You run your own infrastructure. You don't need a regulator to tell you your data should stay put. ARK shards across any mixed CPU + GPU fleet and turns it into a production-grade inference cluster. No MLOps overhaul, no usage meter, no vendor phoning home.
You're not bound by banking or public-sector rules. You just want a runtime you can own, forecast, and depend on — without inheriting someone else's roadmap.
Public LLM APIs are priced to extract margin from the workloads you want to grow most — agentic apps, long-context flows, batch jobs. The bill scales with exactly the usage that matters.
Not every company is a bank. But plenty of teams still don't love sending prompts, traces, and outputs through someone else's pipeline. You want control over the data path — not a DPA that says “trust us.”
The “easy” API comes with vendor lock-in that only shows up later: proprietary formats, model deprecation on their clock, pricing that moves without notice. Owning the runtime is how you stay portable.
ARK drops onto whatever you already operate — a rack in your own facility, a reserved slice from a colo partner, a small leased fleet — and turns it into a multi-model inference cluster your product team actually wants to use.
Any GPU generation, any vendor, plus CPU-only nodes where it makes sense. No NVLink, no InfiniBand, no rip-and-replace. Your existing fleet is the fleet.
Your team keeps its existing SDKs, prompts, and frameworks. Point the base URL at ARK and you're serving — existing agents just work.
No new MLOps platform to learn. ARK brings its own scheduling, model storage, and API gateway — and hooks into the identity, logging, and telemetry you already run.
Nothing phones home. Your usage data, your traces, your evaluations — all of it stays where you put it. ARK doesn't need a callback to work.
Running your own inference used to mean building a platform company alongside your actual product. ARK flips that. The numbers tell you how.
ARK Tailored is licensed by the GPUs it runs on. Nothing meters your usage, nothing phones home. Pay for the scale you deploy, and pick the support tier that fits how much of the operating you want to hand to us.
Deployment works the same way — it's optional. We can deploy and hand over, or you can deploy and we back you up. You run the runtime either way.
Predictable, fleet-sized, no consumption surcharge. Your CFO can forecast it. Your margins don't depend on how your product uses the model.
Standard (<10 GPUs), Premium (10–50), Enterprise (50+). Determined by license count, not upsold. No feature gating between tiers.
We deploy and hand over, or you deploy and we support. Either works. You pick based on how much of the runbook you want to own on day one.
Vision, speech, and extended model catalogues at fixed per-GPU pricing. Turn them on per deployment — not per user, not per token.
Even when you're running the operation yourself, the path from “we want this” to “it's serving traffic” is short, bounded, and supported.
We map your fleet — any mix of CPU and GPU, owned or leased — against ARK's reference deployments. Output: a deployment plan that fits your hardware and your ops model.
ARK is deployed on your boxes. Your team — or ours — stands it up, runs the smoke tests, and wires it into your identity, logging, and observability.
Your apps point at the OpenAI-compatible endpoint. Existing agents, frameworks, and SDKs work unchanged. Models get loaded; quotas and rate limits get tuned.
Your chosen support tier handles incidents and upgrades. New models, modality add-ons, and capacity expansions layer in without downtime.
Tell us the shape of your fleet — or what you're about to lease — and the workloads you want to serve. We'll show you how ARK fits, what the license looks like, and how fast you can be in production.