Gladia vs Deepgram for AI Agents: Compare the Action Contract

For the current Aident Loadout workflow, choose Deepgram when downstream automation needs a typed word-level receipt and explicit transcription controls. Consider Gladia when a one-URL input is the priority and you can run a reviewed fixture to confirm the returned shape. That is a comparison of the current Aident Actions, not a verdict on the providers' full platforms.

This distinction matters because provider feature pages do not tell an agent what it can safely invoke today. The useful contract is the Action that discovery returns now: required inputs, typed outputs, risk, current Aident credit quote, and the evidence a reviewer will receive after execution.

If your goal is to turn many interviews into supported findings rather than choose a provider, use the separate customer interview synthesis workflow. This guide owns the narrower selection question.

The Short Answer

The live catalog comparison on August 11, 2026 looked like this:

Do not turn those dated observations into permanent pricing or feature claims. Rediscover both Actions before each evaluation. A wrapper can change independently from the provider API behind it.

Gladia's own API documentation describes pre-recorded transcription, speaker diarization, and configuration such as subtitle generation in its transcription initiation API. The current Aident Action exposed a much smaller input and no typed output schema. That does not mean Gladia lacks those provider features. It means an agent should not assume they are available through this exact Action.

Deepgram documents how timestamps, utterances, and speaker diarization fit together. The current Aident Action exposed those concerns directly enough to define a stronger downstream acceptance test.

Start From the Live Actions

Install or update Aident Loadout from the canonical guide:

Follow https://aident.ai/SETUP.md

Confirm the account and Vault state:

aident account auth status
aident vault vault --action status

Then search by reader job instead of copying an internal identifier from an old article:

aident capabilities search \
  --queries '["transcribe one pre-recorded audio file with Gladia", "transcribe one pre-recorded audio file with Deepgram and return word timestamps"]' \
  --types '["action"]' \
  --targetEnv staging

Keep the exact Action names returned by discovery. Inspect both current contracts before sending media:

aident capabilities get \
  --name "<current Gladia transcription Action>" \
  --parts '["inputSchema","outputSchema","examples"]'

aident capabilities get \
  --name "<current Deepgram pre-recorded Action>" \
  --parts '["inputSchema","outputSchema","examples"]'

Record the required input fields, defaults, output shape, risk level, connection source, and price definition. If one result does not declare the fields your pipeline requires, treat that as an unknown to test, not a promise to infer.

Preflight the Same Authorized Sample

Use one short recording that you are authorized to send to both providers. A public fixture is easiest to reproduce. For private media, use a deliberately scoped access method and review each provider's data terms before upload. Do not place customer calls, credentials, or sensitive recordings in a casual agent prompt.

Preflight both current Actions with the same file:

aident capabilities preflight \
  --name "<current Gladia transcription Action>" \
  --input '{"audioUrl":"<authorized HTTPS audio URL>"}'

aident capabilities preflight \
  --name "<current Deepgram pre-recorded Action>" \
  --input '{"audio_url":"<same authorized HTTPS audio URL>","content_type":"audio/wav","model":"nova-3","smart_format":true,"punctuate":true}'

Preflight should validate the exact input and return the current estimate without dispatching the provider. Stop when an input is invalid, the media URL changed after review, a required connection is missing, or an estimate is unavailable or above your ceiling. Do not bypass the integration layer to make an inconvenient result disappear.

The dated review used a public 17.566-second WAV. Deepgram preflighted at exactly 1 Aident credit. Gladia preflighted at exactly 2 credits, but its level-3 write was not executed because this research run did not have an explicit risk acknowledgement for that call. The asymmetry is intentional: verified evidence is better than a simulated head-to-head claim.

What the Deepgram Receipt Proved

One approved Deepgram execution completed successfully with Nova-3. The typed result contained:

• model metadata and a version dated July 31, 2025;

• one channel and a 136-character transcript;

• 28 word objects;

• word, punctuated_word, start, end, confidence, speaker, and speaker_confidence fields on every word; and

• no utterance objects in that particular response.

The words themselves are irrelevant to the provider decision, so they are not reproduced here. The durable evidence is the shape: downstream code can validate timing, confidence, and speaker fields without parsing prose from the agent.

