Cost Over Time
Cluster spend and selected model-attributed cost.
Cluster spendModel-attributed costInspect
Your models, running at scale inside your AWS. Never break, continuously optimized by R1,
the most token-efficient inference orchestration engine.
40%
higher token efifciency vs llm-d
70%
higher token efifciency vs SageMaker AI
Continius improvement
R1 contirniusly learns from traffic and optimizes inference down the stack.
Zero operations
Rivvr runs load tests, provisions GPUs, scales deployments, and heals failed replicas automatically.
Any vLLM-compatible model
Deploy your own weights and LoRA adapters with custom SLOs per model. Preserve full flexbility
Qwen3.5-9B
LoRA support-adaptor-v1-1-4
TTFT p50
≤ 1,100 ms
TTFT p90
≤ 1,800 ms
Output
≥ 50 tok/s
Cost
≤ $30/hr
01 · Routing
Routing, designed for LLM applications
Routes requests by cache locality and priority, probes replica health, and manages circuit breaking.
02 · KV Cache
Distributed, tiered KV cache
Coordinates cache across GPU memory, RAM, SSD, and persistent storage, preserving cached state as nodes enter and leave.
03 · Engine
vLLM auto-tuning
Profiles traffic patterns and continuously tunes vLLM to maximize throughput within your SLOs.
04 · GPU
Heterogeneous GPU bin packing
Places models across mixed GPU types and shares compute between them based on demand and workload type (batched or real-time).
05 · Scaling & recovery
Sub-30-second cold starts
and auto-recovery
Fast scaling and model swapping, self-healing dead pods within 1 minute.
06 · Cluster
Demand forecasting and SLO control
Forecasts traffic and adjusts serving capacity using latency, throughput, and cost feedback.
07 · AWS Cloud
Capacity discovery and
auto-reservation
Monitors regional availability across compatible VMs and automatically reserves AWS capacity.
How quickly can you roll out Rivvr?
We send you a rollout checklist. Once you complete it, we install Rivvr and run system checks within 1–3 business days.
What if we don’t have AWS GPU quota?
Your account needs an approved GPU quota before Rivvr can provision the corresponding instances.
AWS GPU instances are often unavailable. How does Rivvr handle that?
R1 checks availability across compatible GPU instance types and automatically reserves capacity. Your deployment can use different compatible GPUs instead of waiting for one specific instance type.
Do we need to change our application?
For an OpenAI-compatible client, update the endpoint URL, model name, and inference API key.
How is Rivvr priced?
A platform fee plus a charge based on GPU capacity under management.