BananaDev

AI Machine Learning Model Hosting Tool

BananaDev - AI Tool for Hosting ML Models on GPUs
BananaDev - AI Machine Learning Model Hosting Tool Website Screenshot
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BananaDev has been marked as closed, shutdown or acquired by our review team. You can find out more information about BananaDev below.

Why Did Banana.dev Shut Down? The Rise and Quiet Fall of a Serverless AI Darling

Ever wondered why BananaDev—a startup once promising to make deploying ML models as easy as copy-pasting a line of code—vanished from the AI landscape? If you’ve tried visiting banana.dev lately, you may have noticed the site is no longer up, and the silence surrounding its fate has left many developers puzzled.

BananaDev (or simply banana.dev) launched with the vision of streamlining the messiness of getting machine learning models into production. It gained early traction in a hot market, scored millions in seed funding, and became a go-to solution for serverless GPU inference. So… what went wrong?

In short: It ran out of runway. But the longer answer is a bit more complicated—and actually quite revealing about the evolving nature of AI startups in 2024 and beyond.

Let’s rewind and break it all down.


What Was Banana.dev?

Banana.dev was a San Francisco-based startup focused on simplifying machine learning model deployments. Founded by Erik Dunteman (who also created Booste—a previous iteration of the platform), Banana offered a “serverless GPU” platform that let developers deploy ML inference APIs with just a few lines of code.

Its central pitch? “Productionize your machine learning model as simply as copying and pasting an endpoint.”

Over time, Banana.dev earned a reputation among indie developers and small AI startups for lowering the friction between experimentation and deployment.

Key Highlights:

  • Launch Date: Rebranded from Booste and active as banana.dev by early 2021
  • Core Offering: Serverless GPU platform for fast, scalable ML inference
  • Funding:
  • $1.6 million pre-seed led by Pioneer
  • $3.4 million seed round led by Basecamp Fund
  • Milestones:
  • Reached $10K MRR during early growth phase
  • Integrated services like GitHub, CLI, and CI/CD pipelines
  • Offered GPU options like Nvidia A100s for heavy ML workloads

Everything seemed to be going right—at least for a while.


Why Did Banana.dev Fail?

Short Answer:

Banana.dev shut down in March 2024 due to lack of revenue, rising technical costs, and tough market trends. The infrastructure demands of scaling serverless GPU inference outpaced what the startup could realistically deliver.

Long Answer:

Like many AI infrastructure startups trying to surf the 2023–2024 machine learning wave, Banana.dev faced a confluence of issues:

  1. Market Fit & Product Constraints
  • While the serverless model was appealing, real-world performance issues—like cold starts and latency—challenged product-market fit for serious production use.
  • For edge cases and hobbyists, Banana.dev worked fine. But for enterprise-grade deployments, more robust and customizable solutions grabbed attention.
  1. Monetization and Runway Problems
  • Despite early growth, traction eventually plateaued.
  • Monthly recurring revenue (MRR) and customer retention weren’t enough to sustain operating costs.
  • Investors may have hesitated to pour more funding into an infrastructure-heavy platform with weak short-term monetization signals.
  1. Cutthroat Competitive Landscape
  • The GPU inference space heated up fast with strong competition from players like:
    • RunPod (another GPU compute provider with customizable infra)
    • Replicate (offering similar ML inference APIs)
    • Giants like AWS, Google Cloud, and NVIDIA themselves
  • These competitors had larger ecosystems, deeper pockets, or stronger enterprise footholds.
  1. GPU Supply Chain Nightmare
  • Throughout 2022–2023, the GPU market was under tight supply constraints.
  • Banana.dev had rolled out high-tier A100 GPUs in 2022, but sourcing and operating these chips at scale likely became prohibitively expensive.
  • RunPod and other providers with custom infrastructure setups may have had better supply deals or partnerships.
  1. High Engineering Burden
  • Serverless GPUs are not trivial to manage. Performance tuning, cold boot time optimization, replica scaling—all technically complicated and resource-heavy.
  • Banana.dev frequently updated its infrastructure (e.g., "V2.1" rollout in late 2023), but these changes were likely reactive, not proactive.
  1. Funding Limitations
  • Despite raising over $5 million, that amount only goes so far in high burn-rate infrastructure startups.
  • Without sustainable revenue or outcomes significant enough to justify a Series A or acquisition, the company had to cut losses.
  1. Timing & Macro Trends
  • While Banana.dev rode the early phase of the generative AI wave, by 2024, the market shifted.
  • Demand consolidated around major players and end-to-end platforms.
  • Many developers started leveraging cloud-native AI tools from OpenAI, Hugging Face, or Google Cloud, reducing Banana’s edge.

