List of BentoML Platform Customers
San Francisco, 94108, CA,
United States
Since 2010, our global team of researchers has been studying BentoML Platform customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased BentoML Platform for AI infrastructure from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using BentoML Platform for AI infrastructure include: Yext, a United States based Professional Services organisation with 1200 employees and revenues of $300.0 million, Mission Lane, a United States based Banking and Financial Services organisation with 400 employees and revenues of $70.0 million, Neurolabs, a United Kingdom based Retail organisation with 35 employees and revenues of $2.0 million and many others.
Contact us if you need a completed and verified list of companies using BentoML Platform, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Mission Lane | Banking and Financial Services | 400 | $70M | United States | BentoML | BentoML Platform | AI infrastructure | 2023 | n/a |
In 2023, Mission Lane rebuilt its MLOps stack on the BentoML Platform to run model scoring and real-time decisioning for credit, risk, and related finance workflows in the United States. The BentoML Platform, described as AI infrastructure, became the primary runtime for serving production models and executing low latency decisioning logic across finance workflows.
Implementation centered on packaging models as bentos and deploying them into production, the team reports the first bentos went live in 2023 and Mission Lane now operates 24 production bentos, with three serving live decisioning traffic. Configuration and automation work focused on standardizing model packaging and deployment artifacts, simplifying CI/CD for model scoring and increasing deployment velocity for model updates.
Operational scope covers credit, risk and related finance teams within the United States, with production bentos embedded in real-time decision flows. Governance and process adjustments emphasized a clearer model lifecycle handoff between data science and engineering teams, and rollout proceeded by incrementally bringing bentos into production for decisioning workloads.
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Neurolabs | Retail | 35 | $2M | United Kingdom | BentoML | BentoML Platform | AI infrastructure | 2024 | n/a |
In 2024 Neurolabs deployed the BentoML Platform as its AI infrastructure to run synthetic computer-vision pipelines that power retail and CPG shelf-audit and in-store analytics for its customers. Neurolabs BentoML Platform AI infrastructure serves as the core inference and model serving layer for production vision workloads, aligning model packaging and serving with operational analytics needs.
The implementation leveraged the BentoML Platform inference capabilities to package models into consistent artifacts, expose standardized model serving endpoints, and orchestrate containerized deployments across cloud compute. Functional configuration emphasized model serving, inference orchestration, autoscaling and model lifecycle management to support frequent daily model updates and predictable production behavior.
Operational coverage targeted customer-facing retail sites and in-store analytics pipelines, with ML engineering and operations teams running continuous model iteration and release cadence. The deployment supported CI/CD style model workflows and standardized deployment templates to reduce friction for daily model iteration and maintenance.
Governance established model lifecycle controls and formalized deployment workflows under ML engineering ownership, reducing ad hoc operational work. The rollout accelerated time-to-market by nine months, cut compute costs by roughly 70 percent, enabled daily model iteration, and avoided additional infrastructure hires.
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Yext | Professional Services | 1200 | $300M | United States | BentoML | BentoML Platform | AI infrastructure | 2024 | n/a |
In 2024, Yext implemented the BentoML Platform to standardize and accelerate AI model serving for its digital presence and search products across global markets. The deployment positioned BentoML Platform as a core AI infrastructure capability supporting Yext product engineering and search teams.
The implementation focused on inference serving and orchestration, including hosted serving runtimes, automated model deployment pipelines, and registry workflows to manage model artifacts and versions. Autoscaling controls and multi region BYOC deployments were configured to allow model instances to scale with traffic and to run on cloud capacity chosen by regional teams.
Operational coverage emphasized production model hosting for Yext search and digital presence products, with teams shipping twice as many production models after adoption. Yext reported explicit outcomes, including 2x faster time to market and up to 80% lower compute costs, driven by the multi region BYOC deployment model and automated autoscaling in the BentoML Platform.
Governance workstreams centralized model lifecycle controls and standardized deployment patterns to align product teams on release processes and runtime configurations. The BentoML Platform implementation thus created a repeatable AI infrastructure blueprint for scaling inference operations across Yexts global markets.
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