and-click, or describe what you want and let the AI Assistant build the flow on the canvas. A scope system on every node input (msg, Custom, JS, AI, variable, or credential) means setting an input to AI scope turns any node into an agent tool with no glue code. Flows are stored as git repositories with full commit history, named versions, branches, and import/export to your own Git - real version control, where Make offers scenario versioning and execution logs but not git-grade history.

Processing model, concurrency, and throughput

Make bills and scales by operations (recently rebranded credits): every module execution consumes one, and your plan defines how many you get and how many scenarios can run concurrently. Throughput therefore has two governors - the operations you have purchased and your plan's concurrency limits. High-volume workloads consume operations quickly, and the cost grows with the volume of work, not just the value of it. There is no goroutine-style parallelism inside a single scenario run beyond routers and the platform's own scheduling.

Robomotion runs concurrency inside a single flow on a single robot, because the robot is a Go runtime built on goroutines. Two patterns compose:

Request-level concurrency. A flow behind a Webhook treats each inbound request as its own isolated message with its own response channel, so hundreds of callers means hundreds of messages flowing through the same flow in parallel, on one robot.
Fork Branch + WaitGroup. A single run can fan out into N parallel branches and synchronize cleanly before continuing, mapping directly onto Go's sync.WaitGroup.

The practical effect: Robomotion does not meter you per operation, and it does not make you buy concurrency by the scenario. One flow can become a genuine high-throughput API backend on a single robot. For bulk processing and high-traffic webhook workloads, that is both a performance and a cost advantage.

AI and agentic automation

Credit where it is due: Make's 2025 agentic release is strong. Make AI Agents are built, run, and debugged on the same canvas as scenarios, can orchestrate across 3,000-plus apps, are multi-modal (PDFs, images, CSVs as inputs and outputs), and keep every decision visible and reviewable. Make Grid auto-generates a visual map of your whole AI and automation landscape, and custom AI provider connections are available on paid plans. For a team whose world is SaaS APIs, this is a capable, well-funded agentic layer with excellent transparency.

Robomotion's agentic model is differentiated by architecture, openness, and what the agent can act on:

Two interchangeable engines - Hermes Agent (a self-improving agent with a deep built-in toolset, filesystem skills, and persistent memory) and ADK Agent (Google's Agent Development Kit for deterministic multi-agent orchestration: Sequential, Parallel, Loop).
Any model, or none of your own. OpenAI, Anthropic, Gemini, xAI, DeepSeek, and dozens more via OpenRouter, or "Use Robomotion Credits" to run with no API key at all.
Your RPA flows become the agent's tools. A Tool In -> [any Robomotion flow] -> Tool Out chain turns any automation - including a recorded browser, Windows, Java, or image automation - into a callable function. The agent can therefore act on systems with no API, which a cloud iPaaS agent cannot.
Visual governance. A callbacks port and a Tool Approve node let you inspect, approve, deny, or rewrite any tool call before it runs, plus human-in-the-loop approvals.

Both platforms give you transparent, on-canvas agents. The difference is reach: Make's agents orchestrate cloud apps; Robomotion's agents can also drive the desktop, the browser, and legacy software, and run code skills safely.

Extensibility: connectors, code, skills, and tools

Make leads on raw connector count. With 3,000-plus apps (1,000-plus maintained by Make's own team) plus a universal HTTP module, it is hard to beat for breadth of SaaS coverage. If your automation is "connect these twelve cloud tools," Make almost certainly has every connector you need out of the box. This is a genuine and important strength.

Robomotion extends at more levels, with real code. It ships 220 packages spanning AI and LLMs, UI automation, communication, databases, SaaS, files, and data transforms - fewer connectors than Make, but each is a full node family. Beyond that:

Four languages for writing your own packages - Go, Python, Java, or .NET.
Skills - packaged directories of SKILL.md plus free-form Python and shell scripts, browsed and installed from an in-Designer marketplace.
MCP tools - external Model Context Protocol servers wired straight onto the agent.
Sub-agents - agents delegating to specialist agents.

So Make wins on the number of prebuilt SaaS connectors; Robomotion wins on depth of extensibility and the ability to run real, arbitrary code as part of an automation.

UI automation and recorders: the RPA gap

This is the single biggest capability difference, and it is the entire reason RPA exists.

