Agentic workflows without building every module by hand.

Quick answer

Updated October 27, 2026. Make.com is a hosted no-code builder where each scenario is assembled module by module; a single run is capped at 40 minutes on paid plans and 5 minutes on Free.[1, 2] Avalanche works at a higher level for complex, long, document-heavy agentic workflows: you set the few steps, checks and approvals that matter, agents do the work in between, and Avalanche Cloud keeps typed inputs and outputs at every step checkpoint in one run history, so you can compare a step across many runs and versions.[20, 21, 22] Choose Make.com when non-developers need to connect thousands of apps without code.[1]

Key takeaway

Choose Avalanche when the work is a long, multi-step, document-heavy agentic workflow and you want to inspect and compare each step across every run, in code your team owns.

Choose Make.com when non-developers need to connect apps visually, without writing code, using ready-made connections.

  • Avalanche wins on level of abstraction: agents do the work inside a few business steps, instead of each module being built and mapped by hand.
  • Avalanche wins on inspecting and comparing runs: typed step outputs for every run sit in one history by workflow and version, so a step can be compared across many runs; Make's scenario history shows module outputs one run at a time.
  • Avalanche wins on long-running runs: no 40-minute scenario cap, 300-second Sleep or 600-second agent step limit to design around.
  • Make.com wins on no-code building: non-developers build and change scenarios on a visual canvas, with 3,500+ ready-made app connections.

By , CTO, Trampoline AI. Updated · Facts checked · Published by the Avalanche team. Avalanche competes with Make.com.

01 / The core differenceAvalanche vs Make.com

Hand-built modules versus higher-level agentic steps

Make.com is a closed, hosted no-code platform: you build a scenario module by module, map data between modules, and pay in credits per operation.[1, 3] Avalanche works one level higher: a workflow is a few business steps in Python, agents do the work inside them, and every step is a checkpoint with typed inputs and outputs you can compare across runs on Avalanche Cloud.[20, 21, 22] In our view, building each module by hand gets cumbersome for agentic work, and Make's run limits show where it was designed to stop.

In Make.com, the scenario is the modules: each app call, router and AI module is added, configured and mapped by hand, and each module run usually costs a credit.[1, 3] In Avalanche, the workflow is a few business steps in Python, and an agent does the work inside a step.[20, 21] Because each step has typed inputs and outputs, every run is structured data: Avalanche Cloud keeps it in one run history by workflow and version, so you can compare a step across many runs. Make's scenario history shows each run's module outputs and logs.[13, 22] Our view is that no-code works well for app-to-app automation but becomes cumbersome for agentic work, where every new decision means another module to build and maintain. Open source helps too: the code, runtime and model choice are yours.

02 / At a glanceChecked 2026-10-27

Avalanche and Make.com, side by side.

Avalanche and Make.com compared, checked 2026-10-27
CriterionAvalancheMake.comEdge
Level of abstractionA few business steps in Python; agents do the work inside each stepScenarios built module by module, with data mapped between modules[1, 3, 20, 21]Avalanche edge
Inspecting and comparing runsAvalanche Cloud: typed inputs and outputs at every step checkpoint, in one run history filtered by status, flow and deployment; a web console with a live DAG of every flow and run, the inputs and outputs of any step in one click, and the full console in your terminal, so you can compare a step across many runs and versionsScenario history per run with module outputs, logs, status, duration and credits, plus CSV export; agent runs add steps, tool calls and reasoning[4, 13, 21, 22]Avalanche edge
Long-running runsRuns for hours or days; on Avalanche Cloud each step can run up to an hour with its own timeout, and completed steps are never re-run after a crashRun capped at 40 min on paid plans, 5 min on Free; Sleep max 300 s; agent step max 600 s[2, 4, 6, 22]Avalanche edge
Document lineageAvalanche Cloud: every run gets a versioned workspace; each step's file changes are committed as a new version, so you can see which step changed a file, compare versions and download any step's files; agent steps get PDF, Word and spreadsheet skillsFile modules pass file data between modules, with PDF extraction; agents read PDF and images; no versioned workspace per run documented[4, 7, 22, 24]Avalanche edge
Versioned deploys and rollbackAvalanche Cloud: each deploy is a frozen version of the whole workflow, mapped to an exact git commit, with locked, hash-verified dependencies; running workflows finish on the version they started on; instant rollback to any previous version from the CLI or console, no rebuild; an audit trail of who promoted or rolled back60-day version history; restoring a version takes effect only after you save it; no in-flight version guarantee documented[8, 22]Avalanche edge
No-code building and app connectionsAny API, SDK or Python library: steps are plain Python with scoped, encrypted secrets and environment variables, so integrations are not limited to a catalog; no prebuilt connector catalog or no-code builderVisual no-code builder with 3,500+ ready-made app connections[1, 2, 20, 23]Make.com edge
Openness and freedomMIT open source, self-hostable but more limited than Avalanche Cloud; any model provider or Python libraryClosed hosted service; the on-prem agent only connects to local systems[9, 20, 26]Avalanche edge
Checkpointed Claude agent stepsAgent steps run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md), each sandboxed and checkpointed like any other stepMake-managed agent loop in open beta[4, 27, 28]Avalanche edge
Schedules, webhooks, CLI and APIAvalanche Cloud: cron schedules and webhooks declared in your workflow code, following rollbacks and promotions; webhook URLs never change on redeploy, redeliveries are deduplicated for 7 days, and idempotency keys return the same run for 7 days; a typed REST API with an OpenAPI spec, and a CLIScheduled interval and date runs, instant triggers and webhooks; a scenarios API and an MCP server[14, 15, 16, 22]Similar

