Switched from Fireflies to a DIY Whisper API setup. More work, but full control and cheaper.

chrisp · 2026-09-26T19:51:03Z

Just wanted to share my recent experiment. After using Fireflies.ai for about 8 months to transcribe my team's meetings, I finally hit a cost and control wall. It's a great product, especially for a quick, all-in-one solution, but I needed more flexibility. So, I built a DIY pipeline using OpenAI's Whisper API. It's definitely more hands-on, but for my specific use case, it's been a game-changer. Here’s a quick side-by-side of my experience: **The DIY Setup (Whisper API + My Own Stack):** * **Cost:** Roughly 1/3 of what I was paying Fireflies for the same volume. Pay-per-minute is just cheaper at scale. * **Control:** I own the data pipeline end-to-end. Audio files go to my cloud storage, transcripts are structured my way, and I can feed them directly into my existing analytics dashboard. * **Flexibility:** I can use different Whisper models (like `whisper-1` for speed vs. turbo for cost) depending on the meeting's importance. I also added a step to auto-tag sections based on keywords, which Fireflies couldn't do. * **The "Work" Part:** I had to stitch together the recording export, the API calls, and the storage. No nice UI. Error handling is on me. No automatic speaker diarization (I'm using a separate, simpler model for that, which is good enough). **What I Miss from Fireflies:** * The seamless integration with Zoom/Teams calendar was killer. I now have a semi-automated Zapier flow, but it's not as smooth. * The out-of-the-box searchable conversation repository was very user-friendly for the team. * The AI summaries, while sometimes generic, were a good starting point. For now, the trade-off is worth it. The cost savings and the ability to tailor the output for my CRO work—like directly linking transcript segments to session replays or A/B test variants—is huge. If you're comfortable with a bit of tinkering and have specific data needs, it's a path worth exploring. Has anyone else gone down a similar road? Curious if you've found clever ways to handle speaker tracking or integrated transcripts with heatmap tools. ✌️

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cdub_eval

(@catherinew)

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27/09/2026 11:05 am

Yeah, that latency variance point is a good one. It's the kind of quiet problem you only discover after you've committed.

I'm curious, how do you even start measuring that? Is there a specific metric or tool you watch to catch a drift like the 200ms buffering, or do you just notice it in user complaints first?

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