How We Beat Silicon Valley at AI Deployment: 3x Faster, 1/5 the Cost For AI Engineers

Cost vs. Inference Speed for One-Click AI Deployment Platforms.

1. Introduction: One-Click Deploy Platforms

As AI adoption accelerates across industries, a new category of infrastructure platforms has emerged—“One-Click Deploy” AI platforms. These services allow developers to launch machine learning models as APIs with minimal configuration, handling the heavy lifting of:

Auto-scaling
Latency optimization
Cost efficiency
Deployment orchestration

These platforms are quickly becoming the preferred choice for AI engineers and startups who want to focus on product and model quality, rather than spending weeks building and maintaining custom inference infrastructure.

Compared to traditional cloud solutions—where teams often invest substantial time managing virtual machines, Kubernetes, and cost controls—One-Click Deploy platforms significantly reduce the operational overhead. This makes them especially attractive for:

Prototyping new ideas
Building minimum viable products (MVPs)
Quickly scaling production models with limited resources

With a growing number of players in this space, it has become increasingly important to understand how these platforms compare in terms of speed, cost, and reliability.

In this report, we present a benchmarking study of four popular One-Click Deploy platforms, including our own—Hyperpod AI—to evaluate how well they deliver on the promise of fast and affordable AI deployment.

2. Benchmarking Overview

Objective

To evaluate the latency and cost efficiency of AI inference across leading deployment platforms.

Platforms Compared

Hyperpod AI (Best Performance)
Baseten
Cerebrium AI
Lightning AI

Models Evaluated

Wav2Vec2 (Automatic Speech Recognition)
Whisper (Speech-to-Text Transcription)
ResNet-DUC (Image Segmentation and Classification)
Stable Diffusion (Text-to-Image Generation)

Each model was tested with 1,000 inference datapoints, collecting latency and cost data. Average inference times were computed per model, followed by an aggregate average across all model types (a form of micro-averaging).

Each model was deployed on the selected platforms, and 1,000 inference datapoints were recorded per model. These were used to compute:

Warm start average latency per model
Cold start latency (where measurable)
Hourly price of the underlying infrastructure

We then computed an aggregate average across all models per platform. This methodology approximates micro-averaging, giving equal weight to each individual inference rather than each model category.

Handling Variability in Performance

During testing, we observed that performance on some platforms varied significantly depending on the time of day and day of the week—likely due to fluctuating user demand and shared infrastructure load. To ensure fairness, we recorded performance data multiple times across several days, and for each configuration, we used the best observed performance during that period. This approach provides a more optimistic and stable view of each platform’s potential, mitigating the noise introduced by temporary load spikes.

This structured approach allows us to make an apples-to-apples comparison of how well each provider performs under optimal conditions, offering meaningful insight for developers deciding where to deploy their models.

3. Results Summary

*Cost vs. Inference Time for One-Click AI Deployment Platforms.*

Our benchmarking clearly shows that Hyperpod AI consistently delivers faster inference at a significantly lower cost compared to leading AI deployment platforms—up to 3x faster performance for approximately 1/5 the price.

Performance Breakdown

Provider	Price/hr (USD)	Cold Start (s)	Warm Avg (s)	Hardware/ Mode
Cerebrium	0.59	27.124	2.019	T4
Cerebrium	0.799	27.124	2.019	L4
Cerebrium	1.951	25.378	1.95	L40
Cerebrium	1.102	24.773	1.9	A10
Lightning AI	0.19	109.342	2.05	T4
Lightning AI	0.55	156.547	1.9188	L4
Lightning AI	1.55	145.609	1.8001	A100
Baseten	0.6312	2.456	2.402	T4
Baseten	0.8484	3.693	2.223	L4
Baseten	1.2072	4.005	2.001	A10
Baseten			Too slow	A100
Hyperpod	0.14	10.054	0.753	Test Mode
Hyperpod	0.31	1.085	0.686	Small
Hyperpod	0.61	0.725	0.458	Medium
Hyperpod	1	0.954	0.289	Large

Key Observations:

Latency: Across warm start conditions, Hyperpod recorded inference times as low as 0.289 seconds, outperforming all compared providers across every hardware tier.
Cost: Even at peak performance modes, Hyperpod’s pricing remains below most competitors’ mid-tier offerings.
Cold Start Time: Hyperpod’s cold start latency was also competitive, staying under 1 second in most performance modes, whereas other providers showed cold starts exceeding 25–150 seconds, especially on Lightning AI and Cerebrium.

A100 Note on Baseten:

We attempted to benchmark Baseten’s A100 configuration over multiple days and test runs. Unfortunately, the performance was consistently unresponsive or significantly delayed, making it infeasible to produce reliable benchmark results. As such, this configuration was omitted from our comparative data table.

4. Conclusion

Hyperpod AI’s performance benchmark reinforces its position as a viable alternative to high-cost, complex AI deployment platforms. As AI adoption grows, platforms that combine simplicity, affordability, and speed will be critical to enabling the next generation of applications.

→ Visit Hyperpod AI to get started