Sistava

Make

Visual scenario builder for complex multi-step automations across 3,000+ apps

About Make

Make (formerly Integromat) offers a visual drag-and-drop builder for creating complex multi-branch automation scenarios with 3,000+ integrations. While Make provides visual flexibility, workflows still require manual mapping of every path and condition. Sistava replaces the need to build scenarios entirely with autonomous employees that figure out the steps themselves.

Platform details

Official website

What does Make.com actually do?

Make.com (formerly Integromat) is a visual workflow automation platform built around scenarios: branching graphs where each node, called a module, performs one operation on data. You drag modules onto a canvas, draw connections, set up routers and iterators, and Make runs the scenario on a schedule or trigger. It supports 2,000+ apps and exposes HTTP, JSON, and webhook modules for anything not in the catalog. Compared to Zapier, Make leans into power-user territory. Loops, arrays, error handlers, and aggregators are first-class, so you can model genuinely complex flows (multi-step ETL, conditional approvals, batched API calls) without falling off into code. The canvas-style editor makes it easier to see what a scenario does at a glance once you learn the icons. Make also added AI agent features and a marketplace of templates, plus enterprise-grade features like teams, role-based access, and dedicated regions. The platform's pricing is metered in operations rather than tasks, and one scenario run usually consumes more operations than the equivalent Zapier task count. Sistava is a different layer of the stack. Make wires apps together for you to operate; Sistava gives you an AI employee who operates the role. If the question is 'who runs growth tomorrow morning', Make does not answer it. Sistava does.

How much does Make.com cost?

Make's Core plan starts around $9 to $10.59 per month for 10,000 operations and unlimited scenarios, which on paper is roughly 47% cheaper than Zapier Starter and 5x the workflow volume. Pro adds advanced features and more operations, Teams adds collaboration, and Enterprise is custom-quoted with regional hosting, SSO, and audit controls. The catch is the operation meter. Make counts triggers, filters, polling, and errors as operations, while Zapier does not count triggers or filters. A five-step scenario in Make can burn 7-10 operations per run once you add the trigger, conditional checks, and an HTTP fetch. Heavy users still find Make 3-10x cheaper than Zapier at equivalent volumes, but the math is not as simple as the headline price suggests. AI usage in Make happens through OpenAI, Anthropic, or built-in agent modules. Each AI call is one (sometimes more) operation, plus you pay the underlying model provider directly. For agentic workloads, Make is cheaper than Zapier but still meters every reasoning step. Sistava charges per outcome through a credit system that bundles model usage, tool calls, and memory. For an end-to-end growth or support role, that pricing shape matches how a real employee would be budgeted instead of metering each thought.

When does Make.com beat the alternatives?

Make beats Zapier when the workflow has real branching, loops, or batch processing. The scenario editor handles arrays and iterators natively, so a flow like 'fetch 200 leads, enrich each through three APIs, route by score, write back to the CRM' is doable without code. Zapier needs paths, sub-Zaps, and code steps for the same shape, which gets expensive fast. Make also wins on cost-per-step. For high-volume, multi-step flows (especially with HTTP and JSON), the operations meter is friendlier than Zapier's task meter. Teams running thousands of executions per day typically save 60-80% by moving from Zapier to Make. Compared to n8n, Make is faster to start: hosted, polished UI, no infrastructure, native AI modules. Teams that want power without owning servers usually pick Make over n8n. Compared to Workato or Tray, Make is dramatically cheaper at small and mid-scale. Where Make stops being the right answer: when you need an entity that decides what to do, not just executes what you already designed. That is the Sistava layer. For deterministic, designed-by-you workflows at scale, Make is one of the best picks on the market.

Where does Make.com fall short for an AI workforce?

Make is a workflow tool, not a workforce. Every scenario is something you, the human, designed: triggers, branches, error paths, exact field mappings. The platform does not plan a quarter of marketing, draft and iterate copy until it converts, or decide that today's priority is to fix a stalled deal. Those judgment calls remain yours, and the canvas just executes the parts you already figured out. Maintenance scales with complexity. A 30-node scenario with branches and error handlers becomes its own piece of software. When an upstream API changes or rate-limits, you debug the graph, fix the schema, and redeploy. Solo founders end up with a second job as an integration engineer. AI inside Make is module-shaped. You bolt on an OpenAI or Anthropic node, prompt it, and pipe the output. There is no persistent role memory, no skill catalog, no learning across runs. Each AI call is stateless from the platform's view, which makes coherent multi-step agent behavior hard to model. Sistava ships the role end to end. The AI employee has a defined function, persistent context, skills appropriate to their role, and tools to act. Instead of designing a scenario, you give a brief and the employee runs the work, including the parts you did not pre-specify.

How does Make.com handle AI agents and integrations?

Make exposes AI via two surfaces. First, dedicated modules for OpenAI, Anthropic, Mistral, Hugging Face, and image or speech models, which you wire into scenarios like any other action. Second, Make AI Agents (in beta or GA depending on plan) which let an LLM decide which scenarios or modules to call within a scoped tool set. Both options bill operations on top of the model provider's own tokens. Integration coverage is strong: 2,000+ apps with HTTP, webhook, and OAuth helpers for anything missing. Make handles long-running scenarios, queued executions, and per-org rate-limit handling better than most no-code peers, which is why agencies and ops teams stay on it through scale. The agent abstraction is closer to 'function-calling within your scenarios' than to an autonomous worker. You define the tool set, the prompt, the guardrails, and the fallback paths. That is useful for narrow assistants, less useful for owning an outcome like 'grow signups by 20% this quarter'. Sistava fills that gap. The AI employee picks tools from a curated skill catalog, remembers context across sessions, escalates when stuck, and produces deliverables. For agentic ownership of a function, the shape is an employee, not a scenario.

Comparison