Make vs Brahmalabs: where automation ends and agency begins

Where automation ends and agency begins

You built the scenario months ago. It watches a webhook, enriches a record, updates a sheet, posts to Slack. Then someone dropped an AI module into the middle of it, and step four stopped transforming data and started making decisions. Approve the refund or escalate it. Draft the customer reply or flag the thread. The canvas looks the same. The risk profile does not.

That shift — from automation to agency — is the honest frame for comparing these two products, because they sit at different points on one spectrum. At the automation end, steps are deterministic and individually harmless; what you want from a platform is breadth of connectors, a good editor, and a low price per step. At the agency end, a step exercises judgment, spends money by the token, and acts on external systems in ways you did not enumerate in advance. What you want changes: approval gates, spend ceilings, sandboxes, egress control, and an audit trail you can hand to someone whose job is to distrust it.

Make has spent over a decade refining the automation end, and it shows. Brahmalabs was built for the agency end, and it is younger, more expensive at entry, and narrower in connector breadth — trade-offs this essay will not soften. Start with what Make does well, because it is a lot.

What Make gets right

Make began as Integromat in Prague in 2012 and was acquired by Celonis in 2020, rebranding to Make in 2022. The scenario canvas is the product of that long refinement: drag-and-drop modules across 3,000+ apps, routers and iterators for real branching logic, and a public library that showed more than 8,200 templates as of July 2026. Make says more than 200,000 businesses use it, with 30,000+ actions across the catalog. If your problem is “connect our SaaS tools without writing code,” catalog depth is the single most important number in this comparison, and Make wins it decisively. Brahmalabs ships hundreds of built-in integration actions across 300+ providers, plus anything MCP-compatible — but the catalog is young and depth varies by provider.

The entry price is equally hard to argue with. As of July 2026, Make’s pricing page shows a free tier with 1,000 credits a month and a single paid “Make” plan starting at $9/month for 5,000 credits, with a usage slider up to 8M+ credits. Each ordinary module action costs one credit. Brahmalabs’ free tier covers 1,000 credits, 5 workflows, and 3 agents; the paid plan is Pro at $299/month with 30,000 credits, metered by runs and sandbox-minutes rather than seats. For pure automation workloads, Make’s entry point is more than an order of magnitude cheaper, and no amount of positioning changes that arithmetic.

Certification is a third area where Make is simply ahead. Make states its information-security program is ISO 27001 certified and that it has completed SOC 2 Type II and SOC 3 audits. Brahmalabs’ SOC 2 Type II is in progress — not certified, not complete. If your procurement team needs the certificate this quarter, Make has it and Brahmalabs does not. On data residency the two are closer: Make offers an EU data center tied to your account alongside US hosting; Brahmalabs hosts in the EU by default.

Make’s operational machinery for automations is also mature in ways that only show up after the honeymoon. Error handling offers five handler directives — Ignore, Break/retry, Resume, Commit, and Rollback as the default — with failed-run state stored as incomplete executions for retry, and scenarios auto-disabled after three consecutive errors. The Waves 2025 release cycle shipped an MCP server and client GA on all plans, sandboxed JavaScript and Python via Make Code, scenario run replay, subscenarios, and authenticated webhooks. This is a platform that ships steadily.

The credit question

Make’s billing model is simple until AI enters the scenario. On August 27, 2025, Make switched its billing unit from operations to credits at a 1:1 conversion. Most modules still cost one credit — but AI and advanced modules consume variable credits based on tokens, file size, or run time. That variability lands exactly where agent workloads live, because token consumption is the least predictable input in the system. Community writeups titled “why your automations suddenly cost more” followed the switch, and in November 2025 Make unified extra-credit purchases at a 25% markup over included credits. A recurring user complaint predates all of this: a runaway scenario can burn a month’s quota fast. With variable-credit AI modules, that failure mode gets more expensive per loop.

None of this is scandalous — every metered platform wrestles with AI cost attribution. But it matters for agents specifically. Brahmalabs’ answer is structural rather than tariff-based: you bring your own model keys, inference is never marked up, and the platform charges a flat fee per 1,000 tokens. Each workflow can carry a spend ceiling, enforced by the same middleware pipeline that runs guardrails and cost tracking. Two candor notes belong here. First, the per-run LLM cost shown in the Brahmalabs UI is a labeled estimate, not exact metering. Second, Make’s native AI Provider — GA since October 2025 on all plans — gives LLM access with no external provider account at all, billed through credits. For teams that do not want to manage API keys, that convenience is genuine.

Agents change what the platform has to be

Make’s AI agents are real and improving quickly. They launched in beta in April 2025, and a rebuilt generation went GA on February 11, 2026: agents built directly in the scenario canvas, a real-time Reasoning Panel, in-canvas chat testing, multimodal file input and output, and a library of prebuilt agents. The design instinct — keep agents inside the canvas ops teams already know — is sound. But as of July 2026, the GA product is under six months old, and the controls surrounding it reflect a platform that treats agency as a feature rather than the core assumption. Three places show the difference.

Approval gates

Make has a dedicated Human in the loop app, available only on the Enterprise plan. It provides four modules: create a review request (which returns a review URL and webhooks the reviewed data back), cancel a request, watch completed reviews, and list reviews. Below Enterprise, teams hand-build approval loops with Slack approve/reject buttons or an Airtable review step. Those workarounds function, but the approval becomes glue you maintain, not a primitive the platform guarantees.

