Top 5 Together AI Alternatives for Inference

Together AI is the canonical research-cloud play. A 200+ model catalog, FlashAttention pedigree, ATLAS speculative decoding, and self-service GPU clusters. The catch shows up when you push it into production. Serverless workloads concentrate on US capacity, so traffic from other regions can cross borders to reach a model. Clusters move to GPU-hour billing on top of tokens. The breadth is real, but the architecture is rented and centralized.

Most inference vendors share that shape. They lease GPUs from a hyperscaler, run from a handful of US regions, and pass the cloud margin through on every token. That cost is structural, and no model swap on the same architecture removes it. The fix is architectural.

What actually decides cost, latency, and data residency at production scale is who owns the infrastructure under the API, whether inference stays in your users' region without a premium tier, and whether voice, transcription, and speech run in the same place as the call. We ranked these five providers on exactly that.

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

Together AI 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. Fireworks AI

• Best for: Teams leaving Together for enterprise control, fine-tuning depth, and dedicated deployment options
• Deployment model: Serverless plus on-demand and reserved dedicated, BYOC, air-gapped EKS for regulated workloads
• Pricing model: Per-token by model size, GPU-second or GPU-hour for dedicated, per-training-token for fine-tuning

Fireworks AI is the closest direct substitute for Together on the enterprise end. Where Together's pitch is research breadth, Fireworks pitches enterprise control: reinforcement fine-tuning as a managed service, multi-LoRA serving on a single base deployment, BYOC, air-gapped EKS, and an AWS partnership for buyers who want the inference platform to live inside their existing cloud.

The architecture is still rented. Fireworks orchestrates across 8 major clouds via its Virtual Cloud abstraction, all of them third-party hyperscalers. If you are leaving Together because of inconsistent throughput on flagship models or fine-tuning gaps, Fireworks closes those gaps but keeps the cloud margin. Teams that need inference in the user's region on infrastructure the provider owns end to end tend to land on Telnyx.

3. Baseten

• Best for: Teams leaving Together because they need multi-model pipelines, custom containers, or hybrid deployment, not just a serverless catalog
• Deployment model: Cloud, self-hosted in your VPC, or hybrid via Truss containers, with Chains for multi-model orchestration
• Pricing model: Pay-as-you-go on GPU and tokens, Pro and Enterprise via sales, volume GPU discounts at the Enterprise tier

Baseten is the answer when Together's serverless catalog is not the unit of work you need. Truss lets you deploy any model as a container, Chains stitches models into pure-Python pipelines (RAG, chunked transcription, multi-step image generation, AI phone calling), and the platform runs Multi-Cloud Capacity Management across 10+ clouds with 99.99% uptime and active-active failover. The Speculation Engine and disaggregated serving target up to 2-3x TPS over a stock stack.

The tradeoff is the same shape as Together's. Baseten orchestrates on rented cloud GPUs rather than owning the hardware, and its regional serverless map is not published in detail. If you are leaving Together for orchestration and deployment flexibility, Baseten delivers it. If you also need inference to stay in your users' region on owned infrastructure with a co-located voice path, Telnyx is the closer fit.

4. Modal

• Best for: Teams leaving Together because they want to define the runtime in code, not consume a hosted catalog
• Deployment model: Python SDK deploy, region selection with a us-east-1 control plane, scale to zero, GPU memory snapshots for faster cold starts
• Pricing model: Per-GPU-second across the NVIDIA fleet (T4 through B200), plan tiers from Starter to Enterprise, 1.5-1.75x regional multiplier

Modal is the opposite shape of Together. There is no hosted, OpenAI-compatible chat endpoint and no curated model catalog. You write Python functions, decorate them, pick the GPU, and modal deploy runs them on Modal's fleet. OpenAI-compatible endpoints exist only via user-deployed vLLM wrapped in @modal.web_server. GPU memory snapshots cut LLM cold starts from roughly 70 seconds to about 12, and the platform elastically scales to 1,000+ GPUs.

If you are leaving Together because the abstraction is too thin, Modal goes thinner: you trade the hosted catalog for control over the container, the GPU, and the serving logic. Two architectural caveats follow. All function inputs and outputs route through Modal's us-east-1 control plane regardless of which region the function runs in, so true in-region residency is constrained by the control plane, not by function placement. And there is no carrier network or voice stack underneath. Telnyx hosts the model and runs in-region end to end, which is the closer fit when you wanted Together's API surface but not Together's geography.

5. DeepInfra

• Best for: Teams leaving Together because the per-token bill is the line-item problem, and the catalog needs to stay broad
• Deployment model: Serverless across a broad open-source catalog, plus dedicated H100/B200/B300 GPUs via DeepCluster
• Pricing model: Per-token with cached input discounts, plus GPU-hour for dedicated (A100 from $0.89/hr, H100 from $1.79/hr, B200 from $2.79/hr)

DeepInfra competes with Together on exactly the axis Together is most exposed: per-token price across a broad catalog of open-source models. The pricing page advertises models like DeepSeek-V3 at $0.32 input and $0.89 output per 1M tokens, with cached input discounts on flagship models. DeepCluster dedicated GPUs cover larger or steadier workloads.

The structural picture stays familiar. DeepInfra runs on H100 and A100 GPUs in a centralized, US-concentrated footprint with no published regional residency story, no carrier network, and no voice AI path. There is no free tier, the minimum spend threshold is $20, and infrastructure is rented rather than owned, so the per-token discount is funded by margin compression rather than ownership. If you are leaving Together purely on price, DeepInfra is the obvious switch. If you are leaving Together because tokens cross borders to reach a model and inference lives separately from your voice stack, Telnyx solves the architecture, not just the line item.

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.