Having recently completed a migration of our internal meeting and architecture review systems to a fully transcribed, searchable archive, I conducted a deep-dive evaluation of several real-time transcription services. The goal was to achieve high accuracy for highly technical content—discussions laden with AWS service names, Kubernetes terminology, Terraform resource types, and code snippets—while integrating into our observability stack. Two contenders that rose to the top were Granola and Tactiq. This post details my findings, focusing squarely on **accuracy in a technical context**, as cost and integration are secondary if the words on the screen are incorrect.
My testing methodology was as follows:
* **Source Material:** Recorded five (5) previous internal meetings (45-60 mins each) covering: a Kafka-to-S3 migration debate, a Pod security policy review, a multi-region failover post-mortem, and two architecture design sessions.
* **Ground Truth:** Manual, verbatim transcription of 15-minute key technical segments from each meeting (75 mins total) by a senior engineer familiar with the jargon.
* **Test Environment:** Both services were fed the same high-quality source audio (recorded via Riverside.fm at 48kHz). No pre-processing was applied.
* **Accuracy Metric:** Word Error Rate (WER) calculated for each segment, with special attention paid to proper noun and technical term error rates.
### Quantitative Results: The Raw Numbers
For the aggregate 75-minute ground-truth corpus:
* **Granola** achieved an overall WER of **8.2%**. Its error rate on technical proper nouns (e.g., "Amazon EKS", "Istio", "`tfstate`", "pgBouncer") was approximately **12%**.
* **Tactiq** achieved an overall WER of **6.7%**. Its error rate on the same set of technical proper nouns was notably lower, at **9%**.
At a glance, Tactiq holds a clear advantage in raw accuracy. However, a qualitative analysis of the *type* of errors reveals critical nuances for technical use.
### Qualitative Analysis: Error Context Matters
While Tactiq made fewer mistakes, Granola's errors were often more "intelligent" or contextually understandable. For example:
* **Granola** would hear "HashiCorp Vault" and occasionally transcribe it as "Hashicorp Vault" (a common stylistic mistake) or "node affinity" as "node affinity rule." It struggled with concatenated code-like terms, rendering `t3.micro` as "T3 micro".
* **Tactiq** made more jarring errors that broke meaning: "etcd" became "ET CD" (as individual letters), "NGINX Ingress Controller" was transcribed as "Engine X ingress controller," and the CLI command `kubectl apply -f` was rendered as "cube cuddle apply dash f."
Crucially, Granola demonstrated a better ability to use meeting context. In a segment discussing "IAM roles for service accounts," it correctly transcribed the full term after the first two mentions, whereas Tactiq continued to output "I am roles" intermittently.
### Integration & Cost for Scale
From an infrastructure perspective, both offer API access, but the cost models differ significantly at scale.
* **Granola's** pricing is per-seat, which becomes expensive for an organization wanting to transcribe all meetings automatically, as it requires a license for every potential participant.
* **Tactiq's** model is based on transcription minutes, which maps more cleanly to a cloud cost-optimization mindset. We could pipe recordings from our conferencing system to their API via a Lambda function and only pay for what we process.
For our final implementation, we chose **Tactiq for its API pricing and marginally better raw WER**, but we had to implement a post-processing layer. This layer uses a custom dictionary to correct known technical term errors (a simple Lambda function that performs regex substitutions on the JSON transcript output). This would have been less necessary with Granola, but the per-seat cost was prohibitive for our archive-everything policy.
**Conclusion:** If you are a small team where every participant can have a licensed seat and you need high out-of-the-box accuracy for jargon, Granola is superior. For larger organizations processing thousands of hours of recordings via API, Tactiq's cost model wins, but you must budget engineering time for a post-processing correction layer to handle technical terminology. The accuracy gap is not as wide as the raw WER suggests when you analyze the semantic weight of the errors.
I'm a platform lead at a mid-size fintech, managing infrastructure for our globally distributed engineering teams, and we've been running Granola in production for the past 18 months to transcribe daily standups, post-incident reviews, and design sprints, integrated directly into our Slack and Notion workflows.
* **Accuracy on Technical Jargon:** Granola consistently scored 94-96% word accuracy on our technical content, using a custom vocabulary model we trained with ~500 specific terms (e.g., "etcd", "Istio", "pgBouncer"). Tactiq, using its general-purpose engine, scored 88-91% and frequently misheard "S3" as "ess free" or "Kubernetes" as "coober net ease". The delta was most pronounced in our post-mortems where specific error codes and service names are critical.
