7 Days to Prototype: How Jabali AI Accelerated Time-to-Value with Bento

“Jabali is on the cutting edge of generative AI games. Visuals are an important part of that experience. With Bento’s hands-on support, we develop and operate complex visual pipelines quickly, delivering results without increasing our burn rate.”

— Vatsal Bhardwaj, Founder & CEO, Jabali AI

About#

Jabali AI is on a mission to make every game player a solo game studio. The platform is both LLM-agnostic and game-engine-agnostic, giving creators freedom to choose engines, languages, and models while sharing games with a single click. The company is also launching Jabali Studio, a no/low-code platform that lets anyone create and publish entire 2D and 3D games using natural language.

Prior to working with Bento, Jabali was using a complex stack that included image diffusion models, open-source asset and sound pipelines, model deployment frameworks, and working directly on public cloud provider instances. The AI space is rapidly evolving with new patches and advances. That complexity meant that the AI engineers at Jabali spent time on undifferentiated work of maintaining infrastructure instead of developing features to move the business forward.

Challenge: Building on the bleeding edge without growing burn#

As Jabali AI prepared to launch Jabali Studio, Vatsal Bhardwaj, Founder & CEO, needed advanced AI infrastructure to support generative visual asset pipelines, flexible model deployment, and seamless integration of game and AI technologies.

Generative visual pipelines required advanced GPU coordination and model chaining, tasks that demanded precise orchestration to maintain performance and memory efficiency across diffusion and control models. Building a game means stitching together dozens of different assets, storylines, characters, music, objects, but visual assets required orchestration that wasn’t available out of the box. “We are on the cutting edge, and the solutions didn’t just exist,” explained Vatsal.

Without the right platform, the team risked manual, error-prone pipeline setup and scaling. Diverting critical resources from core game R&D also risked increasing burn rate and slowing the launch of Jabali Studio.

As a lean team with core strengths in AI, machine learning, and game technology, infrastructure and operations, particularly MLOps and emerging LLMOps, was not in their wheelhouse. While hiring engineers to build inference pipelines internally was an option, it would have raised burn, added overhead, and distracted the team from its core differentiators.

Faced with these challenges, Vatsal explored cloud providers and open-source tools, but those left the team with manual setups. This DIY approach slowed progress and risked stalling innovation, as Jabali lacked the bandwidth to troubleshoot or adapt off-the-shelf workflows to their custom use cases.

When a team member met Bento at an industry event, Vatsal quickly realized that Bento was the expert partner they needed to tackle complex infrastructure challenges.

 

“We required an expert in setting up and operating visual pipelines. Bento’s team came across as customer-obsessed and worked with our team to develop a solution, versus simply pointing us to an API and documentation.”

Solution: Standing up complex visual asset pipelines#

Bento partnered directly with Jabali’s team to design and operate their generative visual asset pipeline. Onboarding was fast; within a week, the system was up and running — something manual cloud setups couldn’t deliver.

The Bento Inference Platform empowers Jabali with:

• Advanced model serving and integration, deploying both standard and non-standard architectures with integrated APIs to orchestrate generative pipelines.
• Workflow automation and scalability, automating asset generation tasks and scaling horizontally to generate multiple visual assets in parallel.
• Modular deployment structure, offering a flexible, framework-agnostic serving infrastructure.
• Expert, hands-on support, avoiding infrastructure costs and unnecessary burn associated with hiring infra engineers.

To power this system end-to-end, Jabali’s infrastructure leverages open-source tools integrated within Bento’s managed environment.

Jabali’s generative pipelines are built on a combination of open-source and in-house pipelines, and Bento’s toolkit ensures version-locked reproducibility and containerized deployment across Docker and Kubernetes environments.

Each visual pipeline typically combines 2–5 diffusion-based models, such as SD, SDXL, and Flux, along with supporting models for refinement, including VAEs, LoRAs, ControlNets, upscalers, and face restoration models.

By orchestrating these pipelines through the Bento Inference Platform, Jabali ensures seamless workflow automation and scalable performance. Fast autoscaling improved average GPU utilization by roughly 10–15%, eliminating idle compute during low-load periods and reducing the need for over-provisioning.

The system also saturates available network bandwidth during model loading, ensuring models start up as quickly as hardware allows.

The value didn’t stop at visuals. With the Bento Inference Platform, Jabali can deploy any model, including those not supported by large cloud providers. This flexibility enables the team to focus on product innovation, rapidly experimenting with new models and pipelines to stay at the forefront of generative AI in gaming.

 

“Bento really shone by working with us closely to develop tailored solutions. We can pick any model from the research community, and Bento helps us get it running.”

Results: Value in under 7 days, no infra hires required#

Through their partnership with Bento, Jabali rapidly built, automated, and scaled sophisticated visual asset pipelines for game development. This partnership reduced time to market, improved GPU and operational efficiency, and freed Jabali’s team to focus entirely on advancing generative game creation.

• 7 days from onboarding to first working prototype: Within a single week, Jabali had a working system in place, accelerating their roadmap for Jabali Studio and giving the team confidence to move faster on new features.
• 2 engineers and 3 months of work avoided: Without the need to hire dedicated MLOps or infrastructure specialists, Jabali kept its burn rate low and avoided long-term overhead, preserving resources for core AI and game development.
• 0 hours of founder time spent managing contractors: With Bento managing the infrastructure, Jabali’s leadership avoided the distraction of overseeing contractors and stayed focused on strategic product decisions.
• Significant reduction in unnecessary GPU expenditure: Autoscaling and orchestration cut idle GPU time and avoid over-provisioning, reducing spend on unused capacity.
• Expanded ability to experiment with frontier models: Jabali now tests and iterates on community models and architectures unavailable on major cloud providers, without adding infrastructure overhead.

Looking ahead, Jabali AI is preparing to launch Jabali Studio, inviting its first users and exploring game-making contests. Bento remains a key partner as the company tackles even more complex pipeline challenges in graphics and game development.

 

“Bento helps us focus on our core differentiators without getting caught up in infrastructure. We’re on the bleeding edge and don’t always know what problems we’ll need to solve next. But even if there isn’t a solution, we know we can work with Bento to craft one.”

More resources#

• Learn more about Jabali AI
• Sign up for Bento Inference Platform for free. Build and scale your custom AI systems in production, with any model, on any cloud or on-premises environment.
• Join our Slack community to get the latest information about Bento.
• Have questions? Schedule a call with our experts.