Serverless inference is an execution model that lets teams run AI and LLM workloads on demand without provisioning or managing servers, GPUs, or underlying infrastructure. In a serverless inference model, compute resources automatically scale up and down based on usage—ensuring low-latency responses while optimizing cost efficiency.
As organizations adopt GenAI applications, LLM-powered features, and real-time inference, serverless inference becomes essential. It eliminates infrastructure friction, accelerates model development, and delivers significant cost savings.
This guide explains how serverless inference works, its benefits, key challenges, best practices, and how Rafay enables a turnkey, enterprise-grade serverless inference platform for LLM providers, GPU cloud partners, and platform engineering teams.
Serverless inference is a cloud execution model where AI and machine learning models run on demand, automatically scaling compute resources to handle inference requests through a simple API without requiring teams to manage servers or GPU infrastructure.
Instead of hosting and maintaining dedicated GPU instances or serverless endpoints, teams access inference through a serverless platform API layer. Compute spins up as needed—then shuts down when idle.
Serverless inference provides:
This makes serverless inference ideal for teams delivering real-time predictions, LLM features, or production-scale AI applications.
Inference is triggered via simple API calls (e.g., OpenAI-compatible endpoints). Compute resources activate only when needed.
The serverless platform dynamically adjusts GPU allocation based on:
This ensures high availability without overprovisioning or wasted memory.
Developers access ML models through a standard API interface, enabling:
Rafay supports full OpenAI-compatible APIs for simplified integration and configuration.
Teams no longer manage server provisioning, scaling, monitoring, or GPU lifecycles.
Automatically handles demand during:
Token-based or time-based billing eliminates waste and ensures significant cost savings.
Teams focus on building AI-powered applications—not infrastructure management.
Quantization, pruning, distillation, and model selection (e.g., Qwen vs Llama) reduce memory size and improve inference speed.
Use:
Monitor concurrency, rate limits, and request patterns to adjust autoscaling thresholds and handle more concurrent requests efficiently.
Track:
Platforms like Rafay provide built-in observability, cost visibility, and detailed documentation.
Agentic AI involves:
Agents are best for autonomous or complex task execution.
Serverless inference is ideal for:
Agents handle orchestration.
Serverless inference handles model execution.
Together, they power production AI systems at scale.
Rafay's serverless inference offering supports all of these through standardized APIs.
To see how this solution was built and why Rafay invested in turnkey inference capabilities, read the full introduction: Introducing Serverless Inference: Team Rafay’s Latest Innovation.
Rafay provides a fully managed serverless inference platform designed for:
Deploy LLMs like:
…with zero code changes using OpenAI-compatible simple API calls.
Rafay automates:
Optimizing cost and performance without manual intervention.
Supports token- or time-based billing with complete consumption tracking for:
Rafay provides:
Serverless inference is included for all Rafay platform customers and partners.
An on-demand execution model where machine learning models run without managing servers or GPUs.
Compute resources autoscale dynamically based on inference requests and concurrent invocations.
Server-based requires long-running infrastructure and server provisioning. Serverless eliminates it with automatic scaling and pay-per-use pricing.
Yes—especially for bursty, unpredictable, or large-scale workloads requiring low latency.
With autoscaling GPUs, turnkey LLM deployment, token billing, audit logs, and secure APIs.
Serverless inference is quickly becoming the standard for delivering scalable AI and LLM applications. It offers:
Rafay provides a complete, turnkey serverless inference solution—enabling GPU providers, cloud platforms, and enterprises to deliver cutting-edge AI models with minimal operational burden.
Explore Rafay’s Serverless Inference Capabilities
Serverless inference lets teams run AI models without provisioning servers. Explore how it works, key benefits, and how Rafay accelerates scalable AI delivery.
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