Vertex AI Performance Collapse After Gemini 3.0 Release Raises Enterprise Concerns

Five days after Google released Nano Banana Pro (Gemini 3.0 Image Preview), multiple startups relying on Google's Vertex AI service have reported a dramatic fivefold increase in latency for their fine-tuned models. The timing has led many to suspect that the new model release is consuming significant compute resources at the expense of existing enterprise customers.

According to reports from affected companies, the latency instability began immediately following the release of Nano Banana Pro. "We've talked with other startups who also make use of finetuned 2.5 Flash models, and they're seeing the exact same, even those in different regions," stated one affected developer in a post on Hacker News. "Obviously this has a big impact on all of our products."

The performance degradation comes at a critical time for Google's AI ambitions, as the company competes with OpenAI and other providers in the rapidly evolving large language model market. While Google has positioned Vertex AI as an enterprise-focused solution, the recent instability has raised questions about the company's ability to balance consumer and business workloads.

The Silence from Google

What has particularly frustrated affected customers is the lack of communication from Google's support team. "From Google's side, nothing but silence, and this is talking about paid support," the developer noted. "The reply to the initial support ticket is a request for basic information that has already been provided in that ticket or is trivially obvious. Since then, it's been more than 48 hours of nothingness."

This stands in contrast to industry expectations for enterprise-grade services, where transparency during incidents is considered best practice. The communication breakdown has led some to question Google's commitment to its enterprise customers.

Resource Allocation Concerns

While the timing could be coincidental, affected developers believe the most likely explanation is resource contention between the new Gemini 3.0 Preview models and existing fine-tuned models. "We can all see what's most likely here; Nano Banana Pro and Gemini 3 Preview consuming a huge amount of compute, and they're simply sacrificing finetuned model output for those," suggested the developer.

This raises broader questions about how Google manages compute resources across its various AI services. The incident highlights the potential risks of relying on a single provider for critical AI infrastructure, especially during periods of high demand or new releases.

Comparing Providers

The performance issues come as developers increasingly compare the reliability of different AI model providers. "For all their faults, OpenAI have been a bastion of stability, despite being the most B2C-focused of all the frontier model providers," the affected developer noted.

This comparison underscores a growing concern in the industry: as AI providers race to release new models and features, they may be compromising the stability of existing services. For enterprise customers, reliability often trumps cutting-edge features, making this a potentially significant competitive disadvantage for Google.

Industry Impact

The incident serves as a cautionary tale for startups building on top of cloud AI services. "I'm posting this mostly as a warning to other startups here to not rely on Google Vertex for user-facing model needs going forward," the developer concluded.

For the broader industry, this highlights the need for robust service-level agreements and contingency planning when adopting AI services from major providers. As AI becomes increasingly integrated into business-critical applications, the reliability of these services will become a key differentiator in the market.

Lessons for the AI Ecosystem

The Google Vertex AI performance issues reflect broader challenges in the rapidly evolving AI landscape. As providers balance innovation with reliability, customers must carefully evaluate their options and prepare for potential disruptions.

Temporary outages are inevitable in complex cloud environments, but the prolonged nature of this incident—now extending five days with no resolution—has pushed it beyond acceptable limits for many enterprise customers. "Temporary outages are understandable and happen everywhere, see AWS and Cloudflare recently, but 5+ days - if they even fix it - of 5x latency is effectively a 5+ day outage of a service," the developer pointed out.

As the AI industry continues to mature, we can expect providers to develop more sophisticated resource management systems and incident response protocols. Until then, customers will need to remain vigilant and diversified in their AI infrastructure strategies.

Source: Hacker News discussion thread (https://news.ycombinator.com/item?id=46042273)