Make Alternative for Building AI Agents

Searching for a Make alternative usually starts from a concrete goal: you want an AI agent or assistant that knows your business, calls tools, and gets real work done - and you are weighing whether Make is the right home to build it in. It is a fair question, because Make now ships AI modules and visual AI agents, and ChatBotKit builds agents too. Both connect to the major model providers, hand the model real tools, and chain several steps into an outcome.

The difference is what each product is organized around. Make is a visual scenario platform - the direct descendant of Integromat - where you drag modules onto a canvas, connect them, and map exactly how each bundle of data flows from one app to the next. The scenario is the artifact, and the AI is a module inside it. ChatBotKit is organized around the autonomous, conversational agent - you give it a goal, knowledge, and tools, and it works out which tool to reach for and in what order, looping until the job is done, then lives on as a conversation your users can have on any channel. Make can now place an agent on its canvas, and ChatBotKit can run scheduled, structured work of its own - but the centers of gravity differ: Make pulls toward moving and transforming data between apps, ChatBotKit toward the agent that reasons and talks. What follows is an honest look at where each one earns its place.

What Make Does Well

Make is a mature, capable automation platform, and its strengths are real:

• A genuinely powerful visual canvas - the scenario builder gives you routers, filters, iterators, and aggregators for branching, looping, and reshaping data with a precision few automation tools match.
• Granular data mapping - you control exactly how each field in a bundle maps into the next module, step by step.
• A deep catalogue of app modules - a large library of pre-built connectors for SaaS tools, databases, and APIs, plus a generic HTTP module for anything without one.
• Managed cloud - Make runs as a hosted service, so there is no core platform for you to operate.
• Consumption-based metering - you pay for the modules that actually run, which can be efficient for high-volume app-to-app plumbing.
• AI modules and visual agents - AI modules, an MCP server, and agents that now sit as first-class citizens inside the scenario builder.
• An OEM white-label program - enterprise partners can run a rebranded instance across multiple client organizations.

If your core need is automating backend processes and connecting apps - and you like working the data flow yourself, module by module - Make is a strong choice.

Where ChatBotKit Is Different

Either platform can produce an AI agent of some kind. The differences below are the ones that start to matter when your real objective is to launch and grow agents - not to keep a data pipeline flowing.

The Agent Decides the Path; a Scenario Draws It

This is where the two products part ways most sharply. In Make the unit of work is the scenario - a canvas of modules connected into the exact route you lay out ahead of time, each one handing a bundle to the next. That is precisely what you want for a structured, repeatable job. In ChatBotKit the unit of work is the autonomous agent - a runtime, or harness, that you hand a goal, knowledge, and tools, and that then decides which tool to call and in what sequence, looping until the work is finished. You describe the outcome you want, not each module along the way. Make can drop an agent onto the canvas, and ChatBotKit can run fixed, deterministic paths through Blueprints and Tasks - but if your problem is open-ended rather than a mapped pipeline, a harness fits it better than a diagram of modules.

Observable While It Adapts, Not Just Transparent About a Fixed Route

Make leads its agent story with transparency. It names the "black box problem" and offers visual execution logs and reasoning panels as the trustworthy answer, casting a hand-drawn canvas as the safe way to adopt AI. The concern is fair, and a drawn scenario is certainly legible - but transparency and rigidity are not the same thing. A scenario is transparent about a route you fixed in advance; the entire point of an agent is to handle the route nobody drew. ChatBotKit makes that adaptive behavior just as inspectable: a millisecond-precision trace debugger reads every step, tool call, and model response, while guardrails, structured tools, and policies hold behavior inside the lines, with human review wherever you want it. And when you genuinely want a fixed, deterministic path, Blueprints and Tasks hand you one on the same platform. You get an agent you can watch and constrain without freezing it into a diagram.

A Conversation Across Channels, Not a Channel Module Per App

Reaching a person from a Make scenario means starting at a trigger and passing through the app module for whichever channel you have in mind - with the credentials, the session, and every turn of the exchange left for you to wire up, channel by channel. A ChatBotKit agent is built to converse from the outset, and it shows up where your users already spend their time - an embeddable web widget, WhatsApp, Slack, Telegram, Messenger, Instagram, Google Chat, Microsoft Teams, email, and SMS and phone-call voice via Twilio - together with realtime voice, lifelike avatars, and live meeting participation in Zoom, Google Meet, and Teams. A single agent configuration covers all of them and pours into one unified Inbox, holding memory and context from one session to the next rather than rebuilding state inside a scenario on every run. And a channel is never just a text relay: agents open file attachments, accept voice and video input natively in places like Slack and the web widget, drop into live meetings, reply as email agents, and place and take calls over telephony.