How Did It Compare to Competitors?

Take RunPod, for example—a competitor that’s still going strong. Where Banana.dev focused on abstraction and serverlessness, RunPod offered more control: containerized models, customizable environments, and a better balance between automation and flexibility.

The difference?

  • RunPod embraced developer customization.
  • Banana.dev pursued elegant ease-of-use, which came at the cost of performance optimization and flexibility.

This meant that when bigger workloads arrived, Banana often faltered while others scaled.


Final Thoughts: What We Learned from Banana.dev

Banana.dev wasn’t a bad idea—it was a smart, developer-friendly response to the complexity of machine learning deployment. But the AI tooling space doesn’t just reward simplicity; it demands performance, scalability, and capital efficiency.

Banana.dev fell into a familiar startup trap: building something novel with great intentions, but facing insurmountable technical and financial realities in a volatile market.

And yet, its impact remains. It helped define and inspire a wave of ML deployment tools built for smaller teams and individual developers. Many will borrow from its playbook.

The final blog post summed it up with grace: clear, honest, and user-centric. In a world of dramatic exits and ghosted platforms, Banana’s quiet self-sunset was... refreshing.


FAQs about Banana.dev

Who founded Banana.dev?
Banana.dev was founded by Erik Dunteman, who previously launched a similar product under the name Booste.

When did Banana.dev launch?
The rebranded Banana.dev platform launched around early 2021, following initial development under the name Booste.

When did Banana.dev shut down?
The company officially shut down its serverless GPU platform on March 31, 2024.

How much funding did Banana.dev raise?
Banana.dev raised over $5 million in funding, including a $1.6M round led by Pioneer and a $3.4M seed round led by Basecamp Fund.

Why did Banana.dev fail?
The startup failed due to a mix of financial issues, stiff competition, technical hurdles, and a hard-to-scale business model centered on underpriced serverless GPU infrastructure.

What happened to Banana.dev’s technology and team?
As of now, it’s unclear what became of its technology or employees. No acquisition or open-sourcing has been publicly disclosed.


Serverless may live on, but Banana.dev’s story reminds us that even smart startups riding the AI wave can get caught in the undertow.

Dang contacted BananaDev to claim their profile and to verify their information although BananaDev has not yet claimed their profile or reviewed their information for accuracy.
Banana is a cutting-edge AI machine learning model hosting tool that simplifies the deployment of machine learning models on serverless GPUs. With its user-friendly Python framework, it allows developers to easily create API handlers for inference, data store connections, and third-party API calls. Banana takes care of the entire deployment process, including building your application into a Docker image and deploying it on their serverless GPU infrastructure. It excels in scaling, seamlessly adapting to varying traffic patterns while ensuring minimal cold boot times. To enhance convenience, Banana provides a wide range of prebuilt model APIs through their Community Templates, featuring over 70 model options for users to choose from. The pricing structure is designed to be flexible and cost-effective, with pay-as-you-go rates based on resource consumption. Moreover, Banana offers dedicated support, scalability, and discounts based on volume and commitment length, making it an indispensable tool for AI enthusiasts and developers.

What is banana.dev?

Banana.dev simplifies machine learning model hosting with a streamlined three-step process and a single line of code, tailored to assist AI teams in scaling their inference workloads effectively. Notable features include auto-scaling GPUs for cost optimization while maintaining performance, transparent pricing without hefty margins on GPU time, and a comprehensive platform experience integrating DevOps tools like GitHub, CI/CD, and real-time monitoring of performance metrics and errors. Additionally, business analytics enable users to track expenditure and endpoint usage, while an automation API and Potassium-powered backend offer flexibility and extensibility. With Banana, deploying models is hassle-free, ensuring seamless integration and efficient operation.

