What's the difference between Auxen and Modal?

Modal is a serverless GPU compute platform — you write Python functions, decorate them with @modal.function, and Modal runs them on GPUs at a per-second billing rate. You bring the model, the inference server, and the deployment logic. Auxen gives you a managed open-source model endpoint (Llama, Qwen, Mistral, etc.) behind an OpenAI-compatible API — you don't write Python, you don't deploy a server, you just call the API. Modal is right when you're building a custom inference pipeline; Auxen is right when you want a private model endpoint without the engineering overhead.

Is Modal or Auxen cheaper?

Depends on workload shape. Modal bills per-second on raw GPUs (currently ~$2-3/hr for an H100, ~$1-2/hr for an A100). Auxen bills per-minute on dedicated GPU instances tied to a specific model size — $0.10/hr small (3-7B), $0.20/hr medium (8-14B), $0.65/hr large (24-32B), $1.50/hr XL (70B+). For high-volume continuous inference on a stable model, Auxen is significantly cheaper because the dedicated-tier pricing is below Modal's per-second rate. For sporadic batch jobs that run minutes per day on rare GPU types, Modal can win on per-second billing.

When should I pick Modal over Auxen?

Pick Modal when: you need raw GPU access for custom training or inference code, you're running model architectures Auxen's catalog doesn't include, you're using non-LLM ML workloads (image, audio, scientific compute), or you have an existing Modal codebase. Modal's Python-native API is excellent for ML researchers and data scientists who think in terms of functions rather than endpoints.

When should I pick Auxen over Modal?

Pick Auxen when: you want a private LLM endpoint without writing inference code, you need an OpenAI-compatible API (drop-in replacement for openai-python / Vercel AI SDK / LangChain), you need MCP integration for agent workloads, you want managed customization (system prompt + RAG) without standing up a training pipeline, or you're a non-ML-engineer team that just wants an API key to call. Auxen abstracts the GPU; Modal exposes it.

Can I migrate from Modal to Auxen?

If your Modal usage is running an open-source LLM as an inference endpoint, migration is straightforward — provision an Auxen instance with the same model, change your client base URL + API key, and the OpenAI-compatible endpoint accepts the same chat completion calls. Custom training pipelines or non-LLM workloads don't have a direct Auxen equivalent.

Auxen vs Modal

Auxen vs Modal: managed endpoint or serverless GPU?

Modal gives you raw GPU plus a Python function decorator and per-second billing. Auxen gives you a managed open-source model endpoint behind an OpenAI-compatible API. Different layers of the stack — pick the one that matches what you actually want to build.

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At a glance

Dimension	Auxen	Modal
Shape of the product	Managed open-source LLM endpoint. You pick a model from the catalog and get a stable HTTPS API.	Serverless GPU compute. You write Python, decorate it with @modal.function, Modal runs it on a GPU.
API surface	OpenAI-compatible /v1/chat/completions. Drop-in for openai-python, Vercel AI SDK, LangChain.	Modal's own Python SDK. HTTP endpoints are something you build on top.
Pricing	Per-minute, per model size tier: $0.15/hr (3–7B), $0.20/hr (8–14B), $0.65/hr (24–32B), $2.85/hr (70B+).	Per-second on raw GPUs: roughly $2–3/hr H100, $1–2/hr A100, $0.40–1/hr T4 / L4.
Cold-start behavior	Instance stays warm for the duration you provision it. No cold-start per request.	Serverless cold starts (seconds to minutes) when a function scales to zero. Modal warms via keep-alive settings.
Audience	Developers and SMB teams who want a private LLM endpoint without writing inference code.	ML researchers and data scientists who think in functions and need raw GPU primitives.
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.	You build it. Modal exposes compute; agent integrations are application code.
Model customization	Persona Studio: managed system-prompt + knowledge-base customization on any catalog model. Full LoRA / fine-tuning is on the roadmap; currently inactive.	Run your own fine-tune code on Modal GPUs. Bring your own framework.
Best for	Continuous private inference, agent workloads, regulated-data customers, teams that want a managed endpoint instead of GPU rental.	Custom training pipelines, non-LLM ML workloads (image/audio), per-second sporadic compute.

