Real customer calls, accents, overlapping speakers, languages switching mid-sentence. That’s where Gladia beats Deepgram.
Deepgram boasts numbers that come from clean, scripted audio. Solaria was built for what real customer calls actually sound like: accents, background noise, crosstalk.
0%
0%
Overlapping speakers are where transcriptions quietly fall apart. A wrong attribution corrupts everything downstream. Gladia's diarization is more reliable than Deepgram.
0%
0%
Real conversations switch languages mid-sentence. Every one of Gladia's supported languages handles that live, with translation included. No separate pipeline, no dropped session.
0+
0+
Summaries, sentiment, entities, translation, and LLM-ready output come back in the same API call with your choice of 700+ models. Deepgram has no single-API equivalent.
400+
None
Every claim on this page comes from Gladia's open benchmark suite, tested on real conversational datasets, not just clean demo audio.
Record, transcribe, and enrich in a single call. No separate capture provider, no enrichment layer to build and maintain — replaces 2–3 separate vendors in a typical stack.
99.9%+ uptime. Thousands of parallel calls spin up in seconds — no pre-provisioning, no capacity forecasting, no backup APIs. When your product scales, Gladia scales with it.
Gladia is a French company, subject to GDPR and EU jurisdiction by default, not as a configured add-on. Deepgram is US-based; its EU endpoint provides regional routing, not full data sovereignty. At Gladia, audio is never used to retrain models.
A named contact and fast technical response from engineers who know your setup – not a ticket queue.
Dozens of teams have shared their Deepgram migration stories with us. Anonymized for privacy, their feedback surfaces consistent, real-world pain points worth considering.
“The issue with Deepgram is they don't really have solid multilingual or auto code-switching support.”
“Sometimes Deepgram tagged English calls as Hindi (70% of the time).”
“Accents throw it off completely.”
“When users spell names or emails, for example firstname.lastname@gmail.com, it just can't handle it.”
“We quickly hit a limit when spelling names or numbers. The model just started outputting random words.”
“They're not easy to work with. The process feels rigid, and they push big commitments upfront.”
“Their name and email recognition just wasn't great.”
“If you lock the language, it won't transcribe anything else. If you try multi-mode, it only does English and Spanish. Language detection only works on clips, not live audio. It's frustrating.”
“It took months to implement. We sent thousands of emails trying to fix issues. We were just done.”
Teams that migrate from Deepgram see fewer misfires on accents and entities, transcripts that don't break mid-conversation, and support that answers before it becomes a ticket.
Open methodology across Switchboard, DIHARD III, and real customer calls — not just clean demo audio.
Accuracy, latency, multilingual coverage, and pricing — side by side.
What Nova-3 costs on the base rate — and what diarization, redaction, and Audio Intelligence add on top.