You're running an inference business. Every 100ms of latency costs you customers. You need single-tenant hardware and auto-scaling — not a shared pool.
On shared GPU pools, another tenant's batch job can spike your inference latency. You have no visibility into what's running on your hardware. Your SLA becomes someone else's problem.
When multiple models share the same GPU, memory bandwidth contention is unpredictable. Your p99 latency varies based on what other customers are doing — not your workload.
Auto-scaling on shared infrastructure means cold starts when a new container loads your model weights onto a GPU that just finished someone else's job. Cold start time is not your model — it's the infrastructure.
If you're running proprietary fine-tunes or customer-specific model variants, shared infrastructure means your model weights are on hardware where other tenants have run. That's not acceptable for most enterprise customers.
Aircloud's Private Inference product gives inference providers dedicated endpoints — one model, one GPU, one tenant. Your p99 latency reflects your workload, not your neighbors'.
When demand spikes, auto-scaling adds dedicated capacity — not shared pool capacity. Each new instance is isolated to your account. The isolation model scales with your traffic.
Private inference — what you get
The goal is a p99 that you can predict and commit to. Dedicated hardware is the only way to get there. Everything else is a guess.
From zero to serving your first token. No warmup pool required.
Scale up and down without rounding to the nearest hour.
Traffic spike triggers dedicated capacity provisioning — no manual intervention.
Dedicated GPU allocation means your latency numbers are yours.
Aircloud is infrastructure, not a platform you're locked into. Standard APIs, VPC peering, and K8s-native tooling so GPU compute fits your existing architecture.
Standard REST endpoints. OpenAI-compatible API surface for drop-in compatibility with existing toolchains and client libraries.
Keep inference traffic off the public internet. VPC peering connects Aircloud infrastructure directly to your private network — no egress, no exposure.
Serve inference from your own domain. api.yourbrand.com backed by Aircloud private inference. Transparent to your customers.
Deploy and manage inference endpoints from your existing Kubernetes cluster. Aircloud endpoints as native K8s resources.
Get notified on scale-up and scale-down events. Build custom autoscaling logic on top of Aircloud capacity signals.
Deploy directly from HuggingFace Hub, private S3, or GCS. Pull model weights at provisioning time — no manual upload step.
Building an inference product? Talk to our infrastructure team. We'll scope your latency requirements, isolation tier, and scaling model — before you sign anything.