Top 5 Modal alternatives for Inference

If your inference bill is metered in GPU-seconds and your team is writing Python to define infrastructure before it can serve a token, the provider is the constraint, not the model. Here is where teams go when they outgrow Modal.

Modal is a code-first compute platform. You write a Python function, decorate it, and modal deploy runs it on Modal's GPU fleet. There is no managed OpenAI-compatible chat endpoint and no hosted model catalog. You ship a vLLM container, you size the GPU, you own the serving logic. That works until you want to stop running infrastructure and start running an inference API.

What actually decides cost and latency at production scale is whether you pay per token or per GPU-second, whether the provider hosts a curated model behind a managed endpoint or makes you build one, whether inference stays in your users' region without routing through a US control plane, and whether voice and transcription can run on the same infrastructure as the call. We ranked these five providers on exactly that.

Here are the five best Modal alternatives for inference, starting with the one we built.

Modal alternatives

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: Skipping the container build and calling a managed endpoint with a 200+ model catalog
• Deployment model: Serverless chat completions, dedicated endpoints, and self-service GPU clusters for teams that still want their own fleet
• Pricing model: Per-token serverless, 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, served from a managed serverless API and OpenAI-compatible by default. If you came to Modal so you could run open-weight models on GPUs and ended up maintaining vLLM containers, Together removes that work entirely. The model is already deployed, the endpoint is already there, and per-token billing replaces GPU-second math.

The tradeoff is concentration. Serverless runs from US data centers, with no advertised in-region serverless for the EU or APAC, so a Modal team trying to escape the us-east-1 control plane bottleneck swaps one US-centric routing problem for another. 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 teams that want fine-tuning and dedicated capacity without writing the serving layer
• Deployment model: Serverless plus on-demand and reserved dedicated deployments, with BYOC and air-gapped EKS options
• Pricing model: Per-token serverless priced by model size, with GPU-second or GPU-hour billing on dedicated

Fireworks AI is an inference platform with proprietary serving optimizations (FireAttention, FireOptimizer, adaptive speculation) and a managed fine-tuning track (SFT, DPO, and reinforcement fine-tuning) on top of open-weight models. For a Modal team that built a fine-tuning pipeline in Python because no managed option existed, Fireworks turns that pipeline into a billed service. Zero data retention is on by default and the platform is OpenAI and Anthropic Messages API compatible.

Where it converges with Modal's limit is geography and ownership. Fireworks runs on rented multi-cloud capacity across CSPs (AWS and Crusoe among them), and EU and APAC are available as dedicated deployments rather than as serverless. If the reason you are leaving Modal is the us-east-1 control plane routing every input and output through one region, a US-routed serverless tier on rented GPUs is a lateral move. Telnyx serves the same per-token API on owned GPUs that are physically in-region.

4. Baseten

• Best for: Production multi-model pipelines where one call fans out across several models
• Deployment model: Baseten Cloud, self-hosted in your VPC, or hybrid, all driven by Truss containers
• Pricing model: Pay-as-you-go GPU and token rates, with Pro and Enterprise tiers gated to sales

Baseten is a model deployment platform built around Truss, an open-source CLI for packaging models, and Chains, a pure-Python framework for multi-model orchestration without YAML. For a Modal team running multi-step pipelines (chunked transcription into an LLM into TTS, or RAG with a reranker) Baseten removes the work of stitching those calls together in your own Python and pushes the orchestration into a managed runtime with KV-cache routing and active-active failover.

It does not remove the underlying tradeoff that drove the move off Modal. Baseten orchestrates on rented cloud GPUs (AWS, Vultr) across 10-plus regions managed by their multi-cloud capacity layer, and they do not publish head-to-head throughput benchmarks. If you want owned infrastructure under the API rather than another abstraction over someone else's cloud, and you want inference colocated with telephony for a future voice path, Telnyx is the architectural fit.

5. DeepInfra

• Best for: Cheap per-token access to a broad open-source catalog with no minimums
• Deployment model: Serverless inference on shared infrastructure, with dedicated GPU clusters available on request
• Pricing model: Per-token for language models, per-minute for audio, and per-GPU-hour for custom deployments

DeepInfra is an inference cloud focused on price and breadth. The catalog runs past 70 language models (DeepSeek, Qwen, Llama, Mistral variants) plus image, audio, and embedding models, served from H100 and A100 GPUs the company operates rather than rents. For a Modal team that ended up writing infrastructure code just to serve a popular open-weight model behind an API, DeepInfra collapses that to a per-token call against a managed endpoint with no long-term commitment.

What it does not change is region posture or expansion path. DeepInfra does not publish regional inference availability and there is no carrier network, no telephony, no voice AI stack behind the API. If a Modal team is leaving because of us-east-1 routing and is going to add voice agents on top of inference, the alternative that solves both is Telnyx, with serverless GPUs in the US, EU, and APAC and STT, TTS, and the carrier network on the same infrastructure.

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.