Choose Gumloop when the job is mainly agentic: people give an outcome in ordinary language, the system decides which tools to use, and a human reviews consequential actions. Choose Make when the job is mainly deterministic: your team can draw the steps, conditions, data mappings, retries, and expected output on a visual canvas. For many small businesses, the best design is both—an agent interprets messy work, then a fixed scenario validates and executes it.
The practical difference is not “which tool has more AI.” Gumloop is now a managed, agent-first platform with a shared organization credit pool, included model access, optional BYOK, conversational skill and trigger creation, and agent channels such as Slack. Make remains a broad visual automation platform with 3,500+ stated app integrations, routers, filters, code, team roles, Maia, and AI Agents alongside conventional scenarios.
Before buying, open the live Gumloop pricing page and Make pricing page. For adjacent decisions, compare Make vs n8n, Zapier vs Make, Make alternatives, and our best automation software guide.
Verification note: product documentation was checked on September 18, 2026. This is vendor-document-based editorial analysis, not hands-on testing. Make's rendered pricing selector was blocked by Cloudflare in this research environment; the plan names and usage rules were reverified in current first-party documentation, while the dollar figures below retain the latest first-party US pricing capture from August 21, 2026 and are clearly marked for checkout confirmation. Gumloop's old Make comparison URL returned 404, so no claim in this guide relies on it.
AI-generated conceptual illustration; not a screenshot of either product.
Quick verdict: who should choose Gumloop or Make?
Gumloop is the stronger first shortlist for agent-led knowledge work; Make is the stronger first shortlist for visible, repeatable system-to-system automation.
| Buyer type | Better starting point | Why | Main caution |
|---|---|---|---|
| Founder or operations lead delegating research, triage, drafting, and cross-app follow-up | Gumloop | The operating model starts with a goal, tools, skills, knowledge, and approvals | Quality and cost vary by model, context, tools, and iteration |
| Automation builder mapping orders, leads, files, and records through known rules | Make | Scenarios expose modules, bundles, routers, filters, mappings, and error paths | Complex canvases need documentation and an owner |
| Team wanting an agent in Slack or another conversational channel | Gumloop | Agents can be reached in Slack, Teams, email, hosted pages, API, and MCP | Shared credentials and write permissions need strict approval rules |
| Team with many SaaS connectors and unsupported API endpoints | Make | Make states 3,500+ apps, plus HTTP and custom-app escape hatches | An app listing does not prove the exact trigger or action you need |
| Business automating ambiguous intake followed by controlled record updates | Both | Let the agent interpret; let a deterministic workflow validate and write | Two platforms add integration, monitoring, and ownership overhead |
Avoid choosing from a feature-count contest. Start with one real business outcome and ask whether the difficult part is judgment or execution.
Gumloop vs Make plans and features as of September 18, 2026
Gumloop has a simpler public plan boundary; Make offers a broader self-serve plan ladder. Prices can vary by region, currency, billing interval, credit selection, tax, and contract.
| Platform / plan | Current pricing posture | What it is for | Important boundary |
|---|---|---|---|
| Gumloop Pro | $37/month, 20,000 included credits; 14-day card-required trial | Self-serve agents and team use | Pro credits do not roll over; overage is $0.005/credit when enabled |
| Gumloop Enterprise | Custom quote and allocation | Organization-wide controls, reporting, deployment, and negotiated capacity | Enterprise can add rollover, organization Insights, custom controls, and negotiated terms |
| Make Free | $0; current first-party pricing capture showed 1,000 credits/month | Learning and very light noncritical workflows | Two active scenarios and slower scheduling make it a weak home for critical operations |
| Make Core | Latest US capture at 10,000 credits: $10.59 monthly or $9/month billed annually | Solo operators and ordinary production scenarios | Collaboration/governance remains below Teams; confirm live selector |
| Make Pro | Latest US capture at 10,000 credits: $18.82 monthly or $16/month billed annually | Priority execution and stronger troubleshooting | Same chosen credit amount can cost more because the tier buys capabilities |
| Make Teams | Latest US capture at 10,000 credits: $34.12 monthly or $29/month billed annually | Multiple builders, team roles, and shared templates | Team structure improves; custom roles remain Enterprise |
| Make Enterprise | Custom quote | Advanced security, support, governance, and scale | Verify every control, limit, and support commitment in writing |
Sources: Gumloop credits, Make credits, Make teams, and the two live pricing links above. Make's official extra-credit documentation still uses a $9 plan with 10,000 credits as its worked example, but that is not a substitute for your regional checkout.