Avalanche Cloud features, including rollback, schedules and webhooks, are available through the waitlist.

03 / Where each one winsOne claim per card

Where each one wins

Where Avalanche wins

  • Higher-level workflows

    You describe the outcome and the few steps, checks and approvals that matter. Agents do the reading, research and drafting inside each step, instead of you building and mapping every module.[20, 21]

  • Runs you can inspect and compare

    Each run of a deployed workflow is structured data: typed inputs and outputs at every step checkpoint. Avalanche Cloud keeps every run in one history for your organization, by workflow and version, so you can see which step fails, which outputs drift and what a new version changed across many runs. It is the move data teams made from one-off queries to managed transformations in Snowflake or Databricks, applied to agent work.[21, 22]

  • Built for long runs

    Workflows run for hours or days, with owner approval before results go out. On Avalanche Cloud every step is a checkpoint, so a failed step retries while finished steps keep their outputs.[22]

  • Every step versions your documents

    Avalanche Cloud gives every run a versioned workspace. Pass one document or hundreds; each step works on the files directly, and every step's changes are committed as a new version of the workspace, so you can see which step produced or changed each file, compare versions, and download any step's files. Document lineage, the way data teams have table lineage. Agent steps get PDF, Word and spreadsheet skills.[22, 24]

  • Versioned deploys and instant rollback

    On Avalanche Cloud each deploy is a frozen, checksum-verified version of the whole workflow, mapped to an exact git commit. Running workflows finish on the version they started on, and instant rollback makes any previous version live again in one click or one command, with no rebuild. Avalanche Cloud is on a waitlist.[22]

  • Open source, MIT

    The runtime is MIT-licensed Python in your git repository. Self-host it, use any model provider, call any Python library, and change the runtime itself if you need to.[20, 26]

  • From a skill to a workflow

    Install the Avalanche skill with npx skills add Trampoline-AI/avalanche and describe the outcome to your coding agent; it writes the workflow with you.[23]

  • Checkpointed Claude agent steps

    Agent steps can run Claude through the Claude Agent SDK and load Agent Skills (SKILL.md), each sandboxed and checkpointed like any other step.[27, 28]

Where Make.com wins

  • No code required

    Operations teams build and change scenarios on a visual canvas without writing code.[1]

  • Thousands of app connections

    Make.com advertises 3,500+ standard apps on every plan, so most integrations already exist as modules.[2]

  • Nothing to operate

    It is a hosted service with a free plan and public signup today; Avalanche Cloud is on a waitlist.[2, 22]

  • Agents and Maia in the builder

    Make AI Agent (New) adds tool-using agents to scenarios, and Maia builds and edits scenarios from plain language. Both are in beta.[4, 5]

04 / Detailed comparison4 buyer questions

What changes for your team.

01

Why does no-code get cumbersome for agentic work?

