model comparison
Llama vs Deepgram
Both are credible choices. The decision comes down to which property your workload actually depends on, and neither vendor pays us to say otherwise.
- Llama
- Meta
- Deepgram
- Open source
- Category
- model
Side by side
Llama
Open-weight models you can host yourself, the default when data cannot leave your building.
- Strongest at
- full control, no per-token cost, and viable air-gapped deployment
- Trade-off
- you own the infrastructure, the scaling and the evaluation work that a hosted API absorbs for you
- Vendor
- Meta
Deepgram
Speech recognition tuned for real-time streaming transcription.
- Strongest at
- low-latency streaming accuracy, which is what voice agents live on
- Trade-off
- Indian-accent performance needs verification against your own recordings before you commit
- Vendor
- Open source
How we would actually choose
Choose Llama when full control, no per-token cost, and viable air-gapped deployment is the property your workload depends on, and accept that you own the infrastructure, the scaling and the evaluation work that a hosted API absorbs for you.
Choose Deepgram when low-latency streaming accuracy, which is what voice agents live on matters more, accepting that Indian-accent performance needs verification against your own recordings before you commit.
In practice most production systems we build use both, routed by task. Standardising on one option for tidiness usually costs more than the tidiness is worth.
Orqent Labs holds no reseller commission on Meta or Deepgram. We benchmark both on your workload and report what the numbers say.
Questions
Llama or Deepgram, which should we use?
Pick Llama when full control, no per-token cost, and viable air-gapped deployment is what your workload depends on. Pick Deepgram when low-latency streaming accuracy, which is what voice agents live on matters more. Most production systems we build end up using both for different tasks rather than standardising on one.
What is the catch with Llama?
You own the infrastructure, the scaling and the evaluation work that a hosted API absorbs for you.
What is the catch with Deepgram?
Indian-accent performance needs verification against your own recordings before you commit.
Do you have a preference?
Not a fixed one, and we hold no reseller commission on either. We benchmark both on your actual workload and recommend from the result, which occasionally means recommending neither.
Still deciding between Llama and Deepgram?
Send us the workload. We will benchmark both and show you the numbers.
Or email bd@dtrasglobal.com · call +91 74118 77878