The billing units are not equivalent. One Gumloop credit can represent model usage, a tool call, compute, orchestration, or a premium tool charge. One Make credit usually represents a module operation, while AI and advanced features may consume credits dynamically. Never convert one platform's credits into the other's with a single ratio.
What is the real product difference?
Gumloop asks, “What outcome should this agent achieve?” Make asks, “What exact scenario should run?” Both products are expanding toward the middle, but their centers of gravity remain different.
Gumloop agents receive a goal, decide which connected tools to call, adapt to results, and can ask for approval. Teams can create reusable skills, attach company knowledge, choose models, deploy agents to Slack, and describe custom polling triggers in plain language. Gumloop's current documentation lists 150+ connectors—not thousands—and emphasizes agent access, tools, knowledge, channels, and governance.
Make scenarios expose each trigger and module on a visual canvas. Routers split a scenario into ordered routes; filters stop bundles that do not meet conditions; iterators and aggregators reshape data; error handlers control failure behavior. Make AI Agents add goal-driven tool selection, while Maia can create, modify, and debug scenarios from prompts. The Make Code app adds JavaScript or Python on paid plans.
These additions do not erase the operating distinction. Maia helps build a scenario; it does not turn every scenario into an autonomous agent. Gumloop can create triggers and skills conversationally; that does not make a variable agent task as predictable as a fixed module chain.
How pricing and credits differ
Gumloop meters agent work as a variable service stack; Make usually meters scenario work module by module. That changes how a small business should forecast cost.
Gumloop's shared organization credits
Gumloop documents one credit as $0.005 at list price. A typical agent task can include:
- model chat and reasoning, converted from model cost;
- at least one credit for each successful tool call, plus any tool-specific charge;
- five credits per active compute minute, with stated minimums;
- an 8% default orchestration fee; and
- additional categories such as evaluations, subagents, or routing when used.
The organization's users and agents draw from a shared pool. Pro includes 20,000 credits, described as 7,400 list-price credits plus 12,600 bonus credits. Pro credits expire monthly. Overage can keep agents running at $0.005 per credit, but the documented default Pro ceiling is one million overage credits—$5,000—so lower the cap before broad rollout.
BYOK can make covered model calls consume zero Gumloop credits while your model provider bills tokens separately. Tool calls and compute remain. Gumloop also documents a 16% BYOK orchestration rate for agents instead of the usual 8%, calculated on the pre-waiver value. BYOK can reduce platform usage without reducing total cash cost; reconcile both bills.
Premium data tools need their own line. Ordinary Slack, Google Sheets, or Gmail tool calls may carry only the base tool-call credit, while services such as Apollo enrichment or Firecrawl can add pass-through charges. “Connector included” does not mean “external data included.”
Make's module and AI credits
For most non-AI Make apps, one operation equals one credit. A trigger check can consume a credit even when it finds nothing. Search modules usually run once, while each downstream action can run once per returned bundle. Routers and filters can be free themselves while the modules reached through their branches still consume credits.
Make's AI Provider is available across plans and charges operations plus token-based credits. On paid plans, a custom AI-provider connection generally leaves Make charging operation credits while OpenAI, Anthropic, Gemini, or another provider bills tokens separately. Make AI Agent chats also count called tool operations. Make Code consumes two credits per second of code execution.
Manual and automatically purchased extra Make credits carry a documented 25% premium. Auto-purchase buys 10,000-credit blocks and can repeat until its plan-based limit. This preserves continuity but can quietly turn a design problem into a billing problem.
