Auxen vs Together AI: per-minute or per-token?
Together AI charges per token across a shared multi-tenant fleet — fast to start, scales with usage. Auxen runs a private, single-tenant deployment per customer at a flat per-minute rate: one model that's yours alone, prompts that never transit a shared per-token service, and data erased when you delete the deployment. If per-token economics on a shared fleet suit you, Together. If single-tenant isolation and predictable cost matter more, Auxen.
At a glance
| Dimension | Auxen | Together AI |
|---|---|---|
| Shape of the product | A private, single-tenant deployment 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 active time, $0.15–$2.25/hr by model tier (Fast to Maximum). 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 | A lightly-used deployment still bills the full hourly rate while it's active. 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 Maximum-tier model costs the same as 100K tokens — $2.25/hr × 730 hr ≈ $1,643/mo left on around the clock. Predictable, not cheaper. | 30M tokens/day on Llama 3.1 70B Turbo at $0.88/M ≈ $26/day, $792/mo — and it stays purely usage-based. Together's per-token rate is hard to beat on price; Auxen's draw at this scale is predictability and single-tenant isolation, not a cheaper bill. |
| Latency consistency | Single-tenant deployment — no co-tenant queueing. Latency is consistent because nobody else's traffic shares your model. | 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-weight model catalog in four tiers — Fast, Balanced, Powerful, Maximum. | 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 | Single-tenant deployment. Your prompts and completions are answered by a model that serves only you. No co-tenants. | Shared multi-tenant inference fleet by default. Together offers Dedicated Endpoints as an enterprise upgrade. |
| Deployment control (MCP) | You control the full lifecycle of your private deployment over MCP — auxen_deploy_model, auxen_set_throughput, auxen_delete_deployment, etc. Delete removes your single-tenant deployment and erases its data. OAuth 2.1 + PKCE. Listed on registry.modelcontextprotocol.io. | Standard LLM endpoints. No first-party MCP integration — and nothing private to control, since inference runs on the shared fleet. 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 a deployment. |
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: single-tenant privacy
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 single-tenant isolation. Auxen gives you one private model in a deployment that's yours alone while it's active: no shared inference, no co-tenants, and — critically — your prompts and completions never transit a multi-tenant per-token service or get routed through third-party infrastructure. When you delete the deployment, its data goes with it. Together, by design, is a shared per-token API: every call lands on a multi-tenant fleet alongside everyone else's traffic. That's a fine model for many workloads, but the data-isolation story is fundamentally different — with Auxen there is a single tenant on a private deployment; with a per-token serverless API there isn't. Deployment control over MCP (deploy, pause, schedule, delete) is part of this: it's how you operate that private deployment and, on delete, guarantee its data is gone — a control capability, not a claim about the model running itself. If keeping your data on infrastructure that's exclusively yours matters, Auxen wins on isolation.
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 genuinely hard to beat, especially when your traffic is bursty or sporadic: a few thousand calls a day, idle in between, no warm pool to keep alive. You pay only for the tokens you actually generate, and there's no floor. Auxen's per-minute math trades that flexibility for a fixed, predictable bill and — the real point — a private single-tenant deployment where your traffic never mixes with anyone else's. We won't pretend Auxen is the cheaper choice; at low or moderate volume it usually isn't. The reason to run a private deployment is data isolation and cost predictability, not saving money on tokens.
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 endpoint on a popular open-weight model; 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, reserved hardware. Auxen offers single-tenant privacy at a $0.15–$2.25/hr self-serve, per-minute rate without the contract. The bet is that most teams that need a private endpoint don't need the sales motion that comes with it.
Which one is right for you?
- ✓You want your prompts and completions on a private single-tenant deployment, never routed through a shared per-token service
- ✓You want predictable monthly cost decoupled from token volume
- ✓You serve sensitive data and need single-tenant privacy with data erased on delete, without an enterprise contract
- ✓You want programmatic control over your deployment lifecycle via MCP
- ✓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 per-minute rate
FAQ
Is Together AI cheaper than Auxen?
Often, yes — especially at low or sporadic volume. Together's per-token pricing is hard to beat when you're not running the model continuously, and it has no fixed floor. Auxen isn't pitched as the cheaper option; an active deployment bills its flat rate whether it's busy or idle. What Auxen offers instead is a predictable bill and single-tenant isolation: one model that's yours alone, prompts that never transit a shared per-token service, and data erased when you delete the deployment. Choose Auxen for data isolation and predictability, not to shave the token bill.
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 a private deployment while the exploratory work stays per-token. LiteLLM or any LLM router can route between them.
Why pick a private deployment when serverless has gotten so fast?
Three reasons, and none of them is price: single-tenant data isolation, cost predictability, and latency consistency under load. Together's serverless fleet is genuinely fast and often cheaper — but every call runs on shared multi-tenant infrastructure. A private Auxen deployment keeps your prompts and completions with a model that's exclusively yours, never routed through a per-token service, with data erased on delete. It also gives you a flat, predictable bill and avoids peak-hour variance or rate-limiting at high QPS. If your traffic is light and you don't need the isolation, serverless is the better fit.
See if Auxen fits your workload.
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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.