Adaptive Inference

Adaptive Inference

Adaptive Inference

What is Adaptive Inference?

What is Adaptive Inference?

What is Adaptive Inference?

Adaptive Inference is a flexible and scalable service designed to adjust seamlessly to your workload demands. It ensures high-performance processing and consistent turnaround times, regardless of the task at hand. With options for real-time, asynchronous, and batch, Adaptive Inference caters to a broad range of project needs, helping you avoid bottlenecks and deliver value without compromising quality.

How Adaptive Inference works

How Adaptive Inference works

Adaptive Inference offers three distinct processing options to match the specific needs of your projects:

Real-time inference

Optimized for live, instant response scenarios, this option delivers sub-second latency, making it ideal for applications like real-time analytics or live monitoring that need immediate results.

Optimized for live, instant response scenarios, this option delivers sub-second latency, making it ideal for applications like real-time analytics or live monitoring that need immediate results.

Asynchronous inference

Tailored for tasks that don’t need immediate results, this cost-effective option is ideal for applications with fluctuating workloads and unpredictable timelines.

Tailored for tasks that don’t need immediate results, this cost-effective option is ideal for applications with fluctuating workloads and unpredictable timelines.

Batch inference

Best suited for high-volume tasks that require predictable turnaround times, batch processing handles large datasets efficiently and at scale.

Best suited for high-volume tasks that require predictable turnaround times, batch processing handles large datasets efficiently and at scale.

Adaptive Inference vs. standard inference

Adaptive Inference vs. standard inference

Traditional inference services are often rigid and don’t adapt well to changing demands. Most of them use fixed rate-limits, which can slow down workflows when workloads exceed predefined thresholds. This lack of flexibility can cause delays and inefficiencies.

In contrast, Adaptive Inference offers:

Dynamic rate-limits

We automatically adjust resources based on your workload, so rate-limits can scale up or down in real-time, ensuring consistent performance and turnaround times.

We automatically adjust resources based on your workload, so rate-limits can scale up or down in real-time, ensuring consistent performance and turnaround times.

Flexible, efficient processing

Our service is optimized for large-scale AI tasks, giving you the option to stretch timelines when possible to save on costs, without sacrificing performance or quality.

Our service is optimized for large-scale AI tasks, giving you the option to stretch timelines when possible to save on costs, without sacrificing performance or quality.

Will I need to manage infrastructure or hardware?

Will I need to manage infrastructure or hardware?

Will I need to manage infrastructure or hardware?

No. Our platform automatically handles scaling and optimization, so you can stay focused on your projects without managing infrastructure.

How significant are the cost savings?

How significant are the cost savings?

How significant are the cost savings?

With Adaptive Inference on kluster.ai, you can significantly cut down on your inference costs. For example, using Llama models can save you up to 50%, and for the DeepSeek-R1 model, you could see savings as high as 95%.

What happens if the completion window is not met?

What happens if the completion window is not met?

What happens if the completion window is not met?

For batch and asynchronous inference, we guarantee processing up to a total of 1 million tokens per user per hour, with a maximum of 4,000 output tokens per request. If these limits are exceeded, requests may extend into the next completion window, and additional charges for the subsequent window will apply. This does not apply to real-time inference, which is designed for immediate responses.

How do we keep costs low?

How do we keep costs low?

kluster.ai leverages a unique and innovative supplier model to dramatically reduce costs while maintaining high performance. Here’s how it works:

We mesh GPUs 

Instead of owning expensive hardware, kluster.ai connects developers to a global network of suppliers. This distributed model enables you to scale your workloads affordably without the overhead costs associated with proprietary infrastructure.

Instead of owning expensive hardware, kluster.ai connects developers to a global network of suppliers. This distributed model enables you to scale your workloads affordably without the overhead costs associated with proprietary infrastructure.

We optimize with Adaptive Inference

By leveraging the power of our Adaptive Inference service, we dynamically scale resources to meet demand, which allows us to offer more affordable processing without compromising on quality or performance.

By leveraging the power of our Adaptive Inference service, we dynamically scale resources to meet demand, which allows us to offer more affordable processing without compromising on quality or performance.

We pass cost savings onto you

We partner with GPU providers worldwide who operate data centers with underutilized compute capacity. By tapping into this excess GPU power, we offer compute at a significantly lower price point compared to traditional infrastructure providers.

We partner with GPU providers worldwide who operate data centers with underutilized compute capacity. By tapping into this excess GPU power, we offer compute at a significantly lower price point compared to traditional infrastructure providers.