In Brahmalabs, human approval is one of 15 workflow node types and exists on every plan; multi-party approval arrives at Pro. A gate can require a single approver or N-of-M consensus, carries SLA timeouts and escalation chains, and renders dynamic approval forms in a reviewer inbox. Because every run is a Temporal workflow, a run parked at an approval gate waits durably — hours or days — without burning compute, then resumes exactly where it stopped. When the thing being approved is an agent’s proposed action rather than a data row, this is the difference between a checkbox and a control.

Containment

An agent that can browse, execute code, or call tools needs a blast-radius boundary. Make Code, GA since October 2025 on paid plans, runs sandboxed JavaScript and Python inside scenarios, with third-party libraries gated to Enterprise. Brahmalabs runs every agent step in an isolated container matched to the workload — a browser image, a data image, a CI image — with CPU, memory, and timeout ceilings, and digest-pinned images. A DNS-and-firewall sidecar enforces a per-run domain allowlist, and the run page shows the actual outbound domains each run touched. Credentials never reach the agent at all: the vault injects short-lived, scoped references at call time, so even an exfiltrated prompt has nothing to leak.

The record

Make’s per-run history records status, duration, credits consumed, and data-transfer size, and paid plans add full-text execution-log search — but retention is capped at 7, 30, or 60 days by plan. For automations, that is usually enough; if a scenario failed last month, you have probably already fixed it. For agent actions, the question arrives later and differently: who approved this, what did the model see, and what did it touch — sometimes months after the run. Brahmalabs keeps an append-only audit log across eight event categories (auth, config, run, HITL, integration, key, tenant, guardrail), queryable and exportable, with 90-day retention on Pro and one-year retention on Enterprise. Guardrail events are hash-chained; PII redaction runs before inference, not after logging.

The same facts, side by side

The table below reflects public pricing and documentation as of July 2026; both products are changing quickly, so verify before you buy.

When Make is the right choice

Treat this as advice to a friend, because it is.

• Marketing and ops automation across many SaaS tools. If the work is moving data between CRMs, sheets, ad platforms, and messaging tools, Make’s 3,000+ app catalog and 8,000+ templates are the whole game. Brahmalabs’ catalog will not have every connector you need today.
• Price sensitivity. $9/month for 5,000 credits against Brahmalabs’ $299/month Pro plan is not a close call. If your workloads are deterministic automations, paying agent-platform prices buys you controls you will never exercise.
• Breadth over depth, delivered fast. Agencies and consultants deploying client automations from the template library get to working scenarios in hours. Nothing on the Brahmalabs side matches that motion.
• Certification today. If ISO 27001 and a completed SOC 2 Type II are procurement gates this quarter, Make clears them now. Brahmalabs’ SOC 2 Type II is in progress, and “in progress” does not pass a vendor-risk review that demands the report.
• Light agent steps under human review. If every AI output in your scenarios is read by a person before anything happens, Make’s in-canvas agents with the Reasoning Panel may be all the agent infrastructure you need.

When Brahmalabs is the wrong choice

• Self-hosting is a hard requirement. Brahmalabs is a managed platform; on-prem or VPC deployment is an Enterprise engagement, not a download. Make is also cloud-only — its on-premise agent is a connectivity bridge, not self-hosting. If this requirement is real, n8n, Dify, or Temporal OSS serve it better than either product here.
• You need the integration to exist today. Hundreds of providers is not 3,000+. MCP servers close some of the gap, but if your stack is long-tail SaaS, check the catalog before committing.
• You need the certificate on paper now. Same point as above, from the other side: SOC 2 Type II in progress is not SOC 2 Type II.
• You have low tolerance for a young product. Brahmalabs is in early access: no public case studies or customer logos, no public status page, and API and DSL docs live behind the dashboard login rather than on a public portal. Roles are fixed at admin/member/viewer — no custom roles, no SCIM provisioning.
• The budget question answers itself. If nothing in your workflows spends money autonomously or acts on production systems, $299/month is buying insurance against risks you do not carry.

Where this leaves you

Locate your workflows on the spectrum and the decision mostly makes itself.

If you are an ops or marketing team automating a SaaS stack, choose Make and do not look back. It is cheaper, broader, certified, and refined by fourteen years of exactly this use case. If you have a few AI steps whose output a human always reads before acting, Make still probably serves you — its agent builder is young but usable, and the surrounding scenario machinery is proven.

The calculus flips when agents start acting rather than suggesting: touching customer accounts, moving money, opening pull requests, calling production APIs. At that point the questions your security team asks — who approved this, what could it reach, what did it spend, where is the record — stop being features you bolt on and become the product you are actually buying. Make answers them partially, with approval gated to Enterprise, 60-day maximum run history, and variable AI credits. Brahmalabs was designed around those questions, and you pay for that in a smaller catalog, a higher entry price, and a certification that is still in progress.

Some teams will run both, and that is not indecision: Make for the automation estate, Brahmalabs for the handful of workflows that exercise judgment. If that second category describes work on your roadmap, Brahmalabs is in early access at brahmalabs.io.