Three normalized small-business workload examples
Compare the same completed outcome, not the platforms' credit labels. These examples are planning templates, not measured benchmarks and not claims that Gumloop and Make will produce equivalent work.
| SMB workload | Stable business unit | Gumloop measurement plan | Make measurement plan | Human check |
|---|---|---|---|---|
| 500 inbound lead records/month | One accepted lead classification and CRM disposition | Record total agent credits per accepted outcome: model, successful tools, compute, orchestration, premium enrichment, retries | Count trigger/search credits, bundles, classification token credits, CRM actions, retries, and any provider bill | Review false positives, duplicate writes, and rejected classifications |
| 2,000 order or ticket handoffs/month | One correctly routed record with required fields | Measure agent tasks only if interpretation is needed; otherwise Gumloop may be the wrong architecture | Count polling or webhook trigger, router branches, filters, per-bundle actions, errors, and replays | Review exceptions and records that crossed more than one route |
| 200 monthly account research briefs | One sourced brief accepted by the user | Measure model/context cost, web and enrichment tools, compute, orchestration, revisions, and file generation | Measure scenario/agent operations, AI tokens, search/enrichment modules, loops, and separate provider charges | Review citation accuracy, omissions, and average correction minutes |
For each platform, run 50 to 100 representative cases: ordinary, difficult, duplicate, malformed, permission-denied, and provider-failure cases. Record cost per accepted outcome, not cost per trigger or chat. Also record elapsed time, human review minutes, and repair time.
The lead example may favor an agent if inputs are messy and judgment-heavy. The ticket example usually favors a deterministic scenario if routing rules are stable. The research example is more naturally agentic, but it can still use deterministic validation before anything reaches a CRM or customer.
Deterministic workflows versus agentic tasks
Keep known rules deterministic and reserve agents for ambiguity. This is the clearest architecture rule in the entire comparison.
Use a deterministic workflow when:
- inputs and required fields are known;
- conditions can be expressed as explicit rules;
- the same input should reliably produce the same action;
- auditability, idempotency, or reversal matters; or
- a mistake can send money, delete data, or create a customer commitment.
Use an agentic task when:
- the input is unstructured language, documents, or mixed sources;
- the right tool depends on context;
- research, synthesis, or exception handling matters;
- a human can review uncertain outputs; and
- variable execution cost is justified by the judgment performed.
AI-generated conceptual illustration. It shows an architecture pattern, not either vendor's interface.
A strong hybrid pattern is: agent proposes → human approves → fixed workflow validates → system writes → monitoring confirms. Gumloop can supply the agent and Make can supply the deterministic scenario, but a single platform may cover the whole pattern if the workflow is modest. Do not buy two subscriptions merely because the diagram looks tidy.
Integrations: test the action, not the logo
Make's 3,500+ stated app catalog is broader; Gumloop's smaller catalog is optimized around agent tools and MCP. Neither count proves workflow fit. A connector may expose only some triggers, searches, actions, objects, pagination rules, or authentication modes.
AI-generated conceptual checklist; not a product interface or a completed connector test.
Run this proof-of-concept checklist before committing:
- Name the exact object and action. “Connects to HubSpot” is meaningless if you need a custom-object association the connector cannot create.
- Test authentication ownership. Confirm personal, shared, service-account, and reauthorization behavior.
- Read and write a sandbox record. Verify field types, custom fields, attachments, pagination, and rate limits.
- Test duplicate delivery. Ensure the design is idempotent when a webhook retries or an agent repeats a tool call.
- Force a permission failure. Confirm what the operator sees and how work resumes.
- Force a partial failure. Test whether completed earlier actions are detected, reversed, or safely skipped.
- Measure one fan-out case. Ten records can turn one search into dozens of Make actions or several Gumloop tool calls.
- Test approval controls. A read-only lookup and a destructive write should not share the same approval posture.
- Capture usage. Record platform credits, AI-provider charges, premium data charges, and review time.
