# Which workflow automation platforms have the most reliable uptime and the fewest failed task runs, based on user reviews?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">When a failed automation means a missed handoff, reliability matters more than features. Based on reviews, these <a class="a a--md" elv="true" href="https://www.g2.com/categories/productivity-bots">productivity bots</a> are the most dependable at volume:</p><ol>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/workato/reviews"><strong>Workato</strong></a> (4.7 stars, 777 reviews): The most reliable at scale in reviews, with one enterprise user citing 1,000–3,000 calls a minute handled cleanly. Built for high-volume, business-critical workflows.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/integromat-by-celonis-make/reviews"><strong>Make</strong></a> (4.6 stars, 332 reviews): Frequently called reliable and fast, with few failure complaints, a solid mid-volume choice.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/zapier/reviews"><strong>Zapier</strong></a> (4.5 stars, 2,083 reviews): Core zaps are dependable, but reviewers flag failed runs when an API or login changes, and debugging a multi-step failure can be fiddly.</li>
</ol><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">One honest note: <a class="a a--md" elv="true" href="https://www.g2.com/products/n8n/reviews"><strong>n8n</strong></a> (4.7 stars, 288 reviews) is loved on price, but reliability is its weak spot, self-hosted reviewers report crashes under heavy load and one broken step taking a whole flow down, so it needs solid error handling in place.</p><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">If reliability is the priority, I'd start with Workato, then Make. Which held up under real volume, and how often were you chasing failed runs? And what did your error handling end up looking like?</p>

##### Post Metadata
- Posted at: 3 months ago
- Author title: SEO Content Writer
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;&lt;span style=&quot;color: rgb(0, 0, 0);&quot;&gt;For a team past small-scale but not yet at enterprise volume, I&#39;d point to Make. Getting called out for reliability with few failure complaints at that mid-tier is exactly the kind of proof point worth looking for at that stage.&lt;/span&gt;&lt;/p&gt;

##### Comment Metadata
- Posted at: 16 days ago
- Author title: Marketing



### Comment 2

&lt;p&gt;The recovery path is probably where I’d separate these platforms. A failed task is much less damaging if the system preserves state and lets you replay only the failed step instead of rerunning the entire workflow. For high-volume automations, I’d test recovery from a downstream API outage and see how much manual cleanup remains once that service comes back.&lt;/p&gt;

##### Comment Metadata
- Posted at: 20 days ago
- Author title: Writer



### Comment 3

&lt;p&gt;Workato would be my first choice if failed runs have real operational consequences. I’d also want retries, clear error alerts, and a dead-letter or fallback path in place so one bad API call doesn’t quietly stall the entire workflow.&lt;/p&gt;

##### Comment Metadata
- Posted at: 20 days ago
- Author title: Marketer



### Comment 4

&lt;p&gt;Enterprise-grade platforms handle high volumes cleanly and tend to have built-in error handling, making failures easier to catch and recover from. For lighter-volume teams, most hosted platforms are reliable enough, though multi-step workflows are where debugging gets time-consuming when something breaks.&lt;/p&gt;

##### Comment Metadata
- Posted at: 3 months ago





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