Running AI models in production isn’t just about choosing the right model. You also need an inference platform that’s fast, reliable, and easy to scale as your applications grow.
The right platform can save you time by handling the infrastructure behind your models, so you don’t have to manage GPUs or worry about scaling everything yourself.
Some platforms are built for real-time AI, while others focus on making it easy to deploy custom models or work with popular open-source LLMs.
To help you find the right fit, we looked at three popular machine learning inference platforms: Telnyx, Baseten, and Together AI.
How We Compared These Platforms
We focused on the things that matter most when running AI models in production.
- Speed
Can it deliver fast responses for real-world applications?
- Ease of Use
How easy is it to deploy models and start building?
- Scalability
Can it handle more traffic as your application grows?
- Developer Experience
Does it offer helpful APIs, integrations, and tools for developers?
- Overall Value
Does it offer a good mix of performance, features, and pricing?
1. Telnyx
Telnyx isn’t just an inference platform. It combines AI inference with voice, messaging, and its own global network, making it a great choice for teams building real-time AI applications.
Because Telnyx owns much of its infrastructure instead of relying entirely on third-party providers, it has more control over performance and latency.
What We Liked
One thing we really liked is how easy it is to get started. Telnyx offers OpenAI-compatible APIs, so if you’re already using the OpenAI SDK, switching over is often as simple as changing your base URL.
We also liked that everything works together on one platform. Besides inference, you can also use voice AI, speech-to-text, text-to-speech, messaging, and telephony without having to connect several different services.
Another big plus is the focus on real-time performance. Telnyx runs inference on its own infrastructure with globally deployed GPUs and supports in-region deployments to help reduce latency for users around the world.
Where It Really Stands Out
- OpenAI-compatible APIs
- Built for real-time AI and voice applications
- Global GPU infrastructure
- In-region deployments
- Autoscaling without managing GPUs
- Voice AI, messaging, and inference in one platform
Who Should Choose It
Telnyx is a great option for businesses building voice AI, AI agents, or other real-time applications. If you want inference, communications, and AI tools in one place, it’s one of the strongest platforms available.
2. Baseten
Baseten has become a popular choice for teams that want to deploy machine learning models without spending time managing infrastructure. It’s built for production and makes it easy to serve custom models at scale.
Whether you’re deploying an LLM, an image model, or another type of AI model, Baseten gives you the tools to get it running quickly.
What We Liked
One thing we liked is how easy Baseten makes deployment. You can serve your own models through APIs without having to worry about setting up or managing GPU infrastructure.
We also liked its autoscaling. As traffic grows, Baseten automatically scales your deployments, so you don’t have to manually manage capacity.
Another plus is that it’s built for production use. Features like monitoring, model versioning, and deployment management make it easier for teams to keep AI applications running smoothly.
Where It Really Stands Out
- Easy deployment for custom AI models
- Automatic scaling
- Production-ready monitoring tools
- Supports a wide range of machine learning models
- Good developer experience
Who Should Choose It
Baseten is a great choice for teams building AI products that rely on custom models. If you want a simple way to deploy and scale models in production, it’s definitely worth considering.
3. Together AI
Together AI focuses on making powerful open-source AI models easy to use. Instead of managing GPUs yourself, you can access a large collection of models through simple APIs.
It’s a good option for developers who want flexibility without having to build and manage their own infrastructure.
What We Liked
One thing we liked is the large selection of open-source models available through the platform. That gives developers plenty of options depending on the type of application they’re building.
We also liked that Together AI supports both serverless inference and dedicated deployments. Teams can start small and move to dedicated resources as their workloads grow.
Another feature worth mentioning is fine-tuning. Developers can customize supported models with their own data while continuing to use the same platform for deployment and inference.
Where It Really Stands Out
- Large collection of open-source models
- Simple inference APIs
- Serverless and dedicated deployment options
- Fine-tuning support
- Easy to get started
Who Should Choose It
Together AI is a great fit for developers who want quick access to modern open-source models without managing infrastructure themselves.
Which Platform Should You Choose?
All three platforms make it much easier to run AI models in production, but they’re designed for slightly different use cases.
Baseten is a great choice if your main goal is deploying and scaling your own machine learning models with as little infrastructure management as possible.
Together AI is a strong option for developers who want easy access to a wide range of open-source models and flexible deployment options.
For us, Telnyx stood out as the best overall choice. It doesn’t just offer inference APIs, it also combines AI inference with voice, messaging, and its own global network, making it especially well suited for real-time AI applications. The OpenAI-compatible APIs, globally deployed GPU infrastructure, and support for voice AI all make it a platform that can grow with your projects.
If you’re looking for a platform that goes beyond basic inference and is built for real-time AI from the ground up, Telnyx is our top pick.