- Document the escape hatch. Verify HTTP, API, MCP, custom app, or code options when the native connector falls short.
For Make, routers process routes sequentially, and filters decide which bundles continue. For Gumloop, tool-management presets can allow, ask, or deny calls, including a useful “ask for writes/deletes” posture. Both are controls; neither replaces testing.
Where Gumloop is stronger
Gumloop is stronger when employees should delegate outcomes instead of maintaining flowcharts. Its agent builder combines model selection, instructions, connectors, reusable skills, knowledge sources, subagents, secrets, triggers, channels, and approval rules.
Conversational creation is a genuine usability advantage, but describe it accurately. Gumloop can create skills from a description and build custom polling triggers from plain language. The agent discovers connected tool schemas, writes trigger code, tests it in a sandbox, and captures baseline state. That is not proof that every integration or production edge case is handled automatically.
Slack deployment is also a differentiator for teams that want agents where work already happens. The same agent can be exposed through Slack, Teams, email, hosted pages, API, or MCP. The risk is permission spread: a convenient shared agent can become a shared path to sensitive apps unless credentials, user access, and write approvals are designed deliberately.
Gumloop includes access to its model catalog through platform credits and supports BYOK. That reduces initial provider setup and gives buyers a cost-control option later. The downside is a more complicated cost ledger than a simple per-seat price, especially when premium tools, long contexts, evaluations, subagents, and repeated attempts enter the task.
Where Make is stronger
Make is stronger when operators need to see and control each step of a repeatable process. Its visual scenario builder exposes data movement, transformations, filters, routes, iterators, aggregators, and error paths. That makes it easier to reason about known logic than an agent's variable tool selection.
The app catalog is materially broader, and HTTP, custom apps, and Make Code provide escape hatches. Make Code supports JavaScript and Python on paid plans; Enterprise adds custom libraries and more execution capacity. The tradeoff is maintenance: code inside a visual platform is still code, with dependencies, tests, errors, and an owner.
Make AI Agents extend the platform into non-deterministic work. Current help documentation labels the current product “Make AI Agent (New)” and documents agent tools, knowledge, files, chats, and token-based credit use. Maia is a separate public-beta builder assistant that can create, modify, and debug scenarios. Maia is available on paid plans, with a Free trial, and consumes the same organizational credit pool after promotional use.
Make's collaboration story also scales more gradually. Below Teams, an organization has one team and cannot manage team roles. Teams introduces roles and multi-team operation. Enterprise adds custom organization and team roles, but permissions remain broad by resource type; separate teams may still be needed for narrower access.
Human review and maintenance costs
The cheaper platform can be the one that needs fewer human repair minutes, even if its subscription costs more. Include labor in the comparison.
Gumloop maintenance tends to concentrate in instructions, skills, knowledge quality, model selection, tool permissions, evaluations, and review queues. Agent behavior can shift when inputs, models, connected tools, or context change. The owner must sample outputs and tune boundaries.
Make maintenance tends to concentrate in credential expiry, app schema changes, field mappings, scenario sprawl, polling frequency, error handlers, data shapes, and incomplete executions. A visual canvas helps diagnosis, but it can become an electrical diagram drawn by a caffeinated octopus if nobody enforces naming and documentation.
For both platforms, assign:
- a business owner for the outcome;
- a technical owner for credentials and failures;
- a review policy for consequential actions;
- a monthly usage and cost review;
- test records for normal and exception cases; and
- a manual fallback when automation stops.
Hidden costs to include before choosing
Neither headline plan is the total cost. Budget for:
- platform overage or higher credit tiers;
- separate AI-provider bills under BYOK or custom connections;
- premium enrichment, scraping, search, email, or database usage;
- polling that runs when nothing happens;
- bundle fan-out, tool retries, and duplicate work;
- implementation, documentation, and test fixtures;
- human review and exception queues;
- security, SSO, audit, legal, and data-residency requirements;
- downtime procedures and incident investigation; and
- migration overlap while old and new automations run together.
