Make vs Activepieces

Pricing: per-op cloud vs free self-host

The most consequential difference, and the one that drives most migrations. Make bills per operation — every module in a scenario counts as one op per run. Activepieces Cloud bills per execution (one workflow run = one execution, regardless of steps); self-hosted Activepieces has no per-run fee at all.

The math that matters: a 5-step workflow firing 1,000 times/month is 5,000 ops on Make versus 1,000 executions on Activepieces — and free at the runtime on self-host. At higher volume the gap widens fast. The math swings toward Activepieces somewhere around 2,000-5,000 ops/month for most teams; by 20k+/month self-hosted Activepieces is dramatically cheaper.

Self-hosting and licensing

Cleanest one-line difference: Activepieces self-hosts under MIT; Make does not self-host at any price. If you need on-prem, air-gapped, EU-only data residency, or simply want the right to own the runtime, Make is disqualified. Activepieces runs in Docker or via Helm on Kubernetes, stores everything in Postgres, and is genuinely free for unlimited workflows.

Worth pausing on what real MIT means in this category. Most "open source" workflow tools are actually fair-code, source-available, or open-core with paid features hidden behind a license. Activepieces is one of the few that release the whole runtime under MIT — you can fork it, white-label it, embed it in your own product, or do anything else MIT allows. That is a structurally different position from Make and from most so-called open-source competitors.

Workflow complexity

Make wins on visual polish and battle-tested complexity primitives. Routers, iterators, aggregators, and nested error handlers are first-class modules on a smooth canvas anyone can read. Anyone who can use a flowchart tool can build a 20-30 step Make scenario without much ramp.

Activepieces has branches, loops, and step-by-step data inspection, plus Code pieces for custom logic. The editor is clean and modern; complexity primitives are functional but younger than Make\u2019s. For workflows up to ~15-20 steps Activepieces is a fine visual experience. For 30+ step scenarios with deep nesting Make\u2019s canvas still has the edge — though at that complexity n8n or Pipedream are often better fits than either.

Developer flexibility

Activepieces wins clean. Code pieces accept JavaScript with full access to upstream data; the editor is integrated and the language feels first-class rather than bolted on. You can also build first-party pieces in TypeScript for repeated logic and contribute them upstream.

Make has Custom Apps and an HTTP module, but writing real logic inside a Make scenario feels like fighting the canvas. Custom Apps live in a separate authoring environment behind a paid tier; the HTTP module + JSON Parse module combo works for one-off API calls but is awkward for anything wanting real control flow. For workflows that are 30%+ custom logic, Activepieces is the more honest choice.

Integrations

Make has ~1,800+ pre-built apps; Activepieces ships ~280+ first-party pieces plus a community piece catalog and a generic HTTP request piece. For mainstream SaaS — Google Workspace, Slack, Notion, Airtable, HubSpot, Stripe, OpenAI, Anthropic, common webhooks — both cover what most teams use.

Make wins on raw count, especially long-tail SaaS it has spent years cataloging. Activepieces closes the gap with the HTTP piece (which can talk to any REST API) and a community catalog that is growing fast. Audit your actual app list against both catalogs before deciding — catalog size only matters for the apps you actually use.

AI workflow support

Tie, with different shapes. Make has dedicated AI modules — OpenAI, Anthropic, Hugging Face, image generators — that drop onto the canvas like any other module. Polished for operators adding an LLM step to an existing scenario.

Activepieces ships first-party AI pieces plus a native AI step for prompt-driven actions and a growing catalog of LLM-related pieces. Roughly tied with Make today for typical "summarize and post" patterns. For agentic workflows specifically, neither is purpose-built — look at Dify or LangChain, or n8n\u2019s native AI Agent and LangChain nodes if RAG and tool-use matter.

Debugging

Make has the more polished review experience for operators — execution history with per-module data, clear visual replay, and a friendly UI for non-engineers reading "what happened on yesterday\u2019s run". The history view is one of Make\u2019s real strengths.

Activepieces exposes step-level JSON data inline and lets you re-run individual steps with edited inputs, which is faster for engineers actively building. Slight edge to Activepieces for technical iteration; slight edge to Make for operator-friendly review. Choose based on who actually maintains the workflows day-to-day.

Scaling

Two different shapes:

• Make scales via cloud tiers — you pay for ops and concurrent scenarios; higher tiers raise concurrency caps and add features like scenario teams, error handling SLAs, and enterprise connectors. Cost grows linearly with operations, which can balloon on multi-step scenarios.
• Activepieces self-host scales horizontally — add workers behind a Postgres database and a queue. Throughput grows with infrastructure, not per-execution fees. Cost stays roughly flat per execution as you scale; operational burden is real but bounded.
• Activepieces Cloud handles managed scaling with worker pools, billed per execution; meaningfully cheaper than Make at equivalent volume on most workflow shapes, especially multi-step scenarios.
• At 10k+ ops/month the math swings hard toward Activepieces; at 100k+/month self-hosted Activepieces is no contest on cost. At under ~1k ops/month either tool fits a team budget.

