Serverless Inference | Instantly Deploy ML Models at Scale

Q: What are common use cases for serverless inference?

Common use cases include real-time recommendation systems, fraud detection, customer support chatbots, predictive maintenance, computer vision inference, and natural language processing (NLP) tasks.

Q: What is the difference between serverless inference and traditional model deployment?

Traditional model deployment requires dedicated infrastructure, manual scaling, and DevOps management. Serverless inference abstracts these complexities, offering automatic provisioning, event-driven execution, and reduced operational overhead.

Q: How does Vultr Serverless Inference optimize latency for real-time GenAI applications?

Vultr optimizes latency through GPU-accelerated inference nodes and persistent container options that reduce cold start times, delivering fast responses for real-time applications like chatbots and fraud detection.

Q: Can Vultr Serverless Inference run multi-modal models such as LLMs with vision capabilities?

Yes. Vultr Serverless Inference supports multi-modal models using inference-optimized GPUs, enabling advanced use cases like image captioning, video analysis, and vision-augmented LLMs within a serverless framework.

Q: What observability tools are available to manage serverless inference workloads on Vultr?

Vultr provides observability tools with metrics like latency, throughput, cold starts, and resource usage. These integrate with external platforms via APIs or logging agents for comprehensive monitoring and alerting.

Train anywhere, infer everywhere

Coming soon: Bring your own model

Whether your models are developed on Vultr Cloud GPU, in your own data center, or on a different cloud, Vultr Serverless Inference enables a hassle-free global inference process.

Self optimizing

Vultr Serverless Inference not only automates the scaling of resources to match demand but also optimizes the performance of your Generative AI applications in real time.

Inference at the edge

Vultr Serverless Inference is designed to effortlessly scale your Generative AI applications across six continents, meeting demands at any volume without manual intervention.

Private clusters

Deploy Serverless Inference on top of private GPU clusters, allowing businesses to benefit from self optimization and scalability while complying with data residency regulations.

AI deployment for the modern enterprise

Turnkey RAG

Using the Vultr API, upload your documents or data to your private, secure vector database, where they are stored as embeddings. When you begin model inference, Vultr’s included pre-trained models will use these embeddings as source material, providing custom outputs without requiring model training, or risking proprietary data leaking to public AI models.

Inference-optimized GPUs

Vultr Serverless Inference operates on the latest models of inference-optimized GPUs, providing exceptional speed and performance efficiently and affordably.

OpenAI-compatible API

With Vultr Serverless Inference’s OpenAI-compatible API, it’s easy to integrate AI models into a variety of common workloads at a more affordable rate, without adding developmental complexities or sacrificing performance.

Affordable, transparent pricing

Deploy AI models affordably, starting at

$10/month

for 50,000,000 tokens!

Usage beyond that amount is billed at an affordable $0.0002 per thousand tokens.

Media inference may incur additional charges based on usage.

Vultr Serverless Inference

Deploy AI securely without the complications of infrastructure management.

Connect

Connect to the Vultr Serverless Inference API.

Upload

Upload your data and documents to the Vultr Serverless Inference vector database, where they will be securely stored as embeddings for use in inference. The data is inaccessible to anyone else and can’t be used for model training.

Deploy

Deploy on inference-optimized NVIDIA or AMD GPUs.

Attach

Attach to your applications using Vultr Serverless Inference’s OpenAI-compatible API for secure and affordable AI inference!

Resources

Browse our Resource Library to help drive your business forward faster.

Doc

Vultr Cloud Inference

Doc

How to Use Vultr Cloud Inference in Node.js

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How to Use Vultr Cloud Inference in Python

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How to Use Turnkey RAG using the Vultr API

Step by step guide to uploading your documents or data to your Vultr Serverless Inference private, secure vector database, where they are stored as embeddings for use in model inference outputs without requiring model training or risking proprietary data leakage.

Additional resources

FAQ

Is serverless inference suitable for real-time applications?

Yes. Serverless inference is designed for low-latency environments and can support real-time use cases such as fraud detection, recommendation engines, and chatbot responses—especially when paired with caching or warm-start optimization.

What are common use cases for serverless inference??

Real-time recommendation systems
Fraud detection
Customer support chatbots
Predictive maintenance
Computer vision inference
Natural language processing (NLP) tasks

What is the difference between serverless inference and traditional model deployment?

Traditional deployment requires dedicated infrastructure, manual scaling, and ongoing DevOps support. Serverless inference abstracts all of this, offering automatic provisioning, event-driven execution, and lower operational overhead.

How does Vultr Serverless Inference optimize latency for real-time GenAI applications?

Vultr minimizes latency through GPU-accelerated inference nodes and persistent container options that reduce cold start impact, ensuring fast responses for real-time applications like chatbots and fraud detection.

Can Vultr Serverless Inference run multi-modal models such as LLMs with vision capabilities?

Yes. Vultr Serverless Inference supports multi-modal model deployment using inference-optimized GPUs, enabling advanced use cases like image captioning, video analysis, and vision-augmented LLMs within a serverless framework.

What observability tools are available to manage serverless inference workloads on Vultr?

Vultr provides performance metrics including latency, throughput, cold starts, and resource usage. These integrate with external observability platforms via APIs or logging agents for end-to-end model performance monitoring and alerting.

How does Vultr handle model versioning and deployment rollbacks in serverless inference?

Vultr supports containerized model deployment with tagged versions, enabling atomic updates, A/B testing, and rollbacks. Users can manage different model iterations seamlessly through the API or dashboard without downtime.

Is serverless inference suitable for real-time applications?

Yes. Serverless inference is designed for low-latency environments and can support real-time use cases such as fraud detection, recommendation engines, and chatbot responses—especially when paired with caching or warm-start optimization.