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1. Telnyx

• Best for: Baseten teams ready to trade rented multi-cloud orchestration for owned infrastructure under the API
• Deployment model: Serverless on Telnyx-owned GPUs in your users' region, OpenAI-compatible, with no container framework or orchestration layer to author
• Pricing model: Per-token only, 1M free tokens per month, no GPU-hour billing and no sales call to see the production rate

Owned GPUs under the API

Telnyx Inference serves open-weight models on GPUs Telnyx owns and operates, behind an OpenAI-compatible API. Four curated models cover real-time and voice, reasoning and agents, cost-efficient intelligence, and balanced workloads. There is no container framework or orchestration layer to author.

Baseten orchestrates inference across rented multi-cloud GPUs. The portability is real, and so is the cost of running on infrastructure you do not own. Telnyx runs its owned GPU network instead, so throughput and price both follow the infrastructure rather than the platform on top of it.

In-region inference and a voice path on one stack

Inference stays in your users' region because the GPUs are physically there across the US, Europe, and APAC, with Dubai and S\u00e3o Paulo next. Data residency is a property of the architecture, not a regional environment you opt into per deployment. Transcription, speech, and voice run on the same infrastructure, with a carrier network underneath.

Migration is usually a base URL change and new credentials, done in an afternoon. Pricing is per-token only with 1M free tokens every month, no GPU-hour billing, and no separate dedicated-deployment tier to fund region selection.

2. Together AI

• Best for: Wider model catalog and cluster access than Baseten exposes
• Deployment model: Serverless across 200+ open models, dedicated endpoints, and self-serve GPU clusters from 8 to 4,000+ GPUs
• Pricing model: Per-token serverless with batch jobs roughly half price, plus GPU-hour clusters starting at $1.75/hr

Together AI is the closest thing to a research lab running an inference cloud. The catalog runs past 200 open-source models, FlashAttention and ATLAS speculative decoding ship inside the inference stack, and self-serve GPU clusters from H100 to GB200 spin up in minutes. If Baseten's Chains and Truss containers were more wiring than you needed, Together's per-model serverless is a step in the opposite direction.

What Together does not solve is the cloud underneath. Serverless runs from US data centers, with no advertised in-region serverless for the EU or APAC, and there is no carrier voice path on the platform. Telnyx serves a tighter catalog on owned GPUs in your users' region by default, with transcription and voice on the same infrastructure. Catalog breadth is one tradeoff, stack consolidation is another.

3. Fireworks AI

• Best for: Open-model fine-tuning depth (RFT, DPO, multi-LoRA) that Baseten's Model APIs do not surface
• Deployment model: Serverless across 18+ regions plus on-demand and reserved dedicated, BYOC, and AWS marketplace billing
• Pricing model: Per-token tiered by model size, GPU-second or GPU-hour for dedicated, zero data retention by default

Fireworks AI sells enterprise control over open models. The fine-tuning toolkit goes deeper than Baseten's managed APIs expose, with reinforcement fine-tuning, DPO, supervised fine-tuning, multi-LoRA serving, and BYOC deployments across 18 or more regions. Fireworks also publishes a voice-agent stack aimed at sub-500ms pipelines, which Baseten's Chains can compose but not host end to end.

The structural cost is that inference still runs on rented hyperscaler capacity, so every token carries a margin Fireworks cannot remove, and the AWS marketplace billing makes that margin explicit. Telnyx runs the same kind of workload on GPUs it owns, on a network it operates, with voice on the same infrastructure. Teams optimizing for single-stack TCO rather than throughput tend to land on Telnyx.

4. DeepInfra

• Best for: Published low per-token pricing across a 100+ open-model catalog
• Deployment model: Serverless OpenAI-compatible API plus DeepCluster dedicated B300 GPU clusters
• Pricing model: Per-token published across the catalog, per-GPU-hour on dedicated, no free tier

DeepInfra competes on price visibility. The catalog runs past 100 open models, the chat completions API is OpenAI-compatible, and rates for popular models like Llama 3.1 70B and DeepSeek-V3 are listed per million tokens on the public site. SOC 2 and ISO 27001 are in place, and dedicated deployments on H100, H200, B200, and B300 are billed by the GPU-hour for teams that need them.

The constraint shows up in the data centers. DeepInfra runs from US-based facilities, and the docs do not advertise region selection in the inference API. Workloads from European or APAC users cross borders to reach a model, and there is no carrier-side voice path on the same platform. Telnyx hosts the same kind of open models on owned GPUs in your users' region by default, with transcription, speech, and voice on the same infrastructure, so the savings from low published rates do not turn into latency and residency costs downstream.

5. Modal

• Best for: Teams who want to write the GPU and serving logic themselves, the opposite shape from Baseten
• Deployment model: Python decorator-based deploy, region selection on Team and Enterprise, us-east-1 control plane
• Pricing model: Per-GPU-second across the NVIDIA fleet, plan-tier monthly base, 1.5 to 1.75x regional multiplier

Modal sits on the opposite end of the Baseten spectrum. Where Baseten's Chains and Truss abstract the infrastructure away, Modal asks you to write the infrastructure yourself in Python. GPU memory snapshots cut cold starts from roughly 70 seconds to about 12 seconds on production LLMs, scale-to-zero handles bursty workloads, and per-GPU-second billing maps neatly to the cost of a specific function. There is no hosted model catalog and no Modal-operated chat-completions endpoint. You deploy vLLM yourself if you want one.

Modal's control plane is locked to us-east-1, so all inputs and outputs route through a single US region regardless of where the function runs. HIPAA via a BAA is reserved for the Enterprise plan only. Telnyx serves open-weight models behind a managed OpenAI-compatible API, on GPUs in your users' region end to end, with the voice and transcription stack on the same infrastructure. The right shape for teams that want inference without writing the platform.

Evaluation guidelines

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 actual infrastructure

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.