Make has no UI or desktop automation. It connects cloud APIs only. There is no browser-click automation, no Windows or Java desktop automation, no image or OCR automation, and no recorders. Any application without an API is out of reach.

Robomotion's Universal Inspector captures automation by point-and-click in four production modes - Web, Windows, Java, and Image - and the Image recorder uses template matching and OCR to drive apps with no accessible UI at all (Citrix, RDP, custom-drawn screens). A business user records a task once, it becomes a reliable subflow, and an agent can call that subflow as a tool. For any organization with legacy ERPs, banking portals, desktop applications, or any software that does not expose a clean API, this is the difference between "automatable" and "not."

Human and system front-ends: Forms, Chat, and Webhooks

Robomotion ships three built-in ways to connect people and systems to an automation:

Robomotion Forms - a drag-and-drop (and AI-generated) form builder with 2,500-plus templates, end-to-end encrypted submissions, and a direct pipe from every submission into a flow.
Robomotion Chat Assistant - a real-time chat over any flow, in Guided mode (the flow drives with structured widgets) or Conversational mode (an LLM-backed agent).
Robomotion Webhooks - every flow can expose a public HTTPS endpoint with no servers and no open inbound ports, because the robot dials out over a secure reverse tunnel.

Make supports inbound and outbound webhooks and can integrate third-party form apps from its catalog, but it does not ship an equivalent built-in, branded forms builder and a conversational chat layer that front arbitrary automations out of the box.

Security, governance, and credential handling

Make is a mature, certified SaaS: ISO 27001, SOC 2 Type II, SOC 3, GDPR, EU and US data regions, and an isolated environment for Enterprise customers. Credentials (connections) are stored and managed by Make in its cloud. For teams comfortable with a hosted model, this is solid.

Robomotion matches the fundamentals and keeps secrets off the vendor's servers entirely. Vaults use client-side encryption (Secure Remote Password), so the server never sees plaintext; secrets are wrapped per-robot, and only the robot that runs the flow can decrypt them. On top of that come two innovations for the agentic era:

Sandboxed code skills. Free-form agent code runs in an isolated, rootless container with CPU, memory, PID, and timeout caps, a locked-down filesystem, narrow per-flow mounts, and network controls. One ephemeral sandbox per run.
The credential proxy. Agent code never receives real secrets - it gets opaque placeholders, and the robot substitutes the real value into the outbound request just-in-time. A prompt-injection attack that dumps the environment walks away with useless placeholders.

The distinction: Make secures a hosted multi-tenant cloud well; Robomotion lets you keep secrets and execution on your own infrastructure, and hardens the specific risk of agents running code with real credentials.

Ecosystem, marketplace, and support

An honest point in Make's favor: as a leading iPaaS with strong brand recognition and the backing of Celonis, Make has a very large ecosystem - the biggest connector catalog in this comparison, a huge template gallery, an active community, and broad awareness among SMB and no-code audiences. For a team that wants to connect popular SaaS tools fast and lean on a large library of ready-made scenarios, that ecosystem is a real advantage.

Robomotion's ecosystem is growing rather than established. It ships an Agent Hub marketplace of hireable agents, an Agent Teams collaboration workspace, a Developer Program where publishers earn credits when their agents are used, examples and templates, and 220 packages - but the connector count, template volume, and brand footprint in the no-code category are smaller than Make's.

Total cost of ownership and licensing reality

Make prices by operations (credits) on tiered plans: a Free plan (1,000 credits per month, 2 active scenarios, 15-minute minimum interval), Core (around 12 USD per month billed annually, 10,000 credits, unlimited scenarios, 1-minute interval), Pro (around 21 USD per month, priority execution and log search), Teams (around 299 USD per month), and custom Enterprise pricing. Entry cost is low and onboarding is fast, but the model has a well-known characteristic: cost scales with the volume of operations, so high-throughput or high-frequency automations can become expensive, and a late-2025 change added a 25 percent markup on extra-operation packs.

