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, private open-weight model endpoint behind an OpenAI-compatible API — you don't write Python, you don't run 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?

Cost isn't the reason to choose Auxen. Modal bills per-second on raw GPUs (currently ~$2-3/hr for an H100, ~$1-2/hr for an A100). Auxen bills per minute of active time for a private deployment, by model tier — Fast $0.15/hr, Balanced $0.20/hr, Powerful $0.65/hr, Maximum $2.25/hr at Standard throughput. For sporadic batch jobs that run minutes per day, Modal's per-second billing can be cheaper. The reason to pick Auxen is single-tenant isolation: your deployment is private to you — no shared inference, no third-party routing — not a lower price.

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 runs everything underneath the endpoint; Modal exposes the compute.

Can I migrate from Modal to Auxen?

If your Modal usage is running an open-source LLM as an inference endpoint, migration is straightforward — deploy the same model family on Auxen, 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 private, single-tenant open-weight model endpoint behind an OpenAI-compatible API, billed per minute while it's active — one model that's yours alone, with no requests routed to a third-party inference 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 of active time, by model tier (Standard throughput): Fast $0.15/hr · Balanced $0.20/hr · Powerful $0.65/hr · Maximum $2.25/hr.	Per-second on raw GPUs: roughly $2–3/hr H100, $1–2/hr A100, $0.40–1/hr T4 / L4.
Warm-up behavior	Your deployment stays ready the whole time it's active. No warm-up 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 programmatic lifecycle control — deploy, pause, schedule, delete — over the API and MCP (auxen_deploy_model, auxen_set_throughput, auxen_delete_deployment, etc.). Single-tenant deployment, OAuth 2.1 + PKCE, and you decide when it's active and when it's gone.	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 renting hardware.	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: 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 privacy: Auxen runs one open-weight model in a private deployment for you alone. There is no shared inference pool, and requests are never routed to a third-party API like OpenAI, Anthropic, or Google — your deployment answers on its own. When you delete the deployment, its data goes with it. For a compliance-constrained team that cannot send prompts or documents through someone else's inference endpoint, that isolation is the whole point. Programmatic lifecycle control over the API and MCP (deploy, pause, schedule, delete) reinforces the same property: you decide exactly when your private deployment exists and when it doesn't, so the isolation is something you drive rather than something you hope a shared platform preserves. Per-token serverless APIs cannot structurally offer this — there is no deployment that belongs solely to the customer.

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, and runs everything underneath it for you. 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 stand up an open-weight model 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 by model tier is excellent for continuous inference that runs hours per day. Auxen is not priced to beat Modal on raw compute cost — it's priced for a single-tenant private deployment and a managed endpoint, and that isolation is what you're paying for. Honestly, for sporadic batch jobs that only run minutes at a time, Modal's per-second billing can come out cheaper. Where the workloads overlap, the choice is isolation and feature set, not which line item is lower.

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: deploy the same model family on Auxen, 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-weight model from Auxen's catalog
✓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 a curated set of open-weight models, fully managed for you. Custom non-catalog model uploads are on the roadmap; if you need this today, reach out at [email protected].

Is Auxen cheaper than Modal?

Cost isn't the reason to choose Auxen — isolation is. Auxen gives you a single-tenant private deployment with no requests routed to a third-party API, which is what a compliance-constrained team needs. Modal can be cheaper for sporadic, per-second workloads that only run minutes at a time. Pick Auxen when the dedicated, private endpoint is the requirement, not because it undercuts Modal on compute rate.

Does Auxen support OpenAI-style streaming?

Yes. Every Auxen deployment 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 more traffic?

Modal scales by launching additional function invocations on separate GPUs — load-balanced by Modal's runtime. Auxen scales with throughput levels per deployment — Standard, Double, Quad, or Max — which handle more simultaneous requests at the same model quality, behind the same endpoint. 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.

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.