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Private AI Inference
for Open-Weight Models.
In Your Region,
Optimized Continuously.

Aurora Inference runs your open-weight models for less — on reserved capacity, optimized continuously, in your region.
Economics you can plan around, without hiring the team that usually comes attached.

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Cheaper Tokens
Still Haven't Made Anyone's Bill Smaller.

Agentic workloads consume tokens with no natural ceiling:
Every retry, tool call, and reasoning chain is a metered event. Per-token prices keep falling; consumption rises faster. The bill scales with your success, and nobody can forecast it.

What you pay for on metered pricing:

Every request. Every retry. Every token of context, re-read on every turn — value created or not. The serving stack gets more efficient every year but on a meter, those gains land in your vendor's margin, not your bill.

Icon suggests how Aurora Inference continuously optimizes your workloads

What Aurora charges you for:

Capacity scoped to your workload and optimized continuously on your behalf.
The efficiency gains land in your unit economics, and that’s the point of our architecture.

Same Models. Same APIs.
A Different Machine Underneath.

In a category where every provider serves the same open-weight catalogs behind the same OpenAI-compatible interface, efficiencies come from the machine underneath and who does the tuning work.
Icon of gear with up-arrows to suggest how Aurora Inference continuously optimizes your private AI workloads

A stack that keeps improving

Workloads drift; static configurations decay. Aurora’s optimization layer keeps learning your traffic, so performance per dollar trends up over time.

Cluster of icons to suggest Aurora Private AI Inference has privacy built-in by default.

Your boundary, by construction

Prompts, outputs, and weights stay in your region, on dedicated capacity. Privacy as a property of the deployment, not a clause in a contract.
We've made the case for private AI in full.

Icon of a gear with nodes, suggesting Aurora Private AI Inference is a done for you service

Serving stack operated for you

Batching, placement, parallelism, quantization, cache policy. These mean the difference between a default configuration and a tuned one is measured in multiples, and it’s our job, not a hire you make.

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Owned infrastructure

Aurora runs on its own data centers and energy position. The cost basis is ours to set,  which is what makes sustainably lower economics possible rather than promotional.

Your Capacity. Aurora’s Optimization.

Capacity Dedicated inference infrastructure, reserved and scoped to your demand profile — not a shared pool with rate-limit roulette.
Integration OpenAI-compatible endpoints — base URL and key swap. Test in staging against your own evals; no re-architecture, no trace handover.
Routing Complexity-based routing — requests land on the model and hardware that match the task, so simple calls stop consuming frontier-priced compute.
Caching KV-cache reuse — agentic workloads repeat context heavily; repeated context is served from cache instead of recomputed.
Models Open-weight models and your fine-tuned LoRA adapters, served on the same reservation.
Regions In-region by default across North America, Western Europe, the Nordics, GCC, and APAC. Confidential compute available where the workload demands it.

Capacity That Learns Your Workload.

The throughput is in there; extracting it is a job that never finishes. Optimal configurations drift as traffic shapes change, engines update, and last quarter's tuning becomes this quarter's waste. Most stacks run on the last config someone had time to benchmark. We take that job over, continuously.
Equalizer icon suggests how Aurora Inference calibrates private AI for engineers

What it does for the engineer

Continuously evaluates placement, parallelism strategy, batching, cache policy, and precision against your live traffic patterns. Aurora Inference reconfigures as the workload drifts, instead of serving yesterday’s tuning to today’s traffic.

Icon suggests how Aurora Inference continuously optimizes your workloads

What it means for everyone else

You’re not buying a machine that ages. Every month on Aurora Inference, your capacity does more useful work per dollar than the month before  without anyone on your team touching it.

Your Inference Bill Isn’t a Price Problem. It’s a Tuning Problem.

Inference is becoming the largest variable line in AI COGS, and the market's answer is a discount on the meter. That treats a control problem like a procurement problem, and we've laid out why cheaper tokens don't fix the economics with the sourced numbers. 

The real lever is how much value each dollar of capacity produces: routing, caching, utilization — and that lever is operated, not negotiated. Aurora operates it for you, on infrastructure whose costs we control, so your spend becomes something you can plan and your margins something you can commit to.
Metrics that move: cost per request, gross margin %, budget variance vs. plan.

Private AI Inference is an optimization problem

Built for the Teams Feeling It First.

Agentic AI in Production

When token bills are climbing faster than the value your agents produce, Aurora Inference  restores the unit economics.

Migrating Off Frontier APIs

Open-weight models now barely trail the frontier by months and they do it at a fraction of the cost. It's where the builders have already landed; the hard part is operating the models well. That's the layer Aurora manages.

Voice and Real-Time Workloads

Inference deployed near the traffic, so the latency budget goes to the model with per-minute economics you can plan.

Private AI and Data Residency

Regulated traffic runs in-region: processing stays in the jurisdiction, not just storage. If you're navigating data residency under GDPR and the EU AI Act, that's a requirement Aurora meets by construction.

Scope It. Test It. Run It.

No migration project, no trace handover. You judge everything with your own evals.

1

Scope

Share your demand profile: models, context patterns, throughput, region. Aurora sizes capacity against it.

2

Test

Point staging at OpenAI-compatible endpoints and judge with your own evals. Start with one workload, in shadow mode.

3

Run

Your capacity, in your region, at economics you can plan. The optimization layer works every token against it, continuously.

Aurora Infra Private AI Cloud Micro DC

The First Cohort Gets Allocation Priority.

Capacity is allocated deliberately, not infinitely.
Early-access allocations are scoped and assigned in the order teams join the list: one work email, one question about your current inference spend.
Get First Access
Talk to Aurora