Our data team uses Python for everything. We're evaluating meeting transcription tools specifically for their API—we need to reliably pull transcripts and timestamps into our own data pipelines for analysis.
Can anyone share hands-on experience with the APIs for tl;dv and Fireflies.ai? I'm looking for specifics:
- Which has more straightforward API authentication and rate limits?
- How clean is the transcript data structure (JSON) for parsing?
- Any major limitations you hit when automating data extraction?
- Webhook reliability for real-time ingestion?
Pricing pages mention API access, but real-world usage details are sparse. Our stack is mostly FastAPI and pandas, if that helps frame any advice.
Still learning.
Based on your Python and FastAPI stack, I'd lean towards Fireflies for the API integration, but with a critical caveat regarding their transcript structure. Their authentication uses a straightforward API key model with clear, documented rate limits per tier. The webhook implementation for real-time ingestion has been reliable in my experience, consistently pushing data within seconds of a meeting ending.
The main friction point is the transcript JSON. While it's comprehensive, it nests speaker diarization, timestamps, and sentiment analysis in a way that requires more preprocessing with pandas than you might expect. You'll likely need to write a dedicated parser to flatten the structure for your pipelines. tl;dv offers a cleaner, more linear transcript object, but their webhook system felt less deterministic during our tests, sometimes requiring a poll-based backup. For pure data extraction simplicity, tl;dv's output wins, but for automation reliability, Fireflies' webhooks and rate limiting are more predictable.
Check the SLA.
You're spot on about the parser overhead for Fireflies. We hit the same issue and benchmarked the extraction time. Flattening their nested JSON to a pandas DataFrame added a consistent 300-500ms latency per meeting transcript on our infrastructure, which became a bottleneck for batch processing.
This pushed us towards implementing a custom Pydantic model for their transcript response. It validated the structure on ingestion and provided a cleaner interface, but that's extra development lift they don't advertise.
Did you measure any variance in webhook delivery latency during high-load periods? We saw the 'within seconds' claim hold, but only after we increased their default webhook timeout.
Show me the numbers, not the roadmap.
The Pydantic model is the right fix, but calling it "extra development lift they don't advertise" is generous. It's technical debt you're forced to take on because they prioritize packing features over a usable data schema. Their API feels built for a demo, not for pipelines.
On webhook timeouts, we saw the same pattern. The 'within seconds' guarantee only applied after we manually tripled their defaults. It makes you wonder if their reliability stats are just measuring properly configured clients, not the out-of-box experience.
Just my 2 cents