Buyer question

Do we want to build each module by hand, or set the steps that matter and let agents do the rest?

Make.com scenarios are built one module at a time: pick the app, configure the module, map fields from earlier modules.[1] That is quick for a fixed app-to-app automation. Each module run is an operation that usually costs one credit, and modules after an iterator run once per bundle.[3]

Agentic work rarely stays fixed. In our view, when a process means reading a stack of documents, comparing them and drafting a result, expressing it as hand-built modules turns every new decision into another module to configure, map and maintain. Avalanche works at a higher level: you set the steps, checks and approvals that matter, and an agent does the work inside a step.[20, 21] It still works for a simple two-step job.

02

How long can a run take?

Buyer question

Will a long, multi-step agentic run fit inside the platform's limits?

Make.com caps a single scenario run at 40 minutes on paid plans and 5 minutes on Free.[2] The Sleep module waits at most 300 seconds; for longer waits, Make recommends splitting the work into two scenarios with a data store in between.[6] An AI Agent (New) step times out after 300 seconds by default and 600 seconds at most.[4] If a run fails with incomplete executions enabled, Make can retry from the failed module.[10]

These limits are the concrete reason we say Avalanche is built for long, complex workflows. Avalanche workflows run for hours or days, and an owner can approve the result before it goes out. On Avalanche Cloud, every step is a checkpoint, so a failed step retries while finished steps keep their outputs and committed workspace files.[22, 25]

03

Where does open source matter most?

Buyer question

How much freedom do we have to change, move or extend the workflow?

Make.com is a hosted subscription service. Its Enterprise on-prem agent connects Make to systems inside your network; it does not run the workflow engine on your infrastructure.[9] Scenarios export as JSON blueprints, but you still rebuild connections when importing them.[11] Version history keeps 60 days, and a restored version takes effect only once you save it.[8]

Avalanche's runtime is MIT-licensed. Workflows are Python in your repository, reviewed in pull requests and deployed from committed source; on Avalanche Cloud, you can roll back to any previous version in one click, with no rebuild.[22] Workflows can call any Python library and any model provider you configure.[20, 26] In our view, this is where the gap with a closed no-code builder is widest: when a step needs something the catalog lacks, you write it, and nothing ties the workflow to one vendor.

04

When is Make.com the better choice?

Buyer question

Is there a job where Make.com still fits better?

Yes. If the people building the automation do not write code and most of the work is moving data between apps, Make.com's visual builder and 3,500+ app connections get it running faster.[1, 2] It is hosted, has a free plan, and adds tool-using agents and Maia, which builds scenarios from plain language; both are in beta.[4, 5]

Avalanche fits once the workflow is the product of real agent work: long runs, many documents, and steps that need checks and approvals. Its MIT-licensed runtime is self-hostable but more limited than Avalanche Cloud, which adds managed runs and versioned deploys and is on a waitlist.[20, 22]

05 / FAQDirect answers

Common questions.

Is Avalanche a good Make.com alternative for AI agent workflows?
Yes, when the workflow is long, multi-step or document-heavy. Avalanche works at a higher level, with agents doing the work inside a few checkpointed steps whose typed outputs you can compare across runs on Avalanche Cloud.[20, 21, 22] Make.com remains the better fit for no-code app-to-app automation.[1]
Can I compare one workflow step across many runs?
Yes, on Avalanche Cloud. Each step checkpoint records typed inputs and outputs, and Avalanche Cloud keeps every deployed run in one history by workflow and version, so you can see which step fails or which outputs changed after a new version.[21, 22] Make.com's scenario history shows each run's module outputs and logs and can be exported as CSV.[13]
How long can a Make.com scenario run?
Make.com caps a single scenario run at 40 minutes on paid plans and 5 minutes on Free.[2] The Sleep module waits at most 300 seconds, and an AI Agent (New) step times out at 600 seconds at most.[4, 6]
Is Make.com open source or self-hostable?
No. Make.com is a hosted service; its Enterprise on-prem agent only connects Make to local systems.[9] The Avalanche runtime is MIT-licensed and self-hostable, more limited than Avalanche Cloud.[20, 26]
Can I build an Avalanche workflow without writing code myself?
Your coding agent writes it with you. Install the Avalanche skill with npx skills add Trampoline-AI/avalanche and describe the outcome.[23] The result is Python your team owns, not a visual no-code canvas.
Is Avalanche Cloud available now?
Avalanche Cloud is on a waitlist. The MIT-licensed runtime is available today and self-hostable, with its operator, web console, REST API and terminal UI; it is more limited than Avalanche Cloud, which runs the production infrastructure for you.[20, 22]
Can I see which step changed a document?
Yes, on Avalanche Cloud. Every run gets a versioned workspace, and each step's changes are committed as a new version, so you can see which step produced or changed each file, compare versions and download any step's files.[22] Make.com file modules pass file data from one module to the next, and agents can read PDF files, but its docs do not describe a versioned workspace per run.[4, 7]
06 / SourcesChecked 2026-10-27

