Run vLLM Inside Your AWS
Without Operating Infrastructure

Run vLLM
Inside Your AWS
Without Operating Infrastructure

Set latency and cost targets. Rivvr runs and continuously optimizes your inference to meet them, inside your AWS account.

Up to 70% lower inference cost

Cold starts under 30s

SLOs hold at 10× traffic bursts

VPC

GPT-20B-Guard

TTFT p90 < 200ms

≤ $6.00 / hour

Qwen3.8-27B

TTFT p90 < 400ms · 40 tok/s

≤ $4.50 / hour

Inkling-LoRA

25 tok/s

≤ $28.00 / hour

Rivvr Orchestrator

SLO & cost controller

Inference optimizer

Workload auto-tuner

Performance monitor

Mixed GPU Fleet

A100

On-demand

vLLM · GPT-20B-Guard

L4

Spot

vLLM · GPT-20B-Guard

L40S

On-demand

vLLM · Qwen3.8-27B

H100×8

On-demand

vLLM · Inkling-LoRA

Live metrics

Built by engineers from:

Enterprise-ready.
No giving up control.
Zero Infrastructure work.

With SageMaker or K8s

Configure infrastructure, routing, scaling, select GPUs

Tune infrastructure and load test vLLM after each model, LoRA or harness update

Overprovision to protect SLOs during traffic spikes, pay for idle GPUs

Latency degrades as traffic shifts, inference breaks eventually

Investigate failures and restore service

With SageMaker or K8s

Configure infrastructure, routing, scaling, select GPUs

Tune infrastructure and load test vLLM after each model, LoRA or harness update

Overprovision to protect SLOs during traffic spikes, pay for idle GPUs

Latency degrades as traffic shifts, inference breaks eventually

Investigate failures and restore service

With Rivvr

Define your model, custom latency SLOs, and cost targets

Auto-tester brings model live in 30 minutes

Latency SLOs hold through 10x bursts, up to 70% lower costs

Rivvr monitors live metrics, infrastructure adapts to traffic

Inference runs and self-heals 24/7

With Rivvr

Define your model, custom latency SLOs, and cost targets

Auto-tester brings model live in 30 minutes

Latency SLOs hold through 10x bursts, up to 70% lower costs

Rivvr monitors live metrics, infrastructure adapts to traffic

Inference runs and self-heals 24/7

You set the targets.
Rivvr runs the inference.

Define requirements, not your infrastructure

Bring your model weights from Hugging Face, S3, or your local store. Set your TTFT, TPS p50/p90, and cost targets. Use the defaults, or customize context length and quantization.

Define requirements, not your infrastructure

Bring your model weights from Hugging Face, S3, or your local store. Set your latency, throughput, and cost targets. Use the defaults, or customize context length and quantization.

Deployment Policy

Qwen3.8-27B

Quantization

LoRa

TTFT p50

Output Token/s

TTFT p90

Cost guardrail

SLO compliance

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Rivvr automatically meets your SLOs

Rivvr continuously optimizes your entire serving stack: vLLM workloads, GPU kernels, KV cache, routing, and GPU placements, to meet latency SLOs, while minimizing costs. Orchestrator monitors production, isolates failures with circuit breakers, and auto-heals inference even under load.

Rivvr automatically meets your SLOs

SLO Autopilot

Qwen3.8-27B

TTFTWithin SLO

p50 141msp90 278ms

Requests / s40 rps

GPU Fleet$8.10 / h · guardrail $20.00

H100

80GBon-demand

72%

L40S

48GBspot

67%

RTX PRO 6000

96GBwarming

H100

80GBwarming

Orchestrator

12:14:00Policy active · TTFT p50 < 200ms · p90 < 400ms

Observe performance. Change your targets.

Track live latency, throughput, and costs across your models. Tune cost to latency trade-off to meet your application needs. Rivvr adjusts the serving stack without manual redeployment or downtime.

Observe performance. Change your targets.

Track live latency, throughput, and costs across your models. Tighten a latency target or change a cost limit. Rivvr adjusts the serving stack without manual redeployment or downtime.