Modal description: Python-first serverless GPU compute (modal.com). Per-second billing on raw H100 / A100 GPUs.

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).

They're solving different problems

Modal sits at the IaaS+ layer: it gives you a programmable GPU you can call from Python, with the cold-start and packaging concerns abstracted away. Auxen sits one layer above: it gives you a fully managed model endpoint, with the inference server and the GPU abstracted away. If your work involves writing inference code, Modal is the better tool. If your work involves calling an LLM endpoint, Auxen is the better tool. There's overlap when someone uses Modal to deploy a Llama 3.1 endpoint — that's the exact case Auxen handles in one click.

The pricing question is workload-shape, not provider

Modal's per-second billing is excellent for sporadic batch jobs that run minutes per day. Auxen's per-minute billing tied to model size is excellent for continuous inference that runs hours per day. A small (3–7B) Auxen instance at $0.15/hour is materially cheaper than a Modal A100 at $1+/hour if your model fits and you'd otherwise leave the function warm. For a 70B model running 4 hours a day, both providers land in similar territory; the choice is feature set, not bill.

Migrate when the inference layer is the bottleneck

Teams that picked Modal early because there was nothing better often migrate to Auxen when their inference workload stabilizes — same model, predictable load, no need to keep maintaining a custom inference server. The migration is straightforward when the workload is OpenAI-shaped: provision an Auxen instance with the same model, swap the client base URL and key, the chat completion endpoint takes the same shape. Custom training jobs stay on Modal.

Which one is right for you?

Pick Auxen if

✓You want a private LLM endpoint without writing inference code
✓You need an OpenAI-compatible API (drop-in for openai-python / Vercel AI SDK / LangChain)
✓You're running an open-source model (Llama, Qwen, Mistral, Gemma, Phi, Command R)
✓You need MCP integration for agent workloads
✓You want managed customization (system prompt + RAG) without standing up your own training pipeline
✓You're a non-ML-engineer team that just wants an API key to call

Pick Modal if

·You need raw GPU access for custom training or non-LLM ML workloads
·You're running model architectures Auxen's catalog doesn't include
·You're comfortable writing Python inference code
·You have an existing Modal codebase you don't want to migrate
·Your workload is sporadic batch jobs better suited to per-second billing

FAQ

Can I run a model on Auxen that Modal doesn't support?

Probably not — Modal lets you run any model you can fit in their GPU memory. Auxen's catalog is curated open-source models with managed Ollama serving. Custom non-catalog model uploads are on the roadmap; if you need this today, reach out at [email protected].

Is Auxen cheaper than Modal?

On continuous LLM inference workloads, yes — significantly. A medium-tier Auxen instance ($0.20/hour for 8–14B models) is below Modal's A100 rate ($1–2/hour) even before counting the operational simplicity. For sporadic batch jobs that run minutes per day, Modal's per-second billing can win.

Does Auxen support OpenAI-style streaming?

Yes. Every Auxen instance exposes streaming chat completions at /v1/chat/completions with the OpenAI NDJSON / SSE wire format. Vercel AI SDK, LangChain, OpenAI SDK all stream tokens against an Auxen endpoint without modification.

How do Modal and Auxen handle scaling to multiple GPUs?

Modal scales by spinning up additional function invocations on separate GPUs — load-balanced by Modal's runtime. Auxen scales via the capacity multiplier (1x, 2x, 4x, 8x) per instance — additional dedicated GPUs assigned at provision time with the proxy round-robining requests. Auxen's model is closer to traditional load-balanced inference; Modal's is closer to per-call function fan-out.

Can I use Auxen for fine-tuning instead of inference?

Full LoRA / fine-tuning is on the roadmap but not currently active. Today, Auxen's Persona Studio offers managed customization through system-prompt engineering plus knowledge-base retrieval on top of any catalog model — sufficient for most domain-adaptation needs. For weight-level fine-tuning, Modal is the better fit for now.

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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.