Auxen vs Together AI: per-minute or per-token?
Together AI charges per token across a serverless multi-tenant fleet — fast to start, scales with usage. Auxen runs a dedicated GPU instance per customer at a flat per-minute rate. If your traffic is bursty and exploratory, Together. If it's steady and you've committed to one model, Auxen's dedicated economics win.
At a glance
| Dimension | Auxen | Together AI |
|---|---|---|
| Shape of the product | Dedicated GPU instance running one model you pick. Stable HTTPS endpoint, OpenAI-compatible API. | Serverless multi-tenant inference fleet. Each call routes to whichever node has capacity. OpenAI-compatible API. |
| Pricing model | Per-minute of instance runtime. $0.15/hr (3–7B) up to $2.85/hr (70B+). Pause to stop the meter. | Per million tokens — roughly $0.20/M (small) to $0.88/M (Llama 3.1 70B Turbo) for inference; fine-tuned models priced separately. |
| Pricing at light load | Lightly-used dedicated instance still bills the full hourly rate. Pause-on-idle scheduling helps but doesn't eliminate the floor. | Wins at sporadic / low volume. A few thousand tokens/day on Llama 3.1 70B is cents per day. Together is designed for this shape. |
| Pricing at heavy load | 30M tokens/day on a 70B model costs the same as 100K tokens — $1.20/hr × 730 hr = $876/mo. Predictable. | 30M tokens/day on Llama 3.1 70B Turbo at $0.88/M ≈ $26/day, $792/mo. Past that point Auxen pulls ahead and the gap widens with usage. |
| Latency consistency | Dedicated GPU — no co-tenant queueing. Latency is whatever the model gives on the underlying hardware, but consistent. | Together invests in fast serving (Turbo variants offer aggressive throughput optimizations). Peak-hour variance exists on the shared fleet but is typically small. |
| Catalog breadth | Curated open-source LLM catalog: Llama 3.1 / 3.2, Qwen 2.5, Mistral, Mistral Nemo, Mixtral, Gemma 2, Phi-3, Command R. | Wider open-source LLM catalog including DeepSeek, WizardLM, Vicuna, Yi, and code-specific models. Multimodal models are limited. |
| API surface | OpenAI-compatible /v1/chat/completions. Drop-in for openai-python, Vercel AI SDK, LangChain — swap the base URL and key. | OpenAI-compatible /v1/chat/completions. Same drop-in story. Together also has their own SDK for fine-tuning / batch. |
| Privacy + tenancy | Dedicated GPU. Your prompts and completions run on hardware exclusively yours for the runtime billed. No co-tenants. | Shared multi-tenant inference fleet by default. Together offers Dedicated Endpoints as an enterprise upgrade. |
| Programmatic lifecycle (MCP) | Full instance lifecycle exposed over MCP — auxen_provision_model, auxen_pause_instance, auxen_set_schedule, auxen_destroy_instance, etc. An agent can self-operate the model without a human. OAuth 2.1 + PKCE. Listed on registry.modelcontextprotocol.io. | Standard LLM endpoints. No first-party MCP integration. Tool calling whatever the model supports natively. |
| Fine-tuning | On the roadmap; not active today. Persona Studio (system prompt + RAG) is the customization layer currently available. | Mature fine-tuning pipeline. Upload data, train, deploy as a hosted fine-tune billed at the base-model rate plus a small fine-tune surcharge. |
| Best for | Continuous LLM inference for SaaS / agent workloads, regulated data, teams that want a managed endpoint with predictable monthly cost. | Sporadic API traffic, fast model evaluation across a wide catalog, fine-tuning workflows, teams that prefer pay-as-you-go per token without managing instances. |
Together AI description: Serverless per-token inference platform (together.ai). Large open-source LLM catalog — Llama, Qwen, Mistral, Mixtral, DeepSeek, and more — served from a shared multi-tenant fleet with OpenAI-compatible endpoints. Pricing scales with token volume.
Auxen's distinctive axis: programmatic lifecycle control
Pricing shape, model catalog, and latency are real dimensions to compare — but they aren't where Auxen's unique fit lives. The axis the comparison turns on is programmatic lifecycle control: an agent operates the whole instance lifecycle over MCP. auxen_provision_model spins up a private, single-tenant instance. auxen_pause_instance and auxen_set_schedule manage runtime. auxen_destroy_instance stops the meter when the task is done. Per-token serverless APIs cannot structurally offer this — there is no instance for the customer to operate. If your workload is agent-driven and benefits from a private, programmable model for the duration of a task, Auxen wins on autonomy + privacy regardless of whether it wins on raw $/token (often it doesn't, and our pages say so).
