Ask AI

1. Telnyx

• Best for: In-region inference on owned infrastructure, with a path into voice
• Deployment model: Serverless on Telnyx-owned GPUs, in-region across the US, EU, and APAC, with Dubai and São Paulo next
• Pricing model: Per-token pricing, 1M free tokens per month, no GPU rental or compute surcharges

Frontier models on owned GPUs

Telnyx Inference is serverless, pay-per-token access to frontier open-weight models on GPUs Telnyx owns and operates. Four curated models cover real-time and voice, reasoning and agents, cost-efficient intelligence, and balanced workloads, and the API is OpenAI-compatible.

Most providers rent GPUs from a cloud vendor, adding a margin to every token. Because Telnyx runs an owned GPU network instead, throughput stays high and price follows the infrastructure. The short catalog is deliberate.

In-region inference and a voice path

Inference stays in your users’ region because the GPUs are physically there across the US, Europe, and APAC, which makes data residency a property of the architecture rather than a premium tier. Transcription, speech, and voice run on that same infrastructure.

Migration is usually a base URL change and new credentials, done in an afternoon. Pricing is per-token only with no GPU rental or compute surcharges, and 1M free tokens every month keeps cost predictable.

2. Together AI

• Best for: Research-driven serverless across a large model catalog
• Deployment model: Serverless, dedicated endpoints, and self-service GPU clusters
• Pricing model: Per-token, with GPU-hour pricing for clusters and batch jobs up to roughly half the cost

Together AI is an inference cloud with a strong research pedigree and a catalog past 200 open-source models, plus self-service GPU clusters for teams that want to train and serve on their own fleet. For breadth of model choice and cluster access, it is one of the most complete options on this list.

The tradeoff is concentration. Serverless runs from US data centers, with no advertised in-region serverless for the EU or APAC, so traffic from other regions crosses borders to reach a model. Teams that need inference to stay in-region by default, on infrastructure the provider owns end to end, tend to land on Telnyx.

3. Fireworks AI

• Best for: Enterprise control and fine-tuning on open models
• Deployment model: Serverless plus on-demand and reserved dedicated, bring-your-own-cloud
• Pricing model: Per-token by model size, GPU-second or GPU-hour for dedicated

Fireworks AI sells enterprise control over open models. It runs serverless across many regions, supports on-demand and reserved dedicated deployments, brings your own cloud, and has a mature fine-tuning and reinforcement-tuning story that larger teams build on. Its first-token speed on short prompts is among the fastest here.

Underneath, Fireworks orchestrates across cloud providers it does not own, and outside the US the global footprint is dedicated deployments rather than serverless. The margin of the cloud beneath it still rides on every token, and long-output throughput trails its short-output speed. Teams that want owned hardware with serverless that stays in-region, and a path into voice on the same stack, move to Telnyx.

4. Baseten

• Best for: Production multi-model orchestration
• Deployment model: Cloud, self-hosted, or hybrid via Truss containers
• Pricing model: Pay-as-you-go on GPU and tokens, Pro and Enterprise via sales

Baseten is built for production multi-model orchestration. Its Chains framework lets teams wire several models into one pipeline in plain Python, deployment runs in Baseten Cloud or self-hosted or hybrid through open-source Truss containers, and the developer tooling is well regarded. For pipelines that need custom containers and per-component hardware, it is a strong choice.

The flexibility has a cost. Baseten orchestrates on rented cloud GPUs rather than hardware it owns, regional serverless availability is not published, and container management adds overhead for teams that only want a drop-in endpoint. Telnyx hosts its models on its own GPUs with published throughput and a serverless endpoint that needs no container work, which is a simpler path when orchestration is not the requirement.

5. Modal

• Best for: Code-first GPU deployment and elastic scale
• Deployment model: Python SDK deploy, region selection with a us-east-1 control plane, scale to zero
• Pricing model: Per-GPU-second across the NVIDIA fleet, plan tiers, 1.5 to 1.75x regional multiplier

Modal is code-first AI infrastructure. You write Python functions, decorate them, and deploy them onto Modal’s GPU fleet, with memory snapshots that cut cold starts and scale-to-zero when traffic drops. For teams that want full control of the container, the GPU, and the serving logic in code, it is one of the most flexible options here.

That control is also the cost. Modal is a deployment platform rather than a drop-in API, so an OpenAI-compatible endpoint means deploying vLLM yourself, and there is no hosted model catalog to call by name. Region selection exists on the Team and Enterprise plans, but every input and output still routes through a us-east-1 control plane, so in-region residency is bounded by that hop. Teams that want a hosted model behind an OpenAI-compatible API, in their users’ region end to end, with a path into voice on the same stack, move to Telnyx.

How we evaluated these inference providers

We scored every provider on the four things that decide inference cost and latency at production scale. Model count was not one of them.

• Owned infrastructure: Whether the provider runs models on GPUs and a network it owns rather than renting cloud capacity that adds a margin to every token.
• Single operational domain: Whether inference is one stack with one bill and one team to call rather than one component in a multi-vendor pipeline you integrate and debug yourself.
• In-region by default: Whether inference stays in your users’ region as a property of the architecture rather than a premium dedicated deployment that costs extra.
• Co-located voice latency: Whether transcription, inference, and speech run on co-located infrastructure where the call lands rather than traveling between providers across the public internet.

Telnyx is the only provider here that clears all four, which is why it sits at the top of the list.

Choose inference built on infrastructure you own

Inference pricing converges. What does not converge is who owns the stack underneath it. The providers that rent their GPUs carry a margin they cannot remove, and the ones that route everything through US regions add distance they cannot optimize away.

Telnyx hosts frontier models on its own GPUs, in your users’ region by default, on the same infrastructure that runs its voice AI agents and messaging. That is why the savings are structural and the latency is predictable, and why a team can start on inference and add voice agents later without a second vendor.

Start with 1M free tokens every month and see what inference looks like on infrastructure built for it.