How much does banana.dev cost?

Banana.dev operates on a flat monthly rate supplemented by the compute cost, adopting a pass-through pricing model unlike conventional serverless providers. This model ensures precise billing based on actual usage. The pricing structure comprises two tiers:

  1. Team: Priced at $1200 per month with additional charges for compute usage. This tier accommodates up to 10 team members, supports 5 projects, and allows for a maximum of 50 parallel GPUs. Features include custom GPU types, logging, search functionality, percent utilization monitoring, autoscaling, request and business analytics, branch deployments, and environment management.

  2. Enterprise: Pricing available upon request, offering all features included in the Team tier, with added benefits such as SAML SSO integration, access to the Automation API, increased parallel GPUs, customizable inference queues, dedicated support, and a unique perk termed "Banana Delivery" available in San Francisco, where the CEO personally delivers bananas rich in potassium to your office. 

Banana.dev caters to AI teams seeking swift deployment and scalable operations.

How do I get started with Banana.dev?

Banana.dev caters to AI teams seeking rapid deployment and scalability. Here's a quick guide to getting started:

  1. Sign Up: If you're new to Banana, log in and follow the onboarding process. You'll need to connect your GitHub account and select the repositories you want Banana to access.

  2. Create a New Project:
      - Navigate to the Banana dashboard.
      - Click on "New Project."
      - Opt for "Deploy from GitHub."
      - Choose the repository you wish to deploy.
      - Monitor the build progress as Banana configures your project.

That's all! You're now equipped to utilize Banana.dev for hosting inference and scaling your AI workloads.

What are the benefits of banana.dev?

Banana.dev presents numerous advantages for AI teams aiming to expedite deployment and enhance scalability:

  1. Auto-Scaling GPUs: Banana dynamically adjusts GPU resources to optimize costs without compromising performance.
  2. Pass-Through Pricing: Unlike conventional serverless providers, Banana maintains transparent pricing by avoiding significant margins on GPU time, prioritizing scalability for users.
  3. Full Platform Experience: The platform offers comprehensive DevOps features including GitHub integration, CI/CD, CLI, rolling deploys, tracing, and real-time logs.
  4. Observability: Real-time monitoring of performance metrics, request traffic, latency, and errors ensures effective management and troubleshooting.
  5. Business Analytics: Users can track expenditure and endpoint usage to gain insights into business operations and customer interactions.
  6. Automation API: Banana offers an open API for extending functionalities and integrating with existing systems.
  7. Powered by Potassium: Users can leverage the open-source HTTP framework Potassium to develop their backend solutions seamlessly.

Banana.dev is meticulously crafted to streamline inference hosting, ensuring efficiency and eliminating hassles for users.

How does banana.dev work?

Banana.dev streamlines the process of ML model hosting with its user-friendly approach. Here's a breakdown of how it operates:

  1. Upload Your Model: Simply send your model to Banana for hosting.
  2. Auto-Scaling GPUs: Banana automatically adjusts GPU resources, optimizing costs without sacrificing performance.
  3. Full Platform Experience: Enjoy a complete DevOps experience, including GitHub integration, CI/CD, CLI, rolling deploys, tracing, and real-time logs.
  4. Observability: Monitor various performance metrics, request traffic, latency, and errors in real-time for effective management.
  5. Business Analytics: Gain insights into expenditure and endpoint usage to better understand your business and customers.
  6. Automation API: Customize and extend Banana's functionalities with their open API.
  7. Powered by Potassium: Utilize their open-source HTTP framework, Potassium, to develop your backend solutions seamlessly.

Banana.dev is tailored for AI teams seeking rapid deployment and efficient scalability, ensuring a hassle-free experience throughout.

BananaDev - AI Tool for Hosting ML Models on GPUs

Does BananaDev have a discount code or coupon code?

Yes, BananaDev offers a discount code and coupon code. You can save by using coupon code when creating your account. Create your account here and save: BananaDev.

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