Comparing Serverless, Dedicated, and Self-Hosted LLM Inference: Cost Efficiency Insights

Every startup founder often encounters the advice to initially rent an API and then transition to self-hosting a GPU to cut costs. This common notion was put to the test on a cloud platform using the same model, prompts, and three product surfaces: Serverless Inference, Dedicated Inference, and a self-hosted GPU. We explored the real costs involved.

Summary: Running your own GPU can indeed become more cost-effective than serverless solutions, but only if the GPU is consistently busy. In the early stages, when traffic is unpredictable and bursty, serverless options are generally two to four times cheaper and require no operational management. Thus, the true question isn’t simply about choosing between serverless and self-hosted options; it’s about how consistently busy your GPU will be. This guide will help locate the cost-effective point.

The analysis includes three practical deployment options for startups, each tested for its cost structure: Serverless Inference, Dedicated Inference, and a self-hosted GPU. Each path was evaluated based on real-world usage.

Key Insights

• Serverless Inference is more economical than a self-hosted GPU until the GPU reaches 22-48% utilization.
• Dedicated Inference incurs about 30% more cost than a self-hosted GPU, moving its break-even point to approximately 29% utilization.
• Start with Serverless Inference and transition to a GPU if it becomes consistently busy.
• Carefully measure your inference expenses instead of relying on guesses.
• Running a near-idle self-hosted GPU can be 2-4 times more costly than serverless alternatives, along with added operational burdens.

Methodology

This analysis utilized a specific model across two realistic workload scenarios: a chat format and a RAG format, with latency measured over the public internet. The self-hosted setup used a 192 GB GPU, while managed setups likely used lower precision, which is a limitation favoring managed options over self-hosting. High concurrency figures were derived from single runs, so they should be considered estimates.

All raw data and scripts are publicly available, and it’s important to verify current pricing before making decisions, as rates can change.

Deployment Options

• Serverless: This option involves calling an API and paying per token, with the provider managing the infrastructure.
• Dedicated: You rent a full GPU capacity as a managed endpoint, offering isolation but still run by the provider.
• Self-hosted: You rent or purchase a GPU and manage the server yourself, granting maximum control but also full responsibility.

Each option should be evaluated based on cost, latency, scalability, and operational workload.

Experiment Overview

The study fixed the model and workload types, measuring the impact of different deployment models on performance and cost:

• Option A: Serverless Inference
• Option B: Dedicated Inference
• Option C: Self-hosted GPU Droplet

Provisioning Times

Provisioning times varied significantly:

• Serverless: Operational in seconds, with payments only for usage.
• Dedicated: Took about 25 minutes before being operational, billing during the warm-up.
• Self-hosted: Set up in approximately 4 minutes, with full responsibility for management.

Latency Analysis

A self-hosted GPU offers faster initial response times, but performance degrades with high concurrency. A self-hosted GPU can manage about 50 tokens per second, while serverless options are slower but handle bursty workloads better.

Cost Analysis

The break-even point for cost efficiency between self-hosted and serverless solutions depends on GPU utilization:

• Saturation: Break-even at about 22% duty cycle with 40-second response times.
• Snappy: Break-even at about 48% duty cycle with 15-second response times.

Underutilized GPUs can be significantly more expensive than serverless options.

Dedicated Inference Costs

Dedicated Inference is about 30% more expensive than self-hosted options, requiring around 29% utilization to break even. Buying hardware may offer savings, but only if sustained usage justifies the initial investment and ongoing management.

Transition Strategy for Startups

• Default to Serverless Inference: Ideal for early stages with variable traffic.
• Switch to Dedicated or Self-hosted: When consistent performance, isolation, or data control is needed.
• Monitor Utilization: Transition to self-hosting when consistent base-load supports it.

Common Pitfalls

• Underestimating Utilization: Leading to higher costs with idle GPUs.
• Neglecting Idle Billing: Especially during setup phases.
• Measuring Throughput Incorrectly: Ensure accurate measurement by accounting for network bottlenecks.
• Comparing Costs Incorrectly: Ensure comparisons are based on complete answers rather than raw token counts.

Conclusion: Optimize model choice and utilization first, then select the appropriate deployment model. Default to serverless and transition to self-hosting when utilization justifies it.