Gumloop-specific warning: shared organization credits make adoption easy but can hide which team or agent is consuming the pool; organization-wide Insights is an Enterprise feature. Make-specific warning: buying extra credits does not upgrade collaboration, file, governance, or plan-specific limits, and extras carry a 25% premium.
Migration and coexistence paths
Treat migration as a rebuild with parallel validation, not a file conversion. Agent instructions, skills, credentials, scenario modules, expressions, error handling, history, and usage meters do not port cleanly.
Moving from Make toward Gumloop
Keep stable Make scenarios that already work. Move only the ambiguous front end first—email interpretation, document extraction, research, or exception explanation. Let Gumloop propose structured data, then send it to an existing Make webhook or API boundary. Compare accepted-output cost and review time before replacing any deterministic step.
Moving from Gumloop toward Make
Identify repeated agent paths that no longer require judgment. Rebuild those as explicit Make scenarios with validation, filters, idempotency, and error handling. Keep Gumloop for the tasks that still need research or interpretation. This reduces agent variability without throwing away useful agent work.
Running both long term
Define a narrow contract between them: a versioned JSON payload, authenticated webhook, stable fields, correlation ID, and explicit success/failure response. Log which platform owns each retry. Never let both sides retry the same irreversible action independently.
Final recommendation
For a small business choosing one platform, start with Make if most workflows are known and repeatable; start with Gumloop if the value comes from interpreting messy information and acting across tools. Make is usually the safer operating system for record movement, routing, and scheduled workflows. Gumloop is usually the more natural interface for research, triage, drafting, and conversational delegation.
Run one two-week proof of concept in the likely winner and one narrower proof in the alternative. Measure accepted outcomes, usage, repair time, review time, and operator confidence. The better platform is the one your team can own after the demo glow wears off.
Frequently asked questions
Is Gumloop better than Make for AI agents?
Gumloop is more agent-first: agents, skills, knowledge, channels, tools, and conversational setup are the primary product model. Make AI Agents are compelling when an agent must use existing Make scenarios and integrations, but Make's traditional strength remains explicit visual automation.
Is Make cheaper than Gumloop?
Make has a lower entry price and a Free plan, while Gumloop Pro starts at $37/month. That does not guarantee a lower completed-outcome cost. Make can multiply credits through polling, bundles, and downstream actions; Gumloop can vary through model use, tools, compute, orchestration, and premium data.
Can Gumloop replace Make?
Gumloop can replace some Make workflows when flexible agent behavior is more valuable than step-level determinism. It is a poor automatic replacement for every mature scenario involving strict mappings, idempotency, transaction-like updates, and predictable branching.
Can Make replace Gumloop?
Make can reproduce many integrations and structured workflows, and Make AI Agents cover some agentic use cases. Replacing a mature Gumloop agent may still require rebuilding its instructions, skills, knowledge, approvals, and conversational channels.
Does Gumloop have more integrations than Make?
No. Gumloop currently documents 150+ connectors, while Make states 3,500+ prebuilt apps. Catalog size is only a shortlist signal; verify the exact trigger, action, object, authentication, and rate limits you need.
Should a small business use both?
Use both only when the boundary is clear: an agent handles interpretation and a deterministic workflow handles controlled execution. If one platform can meet the requirement cleanly, the second subscription and integration boundary may add more maintenance than value.
Are Gumloop credits the same as Make credits?
No. Gumloop credits cover components of agent work, including model use, tools, compute, and orchestration. Make credits usually meter module operations, with dynamic AI and advanced-feature rules. Compare cost per accepted business outcome, never credits one for one.
Sources and verification
- Gumloop pricing, credits and BYOK, agents, Slack agents, and AI-created triggers
- Make pricing, credits, AI Agent credits, routers, filters, Maia, teams, custom roles, and Make Code
- Product catalog claims were checked against the vendors' current first-party documentation indexes. Vendor marketing claims describe availability, not independent reliability or usability evidence.
Last verified: September 18, 2026. Confirm live prices, plan limits, model availability, security controls, and contract terms before purchase.