Robomotion uses transparent, published tiers: Free (0 USD), Solo (49 USD per month), Pro (129 USD per month), and Team (399 USD per month), with self-hosted Starter (499 USD per month) and Growth (999 USD per month) options, and AI usage metered in credits at 1 credit = 0.01 USD. Crucially, Robomotion does not bill per operation or per module execution, and concurrency lives inside the flow, so reaching high throughput does not multiply your bill the way an operations-metered model does. For low-volume SaaS glue, Make's entry pricing is very attractive; for high-volume or always-on workloads, Robomotion's model is typically far cheaper at scale.

Capability matrix at a glance

Capability	Make	Robomotion
Deployment	Cloud-only SaaS (no on-prem/self-host)	Cloud, hybrid, fully on-prem (same binary)
Where automations run	Make's cloud only	Robots you run (local / on-prem / cloud)
Robot OS support	None (no local runtime)	Windows, macOS, Linux, Raspberry Pi
Desktop / UI automation	None	Strong (Web, Windows, Java, Image)
Point-and-click recorders	None	Yes (web / Windows / Java / image-OCR)
Automate apps with no API	No	Yes (image + OCR)
SaaS / API connectors	3,000-plus (largest here)	220 packages + HTTP/any API
In-flow concurrency	Operations + plan concurrency limits	Goroutines + Fork Branch (no per-op tax)
Native AI agents	Yes (Make AI Agents + Grid)	Yes (Hermes / ADK + sandbox + credential proxy)
Model choice	Custom AI providers on paid plans	OpenAI, Anthropic, Gemini, xAI, DeepSeek, OpenRouter, or Robomotion Credits
Run free-form code skills safely	Limited custom code	Sandboxed Python/shell + credential proxy
Extend in real code	Limited	Go, Python, Java, .NET
Built-in Forms + Chat front-ends	No native forms builder	Forms + Chat Assistant + Webhooks
Git-backed version control	Scenario versioning + logs	Yes (per-flow git)
Pricing model	Per-operation (credits), tiered	Transparent tiers; no per-operation tax
Ecosystem / brand (no-code)	Very large	Growing

When each platform is the better fit

Choose Make when:

Your automation is essentially SaaS-to-SaaS over APIs and webhooks.
You want the fastest possible no-code start with zero infrastructure to maintain.
The breadth of the connector catalog (3,000-plus) and a huge template library are decisive.
Non-technical team members need an approachable, beautiful visual builder.
Your volumes are modest enough that operations-based pricing stays comfortable.

Choose Robomotion when:

You need to automate real software - browsers, desktop apps, Windows, Java, or any UI, including apps with no API.
You must run on your own infrastructure (on-prem or private networks) or across Linux, macOS, or Raspberry Pi.
High-throughput or always-on workloads would make operations-based billing expensive.
You want native AI agents that can safely run free-form code skills with real credentials, on any LLM provider or with no key of your own.
You want built-in Forms, Chat, and Webhook front-ends, git-backed version control, and execution you fully control.

Conclusion

Make is an excellent cloud iPaaS: the largest connector catalog in this comparison, a beautifully approachable no-code builder, fast onboarding, a strong template ecosystem, and a capable 2025-era agentic layer in Make AI Agents and Make Grid. For SaaS-to-SaaS integration and quick no-code automation, it is a top choice, and for low volumes its entry pricing is hard to beat.

Robomotion competes on a fundamentally larger surface. It does real RPA - driving the browser, the desktop, Java, and any on-screen UI, including software with no API - on robots that run across Windows, macOS, Linux, and Raspberry Pi, deployed in cloud, hybrid, or fully on-prem. It puts AI agents, sandboxed code skills, and that RPA on one canvas; achieves true concurrency inside a single flow without a per-operation tax; protects secrets with client-side encryption and a credential proxy; and fronts every automation with Forms, Chat, and Webhooks.

The honest summary: Make leads on connector breadth, no-code polish, and SaaS-integration speed; Robomotion leads on real UI automation, deployment control, safe agentic code skills, concurrency economics, and built-in front-ends. But notice the asymmetry - Robomotion can do the cloud-API work Make does, while Make cannot touch the real-software world Robomotion automates. If you are choosing one platform to build on, choose the one that covers both. Choose Robomotion.

See the difference for yourself. Robomotion is free to start: connect your apps, record a real UI, and run an agent on your own robot - on any OS, in the cloud or fully on-prem, with no per-operation tax.

Start building at robomotion.io.