Lock-in risk

Make has high lock-in. Scenarios are proprietary, do not export to anything portable, and there is no self-host option at any price. If Make\u2019s pricing or product direction shifts, your only options are to stay or to rebuild from scratch on another platform.

Activepieces has the lowest lock-in in the category. MIT-licensed source on GitHub, flows export to JSON files you can check into Git, and self-host means the runtime is yours outright. If Activepieces the company changed direction tomorrow, your flows would keep running on your server indefinitely. Structurally different category of risk from Make, not a marginal improvement.

Who should use which

Pick Make if any of these are true

• Non-technical owners build or read workflows as part of their day job.
• Your stack depends on long-tail SaaS apps Activepieces does not yet cover.
• You want the most polished visual canvas in the category as a product surface.
• You explicitly do not want to operate a server, database, or upgrade cycle.
• You are at low ops volume (under ~1k ops/month) where per-op pricing has not started to hurt.
• You are coming from Zapier and want a more powerful canvas with friendlier per-op pricing.

Pick Activepieces if any of these are true

• You want a truly MIT-licensed tool you can self-host, fork, white-label, or embed.
• You care about data ownership, on-prem, EU residency, or air-gapped environments.
• You are at enough volume that Make\u2019s per-op pricing dominates your budget.
• Your stack is mainstream enough that Activepieces\u2019 280+ pieces + HTTP piece cover the apps you actually use.
• You want lock-in risk to be near zero — runtime, source, and workflows all in your control.
• You have at least part-time DevOps or technical capacity to operate self-host.

Migration considerations

Neither platform has an importer for the other. Migration is a manual rebuild. The shape:

• Make → Activepieces: straightforward for typical scenarios. Triggers, actions, filters, and routers map onto Activepieces flows with branches and loops. Custom Apps become Code pieces. Budget 20-40 minutes per scenario for the first few, 10-15 minutes after.
• Activepieces → Make: straightforward if your flows are mostly pre-built pieces; harder if they rely on Code pieces (you will need Make Custom Apps on a paid tier, or rewrite as no-code). Pieces that use the HTTP piece against APIs not in Make\u2019s catalog rebuild using Make\u2019s HTTP module.
• Hybrid is legitimate. Keep operator-owned visual workflows on Make for non-technical ownership; run high-volume, compliance-sensitive, or code-heavy workflows on self-hosted Activepieces. Many teams do this and pay meaningfully less than running everything on either platform alone.
• Cutover pattern (either direction): rebuild → test with real production data → run in parallel for a week → switch the source trigger → keep the old workflow disabled for 30 days as rollback. Never delete the source before parallel testing confirms green.

Best use cases

Make excels at

• Lead routing and CRM sync — multi-branch decision trees on incoming leads, mapped visually for ops teams to edit without engineering.
• Marketing operations — form submission → segment → email → CRM update, visible and editable on a polished canvas.
• Long-tail SaaS automations — apps Activepieces does not yet cover with first-party pieces.
• Operator-owned workflows — anything where the workflow itself is a deliverable a non-engineer needs to read or modify.
• Reporting pipelines — pull data from multiple SaaS, aggregate, push to a dashboard or sheet.

Activepieces excels at

• Self-hosted internal automations — thousands of executions/day on a small VPS with full data control.
• Compliance-sensitive workloads — air-gapped, on-prem, or EU-only data residency requirements.
• Embedded or white-labeled automation — the MIT license makes Activepieces the only viable serious candidate for embedding inside a product.
• High-volume workflows — anywhere per-op pricing on Make would dominate the budget.
• Developer-led teams — workflow code that engineers own end-to-end without per-task ransom.

Our take

These tools serve different humans, and the choice rarely hinges on raw capability — both are production-grade. The real question is two-fold: do non-technical owners need to build or read the workflows, and does ownership of the runtime matter to you? Answer both honestly and the decision usually picks itself.

For most developer-led teams starting fresh in 2026, Activepieces is the better long-term bet — the MIT license, self-host path, and per-execution Cloud pricing add up to a structurally better cost and lock-in story. For teams where workflow ownership lives outside engineering (marketing, RevOps, CS), Make\u2019s polished canvas earns its keep, and the operator experience is meaningfully better.

Two caveats worth naming: Make\u2019s catalog advantage is real for long-tail SaaS, and Activepieces\u2019 self-host path is real operational work. Be honest about which side of each constraint your team actually sits on before deciding.

Next reads

• Make review — the honest deep-dive
• Activepieces review — the honest deep-dive
• Zapier vs Activepieces — proprietary giant vs MIT challenger
• Activepieces vs n8n — MIT vs source-available self-host
• Make vs n8n — managed canvas vs self-host engine
• Make vs Pipedream — canvas vs code-first serverless
• Make vs Zapier — canvas vs catalog
• Best Zapier alternatives — the wider buyer guide
• All head-to-head comparisons