I used to be heavily reliant on Zapier. It was the first automation tool I picked up back when managing IoT devices was chaotic. However, as the number of devices increased and budget constraints tightened, cracks started to show. My IoT devices in remote areas of Kenya needed reliable automations that wouldn't falter with poor connectivity, and Zapier's pricing became a burden on my tight budget. That's when I discovered n8n, followed later by Make (formerly Integromat).
The Zapier ceiling
Initially, Zapier was helpful. It was easy to set up, had many integrations, and featured a user-friendly interface. But as my projects grew, so did the costs: $299/month was not sustainable. The setup became cumbersome with complex workflows, and I often struggled with limitations surrounding multi-step automations. Running an economical operation meant I couldn't afford Zapier every month just for convenience.
A major issue was dealing with unreliable internet connections. With intermittent connectivity, waiting for automations to sync or just halt unexpectedly was frustrating. In Kenya, this is a reality many developers face. Zapier's dependence on constant connectivity made it difficult to trust when I needed to integrate sensor data economically and reliably.
Discovering n8n: an open-source alternative
I found n8n while searching for open-source alternatives. It was free with the option to self-host, which sounded ideal. The transition wasn't simple, but the flexibility was refreshing after using Zapier. Setting up n8n on a local server took some effort, like configuring Docker and managing local network issues, but it paid off.
Here's a snippet of a simple automation with n8n, where it listens to new MQTT messages from IoT devices and saves them in a database:
// n8n simple workflow connecting MQTT and a DB
// Assume MQTT input node
const sensorData = $json;
// Example function node to process data
const processedData = sensorData.map(data => ({
id: data.deviceId,
temp: data.temperature,
humidity: data.humidity
}));
// MySQL Contribution Node (store processed data)
return executeMariaDB({
host: 'localhost',
user: 'root',
password: 'yourpassword',
database: 'IoTData',
values: processedData
});
I ran this on an older VPS with just 2GB of RAM, which was perfectly capable of parsing local MQTT messages and storing data. The cost? Just the server, far cheaper than the recurring costs with Zapier.
n8n's visual workflow builder often made sense once I got used to it, though it's not without quirks. Having access to the code behind automations means you can customize things beyond typical limits.
Make: the unexpected contender
I transitioned to Make when projects required more granularity than what n8n could comfortably handle. While n8n excels in adaptability, Make provides a structured yet broad platform.
Make stands out for conditional operations, and its interface for setting up conditional workflows is superior to n8n, often saving time when debugging extensive workflows. Testing scenarios, like turning on generators based on temperature spikes from sensor data, became less of a hassle.
When decisions get tough
Choosing between Zapier, n8n, and Make relies on balancing needs and constraints. If I've learned anything from scaling small projects to manage thousands of IoT devices, it's the necessity to adapt. Sticking to one tool like Zapier is convenient until outgrown.
Running n8n is more technical but cost-effective, ideal for those ready to dive into Docker setups and local servers. Make, with its reasonable pricing, offers a more refined experience for scenarios with complex conditionals and data flows.
The IoT developer's reality
Living and working in Kenya involves dealing with infrastructure not always aligned with major SaaS tools. Unstable internet isn't just a challenge, it's a regular hurdle. By switching from Zapier and utilizing a mix of n8n and Make, I've saved hundreds per month on automation expenses while ensuring the system is capable of handling real-world connectivity issues.
If you're in a similar spot,balancing ambitious goals with limited resources,consider trying self-hosted n8n for cost control and Make for efficient conditional workflows. The tools' perfection is less important than their adaptability and ability to meet your specific challenges.
Next up for me: integrating more advanced AI-driven analytics into these workflows to process sensor data. For now, though, I’m pleased that my IoT integrations are both cost-effective and adaptable enough to function under Kenya's unpredictable digital conditions. That's a success in any developer's book.
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