Deepgramvs.Otter.ai
Compare word error rate across accents, noise and specialist vocabulary, plus speed and cost per audio hour.
Short answer
This comparison is planned and has not been run. Deepgram is a developer API and Otter.ai is a meeting application, so the test feeds both the same ten recordings and measures word error rate per condition, speed and cost per audio hour. No result or winner is published.
Reviewed. Reviewed by our team from the vendor's pages and public sources, with an editor's rating. Not independently tested yet. No winner is declared and none of the content below is a finding.
Stage 2 of 5: Test conditions published
- 4
- Questions to answer
- 4
- Dimensions
- 0
- Documented differences
- 0
- Evidence records
Questions this comparison will answer
What we will examine, before any result exists.
This page lists what a fair comparison of Deepgram and Otter.ai needs to answer. It publishes questions first and conclusions only when evidence supports them.
- AccuracyWhat is each tool's word error rate on clear speech, accented speech, noisy audio and specialist vocabulary?
- Language coverageWhich terms and names does each miss?
- Privacy of recordingsHow long does each take to return a transcript?
- Speed and costWhat does each cost per hour of audio at list price?
What each side says
Vendor claims, labeled as such, and what is still untested.
These are vendor-reported claims, not test results. We list them so you can see what each side says about itself, and we mark everything we have not tested.
| Dimension | Deepgram | Otter.ai | Evidence |
|---|---|---|---|
| Accuracy | Not yet documented | Not yet documented | Untested |
| Language coverage | Not yet documented | Not yet documented | Untested |
| Privacy of recordings | Not yet documented | Not yet documented | Untested |
| Speed and cost | Not yet documented | Not yet documented | Untested |
Vendor terms differ by plan and change over time. Check each vendor's current terms for the exact plan you would use.
Where this comparison stands
Test conditions, differences and the conclusion.
- PlannedSelected for evaluation
- Test conditions publishedTasks, versions, settings, runs, pass criteria
- Testing runBoth products tested under the published conditions
- Evidence recordedEach result linked to an evidence record
- ConclusionAdded only when evidence supports it
Test conditions
Test conditions are published before results: the tasks, tool versions, settings, number of runs and pass criteria are fixed in advance.
- Recording set: ten recordings of about five minutes each, covering clear speech, regional accents, background noise and specialist vocabulary, each with a human-checked reference transcript. Both tools receive the same audio files.Published
- Measures: word error rate against the reference using a stated scoring method such as the NIST SCTK sclite tool, reported per condition and overall; missed terms listed; time from upload to finished transcript; and cost per audio hour at list price on a stated date.Published
- Scope note: Deepgram is a developer API and Otter.ai is a meeting application, so the test uses uploaded recordings for both and does not judge live meeting features.Published
- Retention and training-use statements are read from each vendor's pages and recorded as vendor-reported.Published
- Versions and plans: the plan, model and version of each tool are recorded for every run and published with the results.Published
- Repeats: every task is run at least three times per tool, alternating which tool goes first, and every run is reported, failures included.Published
- Fixed in advance: prompts, inputs, reference answers and scoring rubrics are written and published before any run, and the operator pastes prompts verbatim.Published
- Publication rule: nothing is published as a result until every planned run is recorded, and each figure is shown with its sample size.Published
Documented differences
Differences are listed here only when each is supported by a linked evidence record.
Conclusion
A conclusion is added only when the evidence supports it. Until then, this page makes no claim about which option is better.
Limitations
- No testing has been run, so there are no results.
- Test conditions are fixed before any run and may be extended, never changed after results exist.
- No winner is declared.
The planned test, in full
Conditions, criteria and result cells, fixed before any run. Every result says Not yet run.
Short answer: This comparison is planned and has not been run. Deepgram is a developer API and Otter.ai is a meeting application, so the test feeds both the same ten recordings and measures word error rate per condition, speed and cost per audio hour. No result or winner is published.
What are the test conditions?
- Recording set: ten recordings of about five minutes each, covering clear speech, regional accents, background noise and specialist vocabulary, each with a human-checked reference transcript. Both tools receive the same audio files.
- Measures: word error rate against the reference using a stated scoring method such as the NIST SCTK sclite tool, reported per condition and overall; missed terms listed; time from upload to finished transcript; and cost per audio hour at list price on a stated date.
- Scope note: Deepgram is a developer API and Otter.ai is a meeting application, so the test uses uploaded recordings for both and does not judge live meeting features.
- Retention and training-use statements are read from each vendor's pages and recorded as vendor-reported.
- Versions and plans: the plan, model and version of each tool are recorded for every run and published with the results.
- Repeats: every task is run at least three times per tool, alternating which tool goes first, and every run is reported, failures included.
- Fixed in advance: prompts, inputs, reference answers and scoring rubrics are written and published before any run, and the operator pastes prompts verbatim.
- Publication rule: nothing is published as a result until every planned run is recorded, and each figure is shown with its sample size.
What will we measure for Deepgram and Otter.ai?
| Question | Deepgram | Otter.ai |
|---|---|---|
| What is each tool's word error rate on clear speech, accented speech, noisy audio and specialist vocabulary? | Not yet run | Not yet run |
| Which terms and names does each miss? | Not yet run | Not yet run |
| How long does each take to return a transcript? | Not yet run | Not yet run |
| What does each cost per hour of audio at list price? | Not yet run | Not yet run |
How do we keep the comparison fair?
Every figure above stays empty until the runs exist. Prompts, inputs, answers and rubrics are fixed first, each task is repeated, failures are published, and judgment calls are labelled as editorial next to the measured numbers. If a condition has to change after results exist, the old results are kept and the change is logged on the updates page.
Read each review first: Deepgram and Otter.ai. See how to read an evaluation and the methodology.
The exact prompts, rubrics and datasets are in the full test protocols.
How do I compare them myself?
A method you can run on your own work today.
Gather recordings that match your real conditions and compare each tool's output with a reference transcript you trust. Test accents, background noise and specialist vocabulary separately, then review how recordings are stored and used and what consent rules apply to voice cloning.
- Gather sample recordings that match your real conditions.
- Compare output against a reference transcript you trust.
- Test accents, background noise, and specialist vocabulary separately.
- Review how recordings are stored, retained, and used.
- Check the consent and safeguard rules for voice cloning and synthetic voices.
Related reading
Frequently asked questions
Short answers to common questions.
Which is more accurate, Deepgram or Otter.ai?
We have not tested either, so we do not know. The planned test measures word error rate on the same ten recordings for both, reported per condition, with the scoring method stated in advance.
Are Deepgram and Otter.ai the same kind of product?
No. Deepgram is a developer API and Otter.ai is a meeting application. The test compares transcription of uploaded audio only, and does not judge live meeting features.
Can I run this comparison myself?
Yes. Use the same inputs for both tools, write your scoring rubric before you start, repeat each task at least three times, and keep failed runs in the results. Our guides describe how to read an evaluation and what to record.
Independence
How this page is funded and edited.
Editorial independence. Commercial relationships, affiliate links and sponsored placements are disclosed and never determine evaluation scores. See the affiliate disclosure and the editorial policy.