Make is a fully managed, cloud-hosted SaaS platform, which means users do not need to set up or maintain their own infrastructure.
Self-Hosted vs. Cloud Infrastructure
Make is a fully managed, cloud-hosted SaaS platform, which means users do not need to set up or maintain their own infrastructure. The platform automatically handles scaling, performance, and traffic spikes. It also features built-in enterprise-grade security, including SOC 2 Type II compliance, GDPR adherence, single sign-on (SSO), and SCIM. However, unlike n8n, Make does not support self-hosting. This inability to self-host or operate in air-gapped environments can be a significant drawback for SMBs with strict data residency or privacy regulations that require keeping data strictly on-premise.
Pricing Tiers and Specific Costs
Make offers a free tier alongside paid plans that start at $9 per month for basic access. The critical difference between Make and n8n lies in Make’s “pay-per-action” (credit-based) pricing model, where every single step and trigger in a workflow consumes a credit. For example, if you build a 10-step AI automation that runs 1,000 times a month, it will consume at least 10,000 credits. This model makes Make affordable and predictable for simple, low-volume automations, but costs can skyrocket rapidly for complex AI workflows that involve loops, branching, or frequent polling. In contrast, n8n uses a more predictable “pay-per-execution” model that charges a flat rate for a full workflow run regardless of the internal step count.
Native AI and Agent Capabilities
Make’s primary strength is its massive ecosystem of 3,000+ native app integrations, but its native AI capabilities are highly limited compared to n8n. While Make does offer an agent builder in beta, it relies heavily on external tools and APIs to function, lacking the robust out-of-the-box support n8n provides for self-hosted LLMs, vector databases, LangChain, and Retrieval-Augmented Generation (RAG). Because managing complex conversational logic and persistent memory in Make can become operationally expensive, experts recommend a hybrid architecture: using a dedicated AI builder (like Flowise) to act as the AI “brain” for reasoning, while using Make purely as the “arms” to connect to third-party tools and APIs.
Failure Modes and Limitations Compared to n8n
When deploying AI workflows on Make, SMBs commonly face the following failure modes and bottlenecks:
- Trigger Setup Limitations: For certain applications, Make relies on time-based scheduling (e.g., polling for new spreadsheet rows every 15 minutes) rather than instant, event-driven triggers, which can cause undesirable delays in real-time AI responses.
- Custom Field Mapping Challenges: Users report difficulties directly mapping custom fields between AI outputs and CRMs (such as GoHighLevel) within Make’s visual modules, necessitating manual workarounds.
- Credit Exhaustion on Loops: Features like loops and complex branching drain Make’s action credits rapidly, making the platform a costly “gas factory” for advanced AI agent logic.
- Limited Code Control: Make is fundamentally designed with a visual, scenario-style layout tailored for non-developers, meaning it has limited support for custom code steps and lacks the native JavaScript execution, Git-compatible version control, and developer SDKs that n8n offers.
Concurrency Limits
The provided sources do not specify exact numerical concurrency limits for Make workflows. However, because Make is a fully managed cloud platform, it is designed to automatically handle traffic spikes and performance scaling behind the scenes without requiring manual infrastructure intervention from the user.