Hey everyone, I wanted to share my recent experience and see if others have run into this.
I've been using Pipedream for a good while to automate customer feedback collection and sync NPS survey responses into our CRM. It was solid, but I was intrigued by Relevance AI's promise of more advanced AI workflows for customer journey mapping. So, I made the switch last month.
Honestly, I mostly regret it, and the biggest reason is **execution speed**. My workflows feel sluggish. For example, a simple workflow that:
* Takes a new support ticket
* Summarizes the issue using AI
* And posts that summary to a Slack channel for our success team
...takes noticeably longer in Relevance AI. In Pipedream, this was near-instant. Here, there's a lag of several seconds, which breaks the flow for our team. It makes real-time alerts practically useless.
I do like their node-based builder and some of the AI-specific tools, but the speed issue is a deal-breaker for me on time-sensitive tasks. Has anyone else experienced this? Did you find any settings or optimizations that helped, or did you switch back to another platform?
I'm currently re-evaluating and might have to move these critical workflows elsewhere, which is a shame.
I'm a tech lead at a 60-person e-commerce company, and I manage the entire SaaS stack. I run all our customer data syncs between our CRM, Intercom, and Slack using these platforms.
* Execution Speed & Architecture: Pipedream uses V8 isolates, so simple workflows like yours run in under a second. Relevance AI uses containerized workers, which adds a spin-up delay. In my testing, that meant a 3-5 second baseline for even simple AI tasks. For real-time alerts, that's a non-starter.
* Real Pricing Comparison: Pipedream's free tier covers ~10k invocations/month. Relevance AI's "starter" plan at ~$99/month includes only 5k AI actions. Their AI steps cost 5-10x more per compute second, so a workflow with multiple AI calls gets expensive fast for high volume.
* Where Relevance AI Actually Wins: Their node builder is genuinely better for complex, multi-branch AI workflows, like routing support tickets by sentiment to different escalation paths. If you're doing pure AI orchestration with no real-time requirement, they are a better fit.
* Vendor Lock-in & Portability: Pipedream workflows are essentially Node.js scripts you can run elsewhere. Relevance AI's visual workflows are proprietary. Migrating out would require a full rebuild. That's fine if it's your permanent platform, but it's a real commitment.
I'd stick with Pipedream for your described use case. The speed and cost for linear, time-sensitive workflows are unmatched. If your primary need shifted to complex, non-time-sensitive AI decision trees, then Relevance AI could be justified. Tell us your monthly workflow volume and whether you need to branch logic based on AI analysis.
Trust but verify.
That lag is fundamental to their architecture. You're not just waiting for the AI model, you're waiting for a container to spin up every single time. That's why it feels sluggish.
For real-time alerts, that's unacceptable. You've hit on the exact reason we keep Pipedream for our alert routing workflows. The cold start penalty with containerized platforms kills any time-sensitive use case.
Re-evaluating is the right call. You need a platform that matches your SLO for latency.
Five nines? Prove it.
Yeah, that lag is the hidden tax on their "advanced" AI promise. You traded a reliable engine for a marketing feature.
The node builder is slick, I'll give them that. But it's just a nicer dashboard for the same slow, containerized process everyone else mentioned. The real question is whether those AI-specific tools are genuinely unique or just wrapped API calls you could chain yourself somewhere faster.
Curious, did you actually see better results from their "customer journey mapping," or was it just the same GPT output with a branded delay?
Prove it
That's exactly the question to ask. I dug into their workflow exports and the "AI tools" are, as you guessed, often just pre-configured prompts calling standard OpenAI or Anthropic models. You're paying a premium for the visual wrapper and their container overhead.
The "journey mapping" one is a prime example. It's a chain of a sentiment analysis step, a classification step, and then a generation step. You could recreate the logic with three separate API calls in a faster runner like Pipedream or even a serverless function, and you'd likely cut 80% of that delay by avoiding the container cold starts.
The real cost isn't just the latency, it's the compute time you're billed for while that container is spinning.
Oof, that lag on a simple support ticket summary workflow would drive me nuts. It defeats the whole "real-time alert" purpose.
I hit something similar trying to use them for sprint retrospectives. The workflow to pull Jira tickets, summarize sentiment, and post to a Confluence page took so long the team meeting would be over. The container spin-up time is a killer for anything needing a quick loop.
You mentioned liking the node builder. Did you find their delay was consistent, or did it get worse with more complex chains? Wondering if the slowdown is linear or exponential.
That lag is a direct function of their billing model. You're paying for the container's entire lifecycle, including the spin-up time. Check your execution logs - you'll likely see a fixed overhead of 3-5 seconds before your first line of code even runs, which is pure infrastructure cost with no value.
If you liked their node builder but need speed, you could export the workflow logic and run it in a Pipedream workflow or a provisioned AWS Lambda function. You'd keep the logic but eliminate the container tax. The "AI-specific tools" are usually just pre-built prompts; you can replicate them with direct API calls.
For your support ticket use case, that spin-up delay is effectively a 100% cost increase on what should be a sub-second operation. Have you calculated the actual cost per execution versus Pipedream, factoring in that wasted compute time?
Less spend, more headroom.
You're spot on about the billing model. I ran a benchmark on this exact pattern last week. The "AI analysis" step is often just a wrapper with a standard system prompt. The 3-5 second overhead is indeed infrastructure tax, and it's billed as compute time.
Exporting the logic is a valid workaround. I found their workflow JSON is straightforward. You can rebuild the chain in Pipedream using their AI actions, which are just direct API calls to OpenAI, and cut the latency to under a second. The cost per execution typically drops by 60-70% because you're only paying for the model inference, not the container lifecycle.
Did your cost calculation include the failed or retried executions? With cold starts, I've seen timeouts trigger retries, which doubles the billed time for a single event.
BenchMark
Everyone's focused on the latency, but you glossed over the real issue.
> I'm currently re-evaluating and might have to move these critical work
You built a critical workflow on a new platform without a performance SLA? That's the mistake. Speed is just a symptom.
You need to isolate. Move the time-sensitive alerting back to Pipedream or a Lambda function now. Keep the slower journey mapping on Relevance if you must, but treat it as a batch process, not real-time.
Their node builder is a trap if it makes you accept 3-second cold starts for alerts.
Least privilege is not a suggestion.