Managed Knowledge and Real Actions, Not Modules You Wire and Meter

Grounding an agent in your own data on Make means building a retrieval flow from modules - an embedder here, a vector store there, a search step - each of which meters every time it fires. ChatBotKit gives you managed knowledge from day one: semantic datasets with built-in document processing, second-pass reranking, crawling that copes with JavaScript-heavy sites, Notion sync, and lasting memory across sessions - and not a single vector store for you to run. When it comes to actions, the agent reaches for an extensive set of pre-built ability templates and custom API abilities, executes Python, JavaScript, and shell inside isolated, ephemeral sandboxes, runs agentic SQL over HubSpot, Postgres, and spreadsheets, steers a headless browser, and talks both directions of MCP - consuming any server and publishing your own skillsets as tools. Which one it uses is the agent's call, not a route you laid out module by module.

Beyond the Chat Box - Voice, Avatars, and Coding Agents

A ChatBotKit agent is not confined to a text bubble. One agent definition - the same knowledge, the same abilities - can take the shape of a coding agent that operates in your shell or CI with local file and command access, a realtime voice or telephony system fielding live, low-latency calls over Twilio, a lifelike avatar that gives the agent a face on screen, or a research agent, a form-filler, and plenty more. Because Make is built for wiring apps to apps, standing up voice, telephony, an avatar, or a local coding agent there is extra plumbing at best and off-map at worst.

Your Models, Keys, and Connections Stay Yours

The models and the credentials remain under your control. ChatBotKit supports a wide field of model providers and lets you change the model behind any agent without rebuilding it, and bring-your-own model API keys means usage lands on your own provider accounts at your own rates. Your secrets and authentication credentials sit on the platform in your hands, and your own OAuth connections let an agent reach third-party services as your apps, with your permissions - not through some shared, opaque account.

Both Managed - but You Choose Where It Runs

Make and ChatBotKit are both hosted services, so neither asks you to operate the core platform - a genuine point in common. The split shows up when data has to stay inside your walls. Make is a cloud SaaS; its on-prem story is an enterprise connector agent for reaching internal systems, not a way to host the platform itself. ChatBotKit gives you the deployment choice: use the managed cloud, or deploy into your own cloud account (your AWS, Azure, or GCP VPC, under your IAM), a private data center, or a fully air-gapped network with self-hosted models on your GPUs. Your data never leaves your perimeter and the keys stay with you - we supply the software, containerized and reproducible. Keeping data local does not force you off a managed platform.

One Platform, Not a Metered Stack of Modules

Standing up production agents on your own usually means welding a stack together - a model gateway, a vector database, a RAG pipeline, a code sandbox, channel connectors, an observability tool, a cost tracker, a PII/DLP layer, a secrets and auth manager, and a branded front end - each one licensed, integrated, and scaled by you. ChatBotKit folds all of it into one platform on one bill: security and compliance, cost control, and observability are built in rather than bolted on, and your data stays yours - ChatBotKit does not train on it and opts into zero data retention with the model providers it calls. On Make, that same layer is stitched from modules and add-ons, and every module that runs meters against your plan - so the more complete the system, the more there is to assemble and pay per step.

Portable, Not Locked In

Going with a managed platform should not weld the door shut behind you. ChatBotKit is deliberately portable: a full API and SDKs move agents and data either way, an OpenAI-compatible endpoint keeps your code off a proprietary interface, your own model keys travel with you, and on-prem deployment is there if you ever want to take the whole thing in-house. Everything you create - knowledge, conversations, configuration - exports without friction, and our team will help you migrate in or out by hand. The reason to stay is that ChatBotKit runs your agents best, not that the way out is blocked.

A Complete Platform, Not Just a Chatbot Builder

The pieces you would string across a Make scenario to give an agent a brain, a memory, tools, and a way to reach people all live inside one platform here - together with the rest of what shipping to production actually demands. Here is what comes standard with ChatBotKit.

Agents That Take Real Actions

• A library of pre-built ability templates alongside custom API abilities, bundled into skillsets the agent can switch on or off as a conversation moves.
• Secure sandboxed execution of Python, JavaScript, and shell in throwaway, isolated environments with no line back to your infrastructure.
• Agentic SQL that converts a plain-language question into a query over HubSpot, Supabase/PostgreSQL, and CSV, Excel, or JSON.
• Headless browsing, web search, vision, image and video generation, and audio and video transcription.

