Define your SLO and cost targets. Rivvr runs within your AWS account and continuously rebalances your GPU cluster, so you hit latency targets without GPU budget overspend.
Up to 85% lower inference costs
Deploys in your VPC
Flat cost per token under bursts
Without Rivvr
SLO incidents get investigated after the fact
GPU bills scale with your worst-case traffic
Engineers tune scaling rules instead of building product
Every model change risks breaking the infra config
With Rivvr
SLOs held automatically at p50/p90
Capacity fits actual traffic in real time
Cost per token stays flat, low or high load
Routing, placement, and topology adapt automatically
What model sizes does Rivvr support?
Up to 400B parameters. If you're running something larger or unusual, let's talk.
What inference engines are supported?
Rivvr uses a vLLM-compatible runtime. Models that run on vLLM can generally be orchestrated by Rivvr.
What GPUs are supported?
NVIDIA GPUs with CUDA compute capability 7.0 or higher — including L4, L40S, A10G, T4, A100, H100, H200, B200, B300, V100.
Does Rivvr modify or compress my models?
No. Rivvr doesn't touch model weights. Optimization happens at the orchestration, placement, and infrastructure layers.
Where does Rivvr run?
Inside your AWS account, behind your VPC. Inference traffic, data, and model weights never leave your environment.
How is this different from managed inference endpoints?
Rivvr gives you the managed service experience — no infra to run, no scaling rules to tune — but inside your own AWS account. You keep full control over security, SLOs, and cost, without the operational overhead.
How is Rivvr priced?
Rivvr is priced as a management layer based on GPUs under orchestration. You pay AWS directly for infrastructure; Rivvr charges a separate management fee.
If you're using AWS or running LLMs with a managed provider,
we'll walk through your current setup and show you
where your costs are going.