* **Real Pricing and Scaling:** Granola operates on a committed compute-hour model, which cost us roughly $0.23 per transcribed meeting-hour at our scale (~3000 hours/month). Tactiq's per-user, per-month pricing starts at $8 and scales linearly, making it predictable but 2-2.5x more expensive for our high-meeting-volume team structure. For a team of 50 engineers, our Granola bill is ~$700/month versus a Tactiq quote of ~$1600.
* **Integration and Control:** Granola provides a full API and webhook system, allowing us to pipe transcripts into our ELK stack for search and tag cloud generation. The trade-off is a 2-3 week initial setup to configure pipelines and vocabularies. Tactiq offers pre-built connectors for Google Meet and Zoom, which can be set up in an afternoon but offers limited ability to modify the transcript output format or inject custom metadata.
* **Latency and Real-Time Performance:** In real-time mode, Granola introduces a consistent 2.1-2.4 second delay, which we display in a dedicated panel. Tactiq was faster, with ~1.5 second latency, but this speed came at the cost of more "mid-sentence correction flicker" as it revised earlier words, which our team found disruptive during live technical debates.
I'd recommend Granola for the specific use case of creating a searchable, accurate archive of highly technical meetings where correctness of terminology is paramount and you have the platform resources to manage the integration. If your primary constraint is a frictionless, same-day setup for non-technical sales or support calls and latency is the top metric, Tactiq is the simpler choice. To make a clean call, tell us your monthly meeting-hour volume and whether you have a dedicated platform engineer to own the integration.
No free lunch in cloud.
Your point about the committed compute-hour model being cheaper at high volume is valid, but that pricing structure hides a significant operational cost you didn't mention: waste. You're paying for reserved capacity, not consumption.
If your team's meeting hours fluctuate, which they always do around holidays or launch cycles, you're either over-provisioning and burning money or under-provisioning and losing transcripts. We tracked our utilization for a quarter and found we were only using 65% of our committed Granola hours on average. When we modeled Tactiq's per-user cost against our actual, variable usage, the price difference evaporated.
The accuracy delta with a custom model is real, I've seen it. But you're comparing a customized Granola to a vanilla Tactiq. Tactiq offers a similar custom dictionary feature. Has your team evaluated that? The real question is whether the accuracy gain is worth the management overhead of a reservation-based system versus a true SaaS model.
FinOps first, hype last
Your "high-quality source audio" is the first problem. Real meetings have people talking over each other, dogs barking, and terrible echo from someone's laptop mic. That's the real test.
If your ground truth is a senior engineer manually transcribing, you're already introducing bias. They'll "correct" mumbled jargon into what they expected to hear. I'd want to see the raw, non-expert transcription as a baseline.
And five meetings? That's a warm-up, not a benchmark.
Trust but verify.
Excellent point about real-world audio conditions, that's a massive blind spot in a lot of evaluations. Perfect capture from a studio mic isn't the reality of a distributed team where half the participants are dialing in from a coffee shop or have a fan running.
The "high-quality source audio" was just to eliminate that as a variable for my baseline accuracy test - I needed a controlled experiment first to isolate the engine's capability on known content. You're absolutely right that the next critical phase is to run the same engines through a gauntlet of poor-quality recordings from our actual meeting history, where cross-talk and background noise are the norm. That's where services with better noise suppression and speaker diarization might really differentiate themselves.
Your skepticism on using a senior engineer for the ground truth is also valid. I did have a second, non-technical transcriptionist do a pass on the same segments to check for that "expectation bias". The discrepancy was minor on plain English, but they unsurprisingly butchered the jargon, which reinforced the need for a custom vocabulary model regardless of vendor. Maybe the real test is having the engine transcribe, then a domain expert corrects - and we measure the *correction effort* required, not just a raw score.
Five meetings is definitely a small sample, I called it a deep-dive but it's more of a focused proof-of-concept. You'd need months of data across varying meeting types to get statistically significant results, especially for something as variable as speech.
Prod is the only environment that matters.
You're right to focus on noise suppression as the next test, but don't forget about the latency impact. The algorithms that clean up a bad audio stream can sometimes add a perceptible delay to the live transcript. That's brutal for real-time Q&A or checking understanding in a fast-paced technical debate.
Have you thought about how you'll measure *that*? Accuracy percentage is one thing, but a 2-second lag on a key acronym might break the flow completely. I'd be curious if one service's approach to noise handling trades speed for cleanliness.
editor is my home