AI Success Stories

How Higgsfield Scales Generative Video with GMI Cloud

Q: Why did Higgsfield choose GMI Cloud instead of hyperscalers, spot-only GPU providers, or building in-house?

The case study contrasts options: hyperscalers (AWS, Azure, GCP) came with higher cost, rigid infrastructure, long procurement and generalized support; other GPU providers focused on spot or preemptible capacity without reliable SLAs for real-time workloads; and an in-house approach would have required high capital expense, operational complexity, and slower time-to-market. GMI Cloud offered immediate access to needed capacity, infrastructure tuned for low-latency inference, transparent pricing aligned with startup growth, and a responsive team.

Higgsfield partnered with GMI Cloud to bring cinematic generative video to everyone, delivering studio-quality creativity with intuitive tools, faster innovation, scalable infrastructure, and 45% lower compute costs.

Overview

Higgsfield is redefining what’s possible in generative video by delivering tools that make cinematic creativity accessible to everyone. With an intuitive editing experience, pre-built visual effects, and fine-grained camera control, Higgsfield empowers creators to produce studio-quality video without the need for technical expertise.

These high-performance creative tools are ideal for digital advertising, content marketing, and social storytelling — where speed, quality, and ease of use are paramount. Higgsfield chose GMI Cloud as its strategic partner. The result: faster innovation, high-performance scalability, and a tailored infrastructure stack purpose-built for generative AI — all while reducing compute costs by 45%.

“Generative video is one of the most demanding AI workloads. It requires real-time inference, top-tier performance, and the ability to scale without tradeoffs. GMI Cloud meets those needs and, more importantly, they partner with us on every step of the journey,” said Alex Mashrabov, CEO of Higgsfield.ai.

45%

lower compute costs compared to prior providers

65%

reduction in inference latency

The Challenge

Powering Cinematic Video Generation at Scale

Higgsfield needed a flexible and powerful infrastructure partner to handle:

High-throughput inference for real-time video generation and editing
Rapid model iteration with cost-effective scaling
Tailored GPU performance for visual fidelity and responsiveness

Before switching to GMI Cloud, Higgsfield encountered:

Average model training time of 24 hours per run
Inference latency of 800 ms under production load
Compute costs growing 25% month over month on traditional cloud services

Generic cloud solutions fell short on cost, performance tuning, and support, especially for inference-heavy, media-centric workloads.

The Solution

GMI Cloud as a Necessary Infrastructure Partner

GMI Cloud delivered an infrastructure solution customized for the generative video stack:

Access to the newest NVIDIA GPUs, enabling smooth rendering and scalable deployment
Custom cluster and inference engine access, optimized for Higgsfield’s unique workload profile
Right-sized resource planning to reduce idle spend and enable rapid scale-up
Hands-on partnership, with GMI acting as an extension of Higgsfield’s technical team

Why the Partnership Worked for Generative Video

GMI Cloud delivered the performance and flexibility Higgsfield needed, while aligning infrastructure strategy with their long-term creative roadmap.

Performance that matches creative vision: low latency, high output quality
Agility to support rapid R&D: infrastructure that evolves with product and model iterations
Aligned incentives: a partner invested in Higgsfield’s success, not just a vendor

Key metrics of improvement:

45% lower compute costs compared to prior providers
65% reduction in inference latency, enabling smoother real-time user experiences
200% increase in user throughput capacity, allowing Higgsfield to scale with demand

Comparison with Alternatives

Hyperscalers (AWS, Azure, GCP): High cost, rigid infrastructure, slow provisioning, generalized support‍

Other GPU providers: Lack of top-tier GPUs, inflexible contracts, limited customization‍

In-house infrastructure: High capital expenditure, operational complexity, slower time-to-market

Key Drivers Behind the Decision

Immediate access to GMI Cloud’s infrastructure recognized as Reference Platform NVIDIA Cloud Partner
Infrastructure tailored to real-time inference needs
Transparent pricing aligned with startup growth
Responsive team that adapts to product and engineering changes

Future Plans

Higgsfield is entering a major growth phase. As their user base expands and product features evolve, the need for scalable compute and holistic cloud solutions will only increase. From experimentation and model refinement to production-grade deployment and global delivery, Higgsfield sees GMI Cloud as a core part of their infrastructure roadmap.

The team expects to grow its reliance on GMI’s broader cloud capabilities, spanning orchestration, storage, and workload management, while continuing to scale inference workloads at the pace of user demand.

"We’re building the future of video creation, and GMI Cloud gives us the foundation to do it without compromise. As our infrastructure needs grow, we know they’ll grow with us."

Alex Mashrabov

CEO of Higgsfield.ai

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Frequently Asked Questions about How Higgsfield Scales Generative Video with GMI Cloud

Get quick answers to common queries in our FAQs.

1. What measurable results did Higgsfield get after moving to GMI Cloud for generative video?‍

Higgsfield reports 45% lower compute costs, 65% reduction in inference latency, and a 200% increase in throughput capacity, enabling smoother real-time experiences and room to scale output.

2. Why did Higgsfield choose GMI Cloud instead of hyperscalers, spot-only GPU providers, or building in-house?‍

The case study contrasts options:

Hyperscalers (AWS, Azure, GCP): higher cost, rigid infrastructure, long procurement, generalized support.
Other GPU providers: focus on spot/preemptible capacity without reliable SLAs for real-time workloads.
In-house: high capital expense, operational complexity, and slower time-to-market.
GMI Cloud offered immediate access to needed capacity, infrastructure tuned for low-latency inference, transparent pricing aligned with startup growth, and a responsive team.

3. What parts of the generative video pipeline did GMI Cloud help with day-to-day?‍

GMI Cloud functioned as a dedicated infrastructure partner: providing the capacity and serving layer for real-time inference, orchestration to maintain performance under production load, and hands-on collaboration that aligned with Higgsfield’s rapid product iteration.

4. How did the partnership improve user experience for Higgsfield’s creative video products?‍

By cutting inference latency by 65% and boosting throughput capacity by 200%, the system delivered smoother real-time experiences for cinematic-quality generation, while the 45% cost reduction let the team scale without compromising quality.

5. What decision factors mattered most to Higgsfield when evaluating an AI infrastructure partner?‍

Key drivers included immediate access to capacity, reliability for real-time inference, transparent/scale-friendly pricing, and a partner team that adapts quickly to product and engineering changes.

6. How does this setup support Higgsfield’s future roadmap?‍

The plan is to expand models and products and release new features at scale. The case study notes that GMI Cloud provides the foundation to grow without compromise, with the team ready to scale alongside Higgsfield as workloads increase.