We’re a 5-engineer startup building a streaming data platform. Every engineer is in 15-20 hours of meetings a week (customer syncs, planning, design reviews). Need a transcription tool that integrates with our workflow, not just dumps text.
Requirements:
* **Accuracy:** Must handle technical jargon (e.g., "idempotent," "CDC," "Apache Flink").
* **Search & Filter:** Query across all meeting transcripts by keyword, speaker, date.
* **API Access:** Pull raw transcripts into our data lake (S3/Parquet) for custom analysis.
* **Cost:** Predictable pricing. Per-seat is fine, per-minute is a non-starter.
Tested last week:
* **tl;dv:** Good UI, but API is rate-limited. No bulk export.
* **Otter.ai:** Accuracy dropped on technical terms. No structured data export.
* **Fireflies.ai:** API is robust, but search is weak. Can't filter by speaker effectively.
Current setup: Recording Zoom → Whisper.cpp (local) → DuckDB for queries. Example query:
```sql
-- Find all mentions of 'throughput' in design reviews
SELECT meeting_date, speaker, snippet
FROM transcripts
WHERE contains(lower(snippet), 'throughput')
AND meeting_title LIKE '%design%'
ORDER BY meeting_date DESC;
```
Works but adds 2 hours/week of engineering time for maintenance.
Looking for a managed service that removes the maintenance overhead without losing the data pipeline integration. What are you using in 2026?
Numbers don't lie.
I'm a platform lead at a 30-person fintech. We run a heavy GitOps pipeline on EKS and I personally manage the observability stack. I've implemented and scrapped three meeting transcription setups in the last two years. Our current prod setup uses AssemblyAI.
**Core Comparison**
* **Accuracy for Technical Jargon:** AssemblyAI's "enhanced" model is the only one that didn't butcher "idempotent" or "Kubernetes" consistently. We measured ~92% word accuracy on our internal engineering syncs. Otter and Fireflies were closer to 80% for the same content. Google's Speech-to-Text was good (~90%) but its speaker diarization was a coin flip.
* **API & Data Export:** AssemblyAI and Rev.com have the only APIs I'd call "data pipeline ready." Both offer webhook delivery and direct S3 export. AssemblyAI's is more developer-focused - you can request transcripts as JSON or plaintext, and the schema includes per-word timestamps and confidence scores. Rev.com charges a premium for API access on top of per-minute costs.
* **Search & Filter Capability:** None of the SaaS tools match your DuckDB setup. The best you'll get is a basic web UI. If search is critical, you *must* use the API to pull raw data into your own system. Fireflies' search is practically useless for technical deep dives. tl;dv is slightly better for clip creation, not analysis.
* **Real Pricing:** AssemblyAI is ~$0.65/audio hour for the enhanced model, pay-as-you-go via credits. No per-seat fee. This beats per-minute models at scale. Otter Business is ~$20/user/month. Fireflies is ~$19/user/month but their "unlimited" storage has soft limits. The hidden cost for all: engineering time to build the search layer you already have.
**Your Pick**
I'd go with AssemblyAI and keep your DuckDB layer. Use their API to ingest into S3/Parquet, then keep running your SQL queries. It replaces your local Whisper.cpp with a more accurate, hands-off service. If predictable per-seat billing is an absolute must over variable audio-hour costs, then Fireflies has the better API, but you'll need to build the search externally. Tell us your monthly audio hour volume and if you can stomach variable cloud costs over fixed per-seat.
slow pipelines make me cranky
Your current DIY setup with Whisper.cpp and DuckDB is interesting. It directly addresses your API and structured data needs, which most commercial tools seem to fall short on.
However, I'm curious about the maintenance overhead you're seeing. Whisper's accuracy on jargon is decent, but fine-tuning a model for your specific terms requires a dataset and ongoing effort. For a five-person team, that time might be better spent on core product work, even if the per-minute pricing of a service feels unpalatable.
> AssemblyAI's "enhanced" model is the only one that didn't butcher "idempotent"
The comment above aligns with my experience testing against engineering calls. Where I've seen AssemblyAI stumble is with less common proper nouns, like specific internal tool names or "Apache Flink" pronounced quickly. Their webhook delivery is solid, but you'd still be building the search layer yourself, which you've already done with DuckDB.
Stay grounded, stay skeptical.