Data in AI-enbaled buckets can be vectorized in seconds to feed LLMs for fast, contextualized inference.
Add AI to your existing applications and workstreams with easy to use APIs, tutorials and demos.
Count on our dedicated GPUs to handle a high-volume of requests concurrently and scale automatically based on your workload to ensure optimal performance at all times.
Consolidate your AI workflows in one place. Store, summarize, embed and utilize your data in a range of models from a single user-friendly interface.
Choose the best model to fit your use case. we currently support models from OpenAI, Meta and MosaicML—with more on the way.
Go from data to inference in near-real time with co-location of Telnyx GPUs and Storage.
Leverage our dedicated network of GPUs to scale your AI-powered services effortlessly.
Thanks to our dedicated infrastructure Telnyx users can save over 20% vs OpenAI and MosaicML on inference alone.
cheaper than competitors
Instantly summarize internal documents to extract the most important information or condense for sharing with stakeholders
pages summarized instantly
Telnyx support is available around the clock—for every customer—so you can build what you need, when you need it.
Get started with Inference
Incorporate AI into your applications with ease via the portal or API.
Telnyx Inference now in open beta
Manage your AI infrastructure, embeddings and inference on one platform.
Easily incorporate AI into your applications, 20% less than competitors
$0.0004inference per 1K tokens
Interested in building AI with Telnyx?
We’re looking for companies that are building AI products and applications to test our new Sources and Inference products while they're in beta. If you're interested, get in touch!
Interested in testing Inference API?
Inference in AI refers to the process by which a machine learning model applies its learned knowledge to make decisions or predictions based on new, unseen data. It's the phase where the trained model is utilized to interpret, understand, and derive conclusions from data inputs it wasn't exposed to during the training phase.