YC-backed Luminal raises $25M from Standard Capital to loosen Nvidia's grip on AI inference. November 12, 2026 * Luminal has raised $25M in Series A funding led by Standard Capital. * The company says its cloud now processes about 90 trillion tokens a month. * The money funds engineering, go-to-market and support for non-Nvidia chips. As demand for artificial intelligence grows, the cost of running models is becoming a major infrastructure challenge. Much of the industry still relies on Nvidia's GPU ecosystem, while companies face limited hardware choices and complex optimisation requirements. Luminal, a San Francisco-based AI infrastructure startup, has raised $25 million in Series A funding led by Standard Capital. All of its seed investors, including Y Combinator, Felicis, SagaVC, and Liquid, doubled down. The company did not disclose its valuation. The US startup is developing a search compiler and inference cloud to help AI developers run models faster and more affordably across GPUs and application-specific integrated circuits, short for ASICs. The funding will support engineering and go-to-market expansion, alongside broader compatibility with AI models and hardware platforms. With its $5.3 million seed round in November 2025, Luminal has raised about $30.3 million in total. Founded to make AI inference more accessible. Luminal was co-founded by CEO Joe Fioti, a former Intel chip designer, with Matthew Gunton and Jake Stevens, who come from Amazon and Apple. The company was part of Y Combinator's Summer 2025 batch. Its seed round was led by Felicis, with angel investors including Paul Graham, Guillermo Rauch and Ben Porterfield. It began by building open-source tools on GitHub as AI adoption accelerated, after spotting demand for software that could optimise model performance across different chips. Its central proposition is to reduce dependence on a single hardware ecosystem. Optimising AI models for different processors often requires specialised engineering and extensive manual tuning, making it hard for developers to switch between hardware providers. Luminal's search compiler addresses this by testing mathematically equivalent ways of executing a model on a particular chip and selecting the fastest implementation. Once optimised, the implementation can be reused for repeated workloads on the same model and hardware combination. "We started by building in the open on GitHub as AI adoption took off, and quickly saw how much demand there was for a more efficient way to run AI models across different hardware. The cost of running AI shouldn't limit who gets to build it. We're building the infrastructure to make high-performance inference more accessible, giving developers more choice beyond NVIDIA's ecosystem without forcing them to compromise on speed or cost," said Joe Fioti, co-founder and CEO of Luminal. A compiler and cloud for faster inference. Luminal combines its compiler with an inference cloud, allowing developers to deploy AI models without managing the underlying infrastructure. The compiler optimises how models execute on available hardware, while the cloud provides access to those computing resources. Together, they aim to improve throughput and lower the cost of serving AI applications. The platform targets workloads requiring continuous, high-volume inference, including AI coding agents, voice agents and image-editing tools. These applications need models to respond reliably to large numbers of requests, so infrastructure efficiency matters. Since launching its public cloud following the seed round, the company says it has reached approximately 90 trillion tokens processed per month within four months. Luminal also says it has committed a significant portion of these workloads to Amazon's Trainium chips through a growing partnership with Amazon. The deal supports its strategy of helping customers use alternative AI hardware that can deliver competitive performance at lower costs. Competition in AI inference optimisation. Luminal operates in a growing market of companies developing software and infrastructure to improve AI computing efficiency. Palo Alto-based Standard Kernel raised $20 million in seed funding in March 2026, led by Jump Capital, to automate GPU kernel optimisation using AI. RadixArk, which emerged from the open-source SGLang project, was valued at about $400 million in January 2026, with Accel as lead investor. Elsewhere, Finnish AI cloud provider Verda raised $117 million in April 2026, led by Lifeline Ventures, to expand its GPU cloud as an alternative to traditional hyperscalers. Larger inference provider Together AI raised $800 million at an $8.3 billion valuation in July 2026. Unlike rivals, Luminal combines compiler-level optimisation with a managed inference cloud, while extending support beyond Nvidia GPUs to alternative processors such as Amazon Trainium. Inference becomes a growing infrastructure market. The opportunity is expanding as AI applications move from model training towards continuous deployment and usage. Grand View Research estimates that the global AI inference software market was valued at $17.5 billion in 2025 and could reach $80.9 billion by 2033, growing at a compound annual rate of 21.1%. For developers, inference costs directly affect the economics of running AI products. Using hardware more efficiently and requiring fewer resources per response can make applications cheaper to run at scale. Luminal believes the broader AI infrastructure opportunity could reach $5 trillion, although this is the company's estimate rather than an independent market forecast. "Inference is the most important market in the world right now, not to mention the fastest-growing. We backed Luminal because of their deep technical expertise and demonstrated proof they can squeeze fantastic performance out of heterogeneous hardware. Luminal's ability to get the most out of GPUs that have already been purchased and powered is incredibly high leverage," said Dalton Caldwell, managing partner of Standard Capital and a former managing partner at Y Combinator. Luminal envisions developers increasingly prioritising performance and cost over loyalty to a single chip ecosystem. So, can it make hardware flexibility work at production scale without compromising speed or reliability?