Public primary sources.

Every source was fetched on the date shown. Prices and plans change; check the vendor before you buy.

  1. [1]Make.com product overviewMake · Checked No-code visual builder and app connections.https://www.make.com/en
  2. [2]Make.com pricingMake · Checked Single run capped at 5 min Free, 40 min paid; 3,500+ standard apps.https://www.make.com/en/pricing
  3. [3]CreditsMake · Checked https://help.make.com/credits
  4. [4]Make AI Agent (New) appMake · Checked Open beta; step timeout default 300 s, max 600 s; file inputs and outputs.https://help.make.com/make-ai-agent-new-app.md
  5. [5]Introduction to Maia by MakeMake · Checked https://help.make.com/introduction-to-maia-by-make
  6. [6]Tools (Sleep module)Make · Checked Sleep max 300 seconds; longer waits via two scenarios and a data store.https://help.make.com/util
  7. [7]Working with filesMake · Checked https://help.make.com/working-with-files
  8. [8]Scenario version controlMake · Checked 60-day history; Restore version, then Save.https://help.make.com/scenario-version-control
  9. [9]On-premise agentMake · Checked https://help.make.com/on-premise-agent
  10. [10]Manage incomplete executionsMake · Checked https://help.make.com/manage-incomplete-executions
  11. [11]BlueprintsMake · Checked https://help.make.com/blueprints
  12. [12]Scenario run replayMake · Checked https://help.make.com/scenario-run-replay
  13. [13]Scenario historyMake · Checked Per-run bundles, module outputs and logs, status, duration, credits; CSV export.https://help.make.com/scenario-history
  14. [14]Schedule a scenarioMake · Checked https://help.make.com/schedule-a-scenario
  15. [15]Make API: scenariosMake · Checked https://developers.make.com/api-documentation/api-reference/scenarios.md
  16. [16]Make MCP serverMake · Checked https://help.make.com/make-mcp-server
  17. [20]Avalanche repository and READMETrampoline AI · Checked Python workflows, MIT license.https://github.com/Trampoline-AI/avalanche
  18. [21]Agent stepsAvalanche docs · Checked https://avalanche-docs.docsalot.dev/agent-steps/index
  19. [22]Avalanche Cloud (avalanche.run)Avalanche · Checked Deploy, observe and govern workflows; Cloud access through the waitlist.https://avalanche.run/
  20. [23]Build a workflow with the Avalanche skillAvalanche docs · Checked https://avalanche-docs.docsalot.dev/build-workflows/build-with-skill
  21. [24]Agent step skills (PDF, Word, spreadsheets)Avalanche docs · Checked https://avalanche-docs.docsalot.dev/agent-steps/skills
  22. [25]Inputs and artifactsAvalanche docs · Checked https://avalanche-docs.docsalot.dev/build-workflows/inputs-and-artifacts
  23. [26]Avalanche MIT licenseTrampoline AI · Checked https://github.com/Trampoline-AI/avalanche/blob/main/LICENSE
  24. [27]Claude Agent SDK agent steps (PR #124)Trampoline AI · Checked https://github.com/Trampoline-AI/avalanche/pull/124
  25. [28]Agent Skills (SKILL.md) in steps (PR #136)Trampoline AI · Checked https://github.com/Trampoline-AI/avalanche/pull/136
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See the runtime for yourselfOpen source · MIT

Our code speaks for itself.

The Avalanche runtime is open source under the MIT License. Read the code, run the starter project, and see how it fits the work your team repeats.