Live Monitoring

Qwen3.8-27B

TTFT

p50 0msp90 0ms

Tokens / s

p50 0p90 0

Prefill / M tokens

$0.00

Decode / M tokens

$0.00

SLO Compliance0.0%

Built for production workloads

Voice AI

Support chat agents

Vertical AI

OCR & document intake

THROUGHPUT-BOUND

The Challenge

•

Batch processing with uneven demand keeps GPUs idle

•

Ambiguous unit economics due to a shared AWS bill

With Rivvr

•

vLLM scales in 30s; costs follow demand

•

Predictable economics with 360° cost observability

Voice AI

Support chat agents

Vertical AI

OCR & document intake

LATENCY-CRITICAL

The Challenge

•

Strict p95 latency requirements; pauses disrupt the customer experience

•

Overprovisioning to protect p95 latency, with 30–40% GPU utilization

With Rivvr

•

Auto-maintained SLOs, even through 10x bursts

•

70–80% GPU utilization with fast cold starts and self-adapting cluster topology

Voice AI

Support chat agents

Vertical AI

OCR & document intake

LATENCY-CRITICAL

The Challenge

•

Strict p95 latency requirements; pauses disrupt the customer experience

•

Overprovisioning to protect p95 latency, with 30–40% GPU utilization

With Rivvr

•

Auto-maintained SLOs, even through 10x bursts

•

70–80% GPU utilization with fast cold starts and self-adapting cluster topology

Runs in your cloud.
Stays in your cloud.

VPC deployment

Run inference inside your AWS account and VPC, with network controls you manage.

VPC deployment

Run inference inside your AWS account and VPC, with network controls you manage.

Data privacy

Your prompts, responses, logs, and model weights stay inside your AWS account.

Data privacy

Your prompts, responses, logs, and model weights stay inside your AWS account.

RBAC and access control

Control who can deploy models, change policies, and view cost and performance data.

RBAC and access control

Control who can deploy models, change policies, and view cost and performance data.

Flexible tenancy isolation

Choose dedicated infrastructure for strict isolation or shared GPU capacity for higher utilization.

Flexible tenancy isolation

Choose dedicated infrastructure for strict isolation or shared GPU capacity for higher utilization.

Everything to run inference in production

Secure model storage

Centralize model operations with secure model storage. Bring weights from S3, Hugging Face or local store.

Managed endpoints

Serve LLMs and LoRAs with OpenAI-compatible endpoints for real-time and batch inference.

Self-healing inference

Automatic failure detection, circuit breaker and self-healing to maintain service live 24/7.

Automatic load testing

New deployment, live within 30 minutes. Auto-tuner that finds the most efficient vLLM configurations across GPU types.

Secure model storage

Centralize model operations with secure model storage. Bring weights from S3, Hugging Face or local store.

Managed endpoints

Serve LLMs and LoRAs with OpenAI-compatible endpoints for real-time and batch inference.

Self-healing inference

Automatic failure detection, circuit breaker and self-healing to maintain service live 24/7.

Automatic load testing

New deployment, live within 30 minutes. Auto-tuner that finds the most efficient vLLM configurations across GPU types.

Frequently Asked Questions

How is this different from SageMaker?

SageMaker gives you infrastructure and autoscaling controls. Rivvr removes the need to operate them: set latency and cost targets, and Rivvr continuously runs the infrastructure for you.

How is this different from AIBrix or NVIDIA Dynamo?

AIBrix and Dynamo are infrastructure software your team deploys, configures and maintains. Rivvr is a managed platform inside your AWS account. Instead of configuring profiling, autoscaling and infrastructure parameters, you specify the outcome: SLO + cost target.

Isn't spot capacity risky for something with an SLO?

Spot capacity is interruptible. Rivvr spreads workloads across compatible GPU capacity pools and uses on-demand as fallback, allowing capacity to change without sacrificing the SLO.

What data leaves my AWS account?

Inside your AWS account, behind your VPC. Inference traffic, data, and model weights never leave your environment.

How is Rivvr priced?

You pay AWS directly for infrastructure. Rivvr charges a separate management fee based on GPUs under orchestration.

Do I need to change my application?

Usually not. Rivvr exposes an OpenAI-compatible API, so most teams only change the endpoint.

Does Rivvr modify my model?

No. Model weights remain unchanged. Rivvr optimizes orchestration, placement and infrastructure.

What models are supported?

Any LLM supported by vLLM, currently up to 400B parameters.

What GPUs are supported?

NVIDIA CUDA compute capability 7.0+, including L4, L40S, A10G, T4, A100, H100, H200, B200, B300 and V100.

Run vLLM Inside Your AWSWithout Operating Infrastructure