Different shapes of the same problem
Together AI and Auxen both let you call open-source LLMs through OpenAI's wire format — that's the surface similarity. The pricing model is where they diverge. Together's per-token math is great when your traffic is bursty: a few thousand calls a day, idle in between, no warm pool to keep alive. You pay only for the tokens you actually generate. Auxen's per-minute math is great when your traffic is steady: a SaaS app or agent loop that keeps a model busy throughout the day. Once you're past roughly 20–30M tokens/day on a 70B model, Auxen's flat hourly rate beats Together's per-token rate, and the gap widens fast. Below that, Together wins on raw math.
Together's catalog is broader; Auxen's is curated
Together hosts a much wider set of open-source LLMs — DeepSeek, WizardLM, Vicuna, Yi, multiple code-specialized variants — alongside the popular Llama / Qwen / Mistral families. If you're still evaluating which model fits your task, that breadth matters. Auxen's catalog is intentionally narrower — the production-ready open-source models that real customers actually deploy. Both will get you to a working Llama 3.1 endpoint; Together will get you to a niche model Auxen doesn't carry. If catalog depth is your blocker, Together's the right tool.
Both serve OpenAI's wire format — migration is small
If you're already on Together AI and the per-token bill is climbing, switching to Auxen is base URL + API key in the same OpenAI-compatible client. The same is true in reverse. Neither platform locks you into a proprietary API surface. The decision to migrate should be driven by where the unit economics fall for your actual usage shape, not by integration cost.
Together's Dedicated Endpoints exist — that's the closest competitor
Together does offer a Dedicated Endpoints product for customers who outgrow per-token billing. It's a sales-conversation upgrade — annual commitments, dedicated capacity. Auxen offers similar dedicated-tenancy economics at a $0.15–$2.85/hr self-serve rate without the contract. The bet is that most teams that need dedicated capacity don't need the sales motion that comes with it.
Which one is right for you?
- ✓You're past 20–30M tokens/day on a single model and per-token billing has crossed the dedicated-instance crossover
- ✓You want predictable monthly cost decoupled from token volume
- ✓You serve regulated data and need dedicated tenancy without an enterprise contract
- ✓You need first-class MCP support for agent workloads
- ✓You want one stable HTTPS endpoint per tenant for routing or auditing
- ✓Your traffic pattern is steady, not bursty
- ·Your traffic is bursty or exploratory — a few thousand tokens/day spread unevenly
- ·You're evaluating many models and need broad catalog coverage
- ·You want first-party fine-tuning hosted by the same provider
- ·You're already using Together's SDK or Together-specific features (Code Sandbox, etc.)
- ·Your token volume is low enough that the per-token math beats any dedicated hourly rate
FAQ
Is Together AI cheaper than Auxen?
At low or sporadic token volumes — yes. Together's per-token pricing is hard to beat when you're not running the model continuously. As volume climbs into the tens of millions of tokens per day on a single model, the math crosses over. A rough rule: above ~25M tokens/day on a 70B model, Auxen's flat $1.20/hr × 730 hr/mo lands cheaper, and the gap widens with more usage.
Can I migrate from Together AI to Auxen?
Both expose OpenAI-compatible /v1/chat/completions. Migration is base URL + API key in your existing client (openai-python, Vercel AI SDK, LangChain). Streaming, tool calling, and message format work identically. Most teams complete the swap in under an hour.
Does Auxen have a model that Together doesn't?
Probably not — Together's catalog is wider on the open-source side. Auxen picks production-ready models the team can support well. If you need a niche model like DeepSeek-V2, WizardCoder, or one of Yi's variants, Together is the better catalog match.
Does Auxen support fine-tuning like Together does?
Not yet. Full LoRA / fine-tuning is on Auxen's roadmap but not active. Persona Studio (managed system-prompt + knowledge-base customization on top of any catalog model) is the customization layer today. If weight-level fine-tuning is a hard requirement, Together's training pipeline is the right tool.
Can I use Together for one model and Auxen for another?
Yes. Both speak OpenAI's wire format. Many teams use Together for sporadic models and Auxen for the one model their core product depends on — letting the steady workload run on dedicated capacity while the exploratory work stays per-token. LiteLLM or any LLM router can route between them.
Why pick dedicated when serverless has gotten so fast?
Three reasons: cost ceiling at high volume, latency consistency under load, and tenancy. Together's serverless fleet is genuinely fast, but at high QPS you can hit peak-hour variance or rate-limiting. A dedicated GPU avoids that entirely. The tenancy angle matters for regulated data — a dedicated GPU instance is a different conversation with compliance than a shared fleet.
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Competitor pricing and product positioning shift quickly. Facts on this page last verified 2026-05-30 against each provider's public docs. If a number looks stale, let us know and we'll fix it.