Managed Knowledge (RAG)

• Semantic datasets assembled from PDFs, documents, and spreadsheets, refined by second-pass reranking, topped up by crawling JavaScript-heavy sites and live Notion sync - with no vector store left for you to operate.
• Persistent memory that carries context between sessions - scoped to a contact, a bot, or the whole platform - and retrievable by meaning.

Multi-Agent, on the Platform

• Native bot-to-bot hand-off, visual Blueprints that wire agents, datasets, and skillsets into a running system, shared Spaces that pool knowledge, and cron-driven autonomous Tasks - none of it needing a separate orchestration engine.
• A Community Hub where you publish and clone blueprints, skillsets, datasets, and widgets - so you begin from something rather than nothing.

Enterprise-Grade Governance and Observability

• Reversible-token PII redaction, audit trails, retention and usage policies that apply themselves, EU data residency, and SSO on every tier.
• Observability across the stack - performance analytics, token-level usage and cost, event monitoring, and a trace debugger precise to the millisecond.
• Multi-tenancy and white-label - isolated parent-child sub-accounts through the Partner API, and branded Portals on your own domains.

Both Sides of MCP

• Call out to any MCP server from within an agent, and expose your own skillsets as MCP tools for outside clients - Claude Desktop, IDEs, your own apps - to pick up.

ChatBotKit vs Make at a Glance

Pricing: Priced Around the Agent, Not the Module

The clearest way the two products differ is on the invoice.

Make meters consumption per module. Every module that runs in a scenario - a trigger, a router, a search, an HTTP call, a data transform - counts against your plan (what Make long called an operation and now bills as a credit), and AI modules and in-scenario code tend to draw more than a plain step. For high-volume app-to-app plumbing that model can be efficient and predictable. For an agent that reasons across many tool calls, it cuts the other way: the richer the behavior, the more modules meter, and the harder the bill is to forecast. Governance controls also sit toward the higher and enterprise tiers, so the more of a platform you need, the further up you go.

ChatBotKit is set up to lean the opposite direction. You can begin free, move onto self-serve plans that grow with how much you use, and reach full enterprise options - on-prem and air-gapped deployment - only when the need is real. The entire managed stack - models, RAG, sandboxes, every channel, security, and observability - is in place with nothing to provision and no enterprise contract required to get going. Both sides adjust their prices, so confirm the current plans directly.

Choose Make If

• Your primary need is app-to-app automation - connecting SaaS tools, moving and transforming data, and running scheduled or event-triggered scenarios.
• You want the most granular visual canvas - routers, iterators, and aggregators - to shape exactly how data flows, bundle by bundle.
• You are comfortable treating AI as one module among many inside a broader automation.
• You want a deep catalogue of pre-built app modules for wiring existing tools together.

Choose ChatBotKit If

• Your goal is to build and ship AI agents, not to wire and meter app-to-app scenarios.
• You want one agent configuration to reach every channel - web, WhatsApp, Slack, email, and voice - with a unified inbox and memory.
• You want the agent to decide the path rather than drawing every module in advance.
• You would rather have managed knowledge, tools, and observability built in than assemble and meter them as modules.
• You want a single platform in place of the ten-plus tools a production agent stack usually needs, running on your own model keys and OAuth connections.
• You need deployment choice - managed cloud, your own cloud, on-prem, or air-gapped - for data residency.

Moving from Make to ChatBotKit

Pull your knowledge sources into a dataset, capture what the agent should do as a backstory with abilities - through the dashboard, the visual Blueprint Designer, or whichever SDK suits your stack - then attach the channels you want. There is nothing below to provision. If Make is still running backend plumbing worth keeping - a CRM sync, a file move, a nightly ETL - keep those scenarios in place and point them at your ChatBotKit agent over the API, an HTTP module, or MCP. They pair up well: Make handles the app-to-app work, ChatBotKit handles the agent.

Summary

Make and ChatBotKit start from different centers of gravity. Make is a managed visual automation platform - the heir to Integromat - where you draw scenarios of modules that move and transform data between apps, and AI shows up as a module or an agent on that canvas. ChatBotKit is a managed agent platform you can use no-code or in code, where the autonomous, conversational agent is the whole point - it reaches users on every channel and runs wherever your data must live. If your work is app-to-app automation, Make is a great choice. If your work is building, shipping, and growing AI agents without operating infrastructure, ChatBotKit is the Make alternative built for you.