That result does not prove accuracy across accents, noise, languages, meetings, phone audio, or long files. It proves that one current Action returned a typed receipt for one bounded public fixture. A real evaluation needs a small, representative corpus and expected outcomes defined before either provider runs.

Six Acceptance Checks for an Agent Transcription Action

1. Schema discoverability

Can the agent identify every required input and output field before execution? If the result contract is open, require a fixture run and version the normalizer against the observed response.

2. Media boundary

Can both Actions consume the same authorized file without silently transcoding, truncating, or changing the channel count? Record duration, format, sample rate, and channel count with the fixture.

3. Timing integrity

If the next step creates clips, citations, or evidence links, verify monotonic start and end times. Reject negative times, overlaps that violate your policy, and words beyond the media duration.

4. Speaker and utterance behavior

Do not treat a speaker field as accurate diarization merely because it exists. Use a multi-speaker fixture with known handoffs, score diarization separately, and decide whether your pipeline needs word, utterance, or segment boundaries.

5. Normalization provenance

Preserve the raw provider result and write normalized output as a separate versioned artifact. Store the Action name, model, model version when returned, options, execution time, and normalization version. This keeps a later schema change explainable.

6. Risk and cost receipt

The approved input, preflight quote, risk acknowledgement, and execution result should describe the same request. Credit approval and Action-risk acknowledgement are separate decisions. Use the cost guardrails workflow when an estimate crosses your local ceiling.

When to Choose Each Current Action

Choose the current Deepgram Action when:

• downstream automation requires typed word timing or confidence fields;

• you need input controls such as model, language, punctuation, formatting, or diarization;

• the one-credit dated quote fits the approved test; and

• one successful fixture is enough to justify a broader representative benchmark.

Consider the current Gladia Action when:

• a minimal one-URL invocation is valuable;

• you are willing to inspect a reviewed proof response before writing a normalizer;

• the two-credit dated quote fits the approved test; and

• a human explicitly acknowledges the current write risk before execution.

Stop and redesign when neither Action declares the contract you need. For example, provider documentation may advertise subtitles or diarization while the discovered Action does not expose the necessary control. Search for another current Action, narrow the workflow, or keep that feature outside the automated path. Do not smuggle provider-specific assumptions into a generic transcription step.

A Safe Comparison Prompt for Codex or Claude Code

Follow https://aident.ai/SETUP.md

Compare the current Gladia and Deepgram pre-recorded transcription Actions for
one authorized audio fixture. Use the installed public aident CLI. Confirm auth
and Vault state, discover by job in staging, inspect both input and output
schemas, and preflight the exact same file for both providers.

Before execution, show me a table with the exact public Action names, required
inputs, output fields, risk levels, current Aident credit estimates, and any
unknowns. Do not copy internal identifiers into the report. Do not execute if
the input is invalid, an estimate is unavailable or above 3 credits, or the
media URL changed after review.

Wait for my approval before either provider call. If approved, execute each
Action at most once with the reviewed input and the narrowest required risk
acknowledgement. Preserve raw receipts separately. Compare schema coverage,
timing integrity, speaker behavior, confidence fields, duration, and credits.
Do not publish or retain transcript text in the comparison report

The success condition is not "both commands returned text." It is a reviewable decision backed by two exact contracts, two bounded quotes, comparable fixtures, and acceptance checks that match the next step in your agent workflow.

Failure Matrix

Set Up the Comparison

Follow https://aident.ai/SETUP.md

Set up Aident Loadout and compare one approved transcription fixture

Sources

• Aident Loadout setup guide, reviewed August 11, 2026.

• Live Aident Loadout catalog, schemas, preflights, and one Deepgram execution, inspected August 11, 2026.

• Gladia pre-recorded transcription quickstart, reviewed August 11, 2026.

• Gladia transcription initiation API, reviewed August 11, 2026.

• Gladia speaker diarization documentation, reviewed August 11, 2026.

• Deepgram guide to timestamps, utterances, and diarization, reviewed August 11, 2026.

Refresh this comparison when either Action changes its input, output, risk, pricing, or connection contract, or when a representative benchmark replaces the single-fixture evidence.