---
title: "Together AI alternatives"
canonical: "https://router.xark.io/alternatives/together-ai"
description: "The realistic alternatives to Together AI for open-weight inference, fine-tuning and dedicated capacity, and when none is worth the move."
section: "alternatives"
updated: "2026-09-02"
source: "https://router.xark.io/alternatives/together-ai.md"
---

# Together AI alternatives

Together AI does three separate things — serverless inference, fine-tuning, and dedicated GPU clusters — and no single alternative replaces all three. If you left over per-token cost, DeepInfra is the first place to check. If you left because you also need training, Fireworks AI is the closer match. If you only ever used the inference half, the field is wide open.

## Why people leave

- On per-token inference their rates sit above DeepInfra on most models, which is the comparison a high-volume text workload eventually runs.
- Video model support is limited, so a stack that generates video is already paying a second vendor.
- Time to availability for a newly released open-weight model varies by model, which matters if adopting new weights quickly is part of how you compete.

## The options

| Option | Best for | Trade-off |
| --- | --- | --- |
| DeepInfra | The most common reason for leaving: per-token cost on text. DeepInfra runs its own GPU fleet at real scale and is frequently the lowest published rate on open text models, with a broader open-model catalogue than ours. | They publish their own rate only, with no official rate alongside it, so the markup question is one you answer yourself. Video is limited, spending controls are account-level rather than per key, and the time to add a new model varies — so this move fixes cost, not the other two reasons. |
| AI Token Router | Leaving over video or over new-model latency. Video generation runs on the same key and the same balance, we add newly released models within 24 hours of the weights dropping, and every rate is printed next to the model's official rate so the saving is stated rather than derived. | We do not fine-tune and we do not sell dedicated GPU capacity, so if you use either half of Together we replace only part of your stack. Seven of 24 catalogued models are callable today, we are a newer platform with a shorter reference list, and video is billed at a fixed five-second duration during rollout. |
| Fireworks AI | Keeping the training half. Fireworks has a serverless training API on open weights priced per token, a materially larger open-model catalogue, Standard / Priority / Fast tiers so you pick your own latency-cost point, and distribution through Microsoft Foundry / Azure if you are already there. | Their commercial pricing page carries no serverless rates — you click through to the docs and read across three tiers. Billing is postpaid, so usage accrues and is invoiced after the fact, and the serverless catalogue covers text and vision only: no image, audio or video endpoints. |
| Nebius Token Factory | Replacing Together's enterprise standing with something stronger. SOC 2 Type II, HIPAA, ISO 27001, a zero-retention data mode, a 99.9% uptime SLA, EU data residency in Finland and France, 60+ open models, and volume discounts that scale with monthly usage. | No public rate card — pricing sits behind a self-service portal and the headline number is not what you pay once discount tiers apply. Onboarding is enterprise-first, and there is no image or video generation at all: text, vision and embeddings only. |

## When to stay

Stay on Together AI if you fine-tune a model and serve it from the same platform, or if you hold dedicated GPU capacity there. Splitting training and inference across two vendors adds a weight-transfer step and a second bill to every experiment, and the per-token saving on inference rarely covers that for a team that is actively training. Their enterprise track record is also the real thing — if your procurement process weights vendor history heavily, moving to a newer platform is a cost you pay in reference calls rather than in dollars.

## Migrating

Inference-only migrations are small: change base_url, change the key, change the model id. The parts that do not travel are the platform features — fine-tuning jobs, hosted fine-tuned checkpoints and dedicated endpoint configuration have no counterpart on a plain serverless API, and a fine-tuned model you serve on Together cannot be pointed at another provider without moving the weights first. Check that before you plan the cutover, not after.

## Where the two rate cards overlap

**Availability:** 7 of the 24 catalogued models are callable today. The rest are listed at their intended rate but no configured upstream serves them, so a request naming one returns `no available channel`. Every table below marks which is which.

| Model | AI Token Router input | AI Token Router output | Together AI input | Together AI output |
| --- | --- | --- | --- | --- |
| Kimi K2.6 | $0.55/M | $2.30/M | $0.90/M | $3.60/M |
| GLM-5.2 | $0.82/M | $2.55/M | $1.30/M | $4.10/M |
| DeepSeek V4 Pro | $0.28/M | $0.55/M | $0.40/M | $0.82/M |
| DeepSeek V4 Flash | $0.090/M | $0.18/M | $0.13/M | $0.26/M |
| Qwen3 Embedding 8B | $0.030/M | — | $0.046/M | — |

## Questions

### Is there a cheaper alternative to Together AI?

On text, DeepInfra is frequently the lowest published rate on open models and sits below Together on most of them. We are 36–43% below each model's official rate on everything we carry, with the official rate printed alongside — check both against your own model mix rather than taking either claim on trust.

### Which alternative also does fine-tuning?

Fireworks AI has a serverless training API on open weights, priced per token, and serves the result. Hyperbolic rents on-demand H100 / H200 / B200 capacity if you would rather train on raw GPUs. We do not fine-tune at all.

### What replaces Together's dedicated GPU clusters?

Hyperbolic sells on-demand H100s, H200s and B200s with no quota limits and no long-term contract, plus a route through reserved clusters to private cloud. Note that their pricing and inference pages both return 404 and no per-token rate card is published anywhere on their domain, so budget a sales conversation.

### Do I have to rewrite my code to move off Together AI?

Not if you move to another OpenAI-compatible endpoint — that is a base URL, a key and a model id. The exception on this list is Replicate, whose prediction API is its own shape, which means rewriting the call sites rather than repointing them.

[Head to head: AI Token Router vs Together AI](https://router.xark.io/compare/together-ai.md)