Your operations team spends hours on repetitive work that should run automatically. Product listings need review before going live. Customer orders trigger actions across six different systems. Support tickets require routing based on complexity. Every process involves the same decisions, the same data entry, the same manual coordination.

Automation platforms can eliminate this repetitive work. Zapier dominates business automation with 7,000+ app integrations and multi-step workflows. IFTTT pioneered simple automation for smart homes and personal productivity with 900+ services. Both platforms move data between apps automatically, but they serve different needs. For complex decisions requiring judgment, tools like Logic add decision intelligence that complements data routing. Understanding which platform fits your requirements means looking at what each does best.

Zapier vs IFTTT: Quick Comparison Overview

The right choice depends on your workflow complexity, integration needs, and whether you're automating business processes or personal tasks:

Pricing considerations: Zapier's task-based model means costs scale with usage—a workflow that creates a lead, sends an email, and updates a spreadsheet consumes three tasks per execution. IFTTT's unlimited applets at $2.50/month deliver exceptional value for personal automation, while Zapier's higher cost reflects business-critical features like advanced error handling, premium integrations, and team collaboration. Both platforms require ongoing configuration time when business rules change.

Zapier vs IFTTT Feature Comparison

Beyond the quick comparison, understanding these technical capabilities helps you choose the platform that fits your specific automation requirements:

Workflow Capabilities

Zapier handles multi-step workflows across multiple applications, while IFTTT operates on if-this-then-that automation. Zapier's architecture supports conditional paths, filters, and formatters that business processes need, but IFTTT's simpler design works well for personal automation while lacking the conditional branching business workflows typically require.

With Zapier, you can connect trigger events to several actions with conditional paths. The Paths feature enables if/then branching, filters control which data moves through workflows, and formatters change information between steps. That said, you'll need to understand trigger-action architecture to set things up. Business users will spend time learning how to structure workflows using visual elements.

IFTTT operates differently, using single trigger-action pairs called Applets. Instagram post saves to Dropbox. Location changes adjust thermostat. If you need complex sequences, you'll need to chain multiple Applets together, which creates connections between separate automations.

Simple automations work well. "When I post on Instagram, save the photo to Google Drive" takes minutes to configure and runs reliably. On the other hand, complex business processes requiring conditional logic and data transformation exceed what IFTTT was designed to do.

This limitation points to a broader pattern: Both platforms are good at workflow execution: routing data based on conditions and triggering actions. However, when processes require complex judgment calls, like assessing content quality against nuanced guidelines or analyzing documents with varying structures, workflow tools reach their limits. These scenarios need decision intelligence capabilities that work alongside data routing, handling the "thinking" while workflow tools handle the "plumbing."

Integration Ecosystem and App Support

Zapier provides an extensive catalog of business applications compared to IFTTT's focused selection of consumer services and IoT devices. The breadth matters because Zapier provides deep integration with enterprise software, including Salesforce, HubSpot, Slack, Shopify, QuickBooks, and Asana, while IFTTT specializes in consumer IoT devices like Alexa, Google Home, Philips Hue, and Ring doorbells.

IFTTT takes a different approach, focusing on consumer services and smart home devices that business platforms rarely support. Social media platforms and personal productivity apps round out the catalog, though business software coverage is more limited by design. For personal automation and smart home control, IFTTT connects devices other platforms ignore. But for business processes, Zapier's integration breadth will provide more coverage.

Data Handling, Error Tracking, and Reliability

Zapier provides detailed error logs and automatic retry logic, while IFTTT offers basic activity logs with limited error visibility. The difference can matter when automation failures impact revenue, though IFTTT's approach works fine for personal automations where delays create minor inconvenience rather than operational problems.

When automations fail in Zapier, you can see what broke and why. Task history enables debugging, data transformation tools format, parse, and change information between steps, and higher-tier plans add advanced error handling. For business operations, this visibility matters. An e-commerce order sync fails, Zapier flags the error immediately, and you can address the issue before customers complain about missing tracking information days later.

IFTTT shows basic activity logs for successful runs, but error visibility is more limited. Failures sometimes go unnoticed until consequences appear, data transformation capabilities are basic, and applet execution can experience delays, which makes time-sensitive automations less reliable.

Understanding these technical differences helps you choose the right workflow platform, but there's a broader consideration: what workflow tools fundamentally can't do.

When Workflow Tools Need Decision Intelligence

Zapier and IFTTT handle data orchestration effectively but struggle when business processes require complex judgment calls. Both platforms move data between applications and trigger actions based on conditions, yet they can't assess content quality against nuanced guidelines, analyze documents with varying structures, or make contextual decisions that typically require human review. This limitation creates bottlenecks for operations teams managing processes that involve both data routing and complex reasoning.

Decision intelligence platforms like Logic work alongside workflow tools as an API layer. The integration pattern is straightforward: your workflow triggers and calls the Logic API with relevant data, Logic applies complex reasoning based on plain English rules, and returns a decision that routes the workflow appropriately. Zapier or IFTTT handles the plumbing, connecting systems and moving data, while Logic handles the thinking. Engineering completes the initial integration by connecting the API endpoint, then domain experts own the decision rules and update them directly in plain English when policies evolve.

This separation enables operational agility. When content moderation policies change weekly, operations teams update the decision rules directly while workflows remain unchanged. When document processing requirements evolve, business experts modify the reasoning logic without touching the data routing infrastructure. The workflow platform does what it does best, and decision intelligence fills the gap.

Real-World Example: Content Moderation at Scale

Garmentory, an online marketplace, relied on a 24-page standard operating procedure to guide contractor reviews of product listings. Determining policy violations, assessing image quality, and inferring correct categories required understanding context and making judgment calls. The workflow part was straightforward, routing listings for review and updating databases with decisions, but the decision-making created bottlenecks. During Black Friday, 14,000 items flooded the queue, overwhelming the four-contractor team managing manual reviews.

Garmentory integrated Logic into their existing workflow infrastructure. Product listings trigger the workflow, the workflow calls Logic's API with listing data, Logic applies the 24-page SOP as plain English decision rules, and the workflow routes items based on Logic's response. When moderation policies evolve, the operations team updates the decision rules directly without changing the underlying workflow.

The results demonstrate the impact of separating decision logic from workflows:

• 190,000 decisions processed monthly with consistent quality and reliability

• Error rates dropped from 24% to 2% through automated policy application

• Black Friday backlog eliminated as the 14,000-item queue became real-time processing

• Four contractor positions eliminated, with remaining reviewers focused on edge cases requiring human judgment

These outcomes show how decision intelligence complements workflow automation rather than replacing it.

With this complete picture of what workflow platforms can and can't do, you're ready to choose the right tool for your needs.