Luminal, a search compiler and inference cloud provider, has raised a $25 million Series A led by Standard Capital, chief executive officer Joe Fioti told Axios Pro. The company's technology lets customers run workloads on different chips rather than rely on Nvidia.

Reflex raises $14M Series A for custom enterprise Apps. Selling a company software it can keep gives Reflex a demanding proposition to build around. The San Francisco startup offers customers editable applications, a Python foundation and engineers who can help put those applications to work, leaving the continuing commercial relationship to earn its place alongside code the buyer already controls. Reflex has raised a $14M Series A led by Standard Capital, with Databricks Ventures, Y Combinator, Lux Capital and Abstract Ventures participating, according to SiliconANGLE's October 29 report. Co-founders Nikhil Rao, CEO, and Alek Petuskey, CTO, are expanding a business whose assignment now includes helping customers choose, build and deploy the software their operations require. The financing supports engineering and sales expansion as the company prepares its Workflows product. What Reflex's Series A is funding. SiliconANGLE reports that the round brings Reflex's total funding to $19M. The company's own August 2023 seed announcement documents a $5M round led by Lux Capital, backing the open-source ecosystem and a hosting service. That earlier investment addressed a developer's journey from writing Python to deploying a web application; the current offer reaches further into the customer's operating business. Reflex's enterprise service starts with an agreed project and a measure of success. Engineers work alongside the customer, connect the relevant systems, prepare access and deployment with IT, and organize a handover with code, documentation and support. Building an application together is scoped as its own project, separate from the onboarding included in enterprise plans. That division matters to the purchase. A customer needs to understand what the platform supplies, which implementation work is included and who will carry the application forward after rollout. Reflex is putting those questions inside its commercial offering. The work can begin with a purchasing approval or a dashboard around existing systems, giving the team a bounded assignment before it considers replacing something as consequential as an ERP. The Python project has become an enterprise proposition. Reflex's founders started with a problem familiar to Python developers: useful logic can be ready well before there is a usable web application around it. The seed announcement dates the framework's launch to December 2022. The company then participated in Y Combinator's Winter 2023 batch, developing the infrastructure around an open-source foundation. The current documentation spans UI components, databases, authentication, deployment, secrets and observability. That breadth shows how much has to surround generated application code before a team can share an internal tool with colleagues. A useful screen needs a route to the right data, the right access rules and someone able to understand a failure when work depends on it. Company materials report more than 1M applications built, nearly 30,000 GitHub stars and usage inside 30% of the Fortune 500. These are different measures of developer adoption, with no published conversion rate from framework use to paying enterprise contracts. They describe an unusually broad introduction to prospective users while leaving the sales team with the more personal task of finding a funded problem inside each organization. For buyers, the open-source foundation also changes the review. Their developers can inspect and extend the code, bringing technical judgment into a purchase that might otherwise be judged mainly through a vendor demonstration. Deployment belongs in the buying decision. Reflex's bring-your-own-cloud offering supports deployment into an enterprise customer's AWS, GCP or Azure account. Its product documentation describes builds, registries and managed runtimes operating within that customer's cloud. The proposition addresses a constraint that exists before the first application feature is considered: the organization may already have decided where its workloads are allowed to run. The company also established a Databricks Apps integration in June 2025. That integration connects the AI Builder to data and deployment inside the Databricks environment. For a Python-oriented data team, the route from an existing data investment to an application can therefore remain close to infrastructure the organization already uses. The operating value becomes clearer in Reflex's Autodesk customer case study. The company-hosted account describes a consultant delivering a connected client application during a 60-day engagement, moving project information out of recurring spreadsheet handoffs. It is a specific customer account, rather than a forecast for every buyer, and its relevance lies in the delivery arrangement: a person with knowledge of the customer's problem could take more of the application into production. That is where the sales conversation becomes concrete. Buyers can ask which systems the proposed application touches, where it runs and which person will be able to change it when the process changes. Workflows extends the job beyond the application. Reflex's planned Workflows product remains on a waitlist. The company describes processes backed by readable Python, with progress saved between steps, explicit human approvals and retries after failed calls. Its examples follow work across documents, customer systems and notifications, extending the platform's role from presenting information into coordinating an operation. Those mechanics carry a different kind of responsibility. A workflow that waits for a purchasing decision must preserve whose approval is needed and what happens after it arrives. A retry that changes an external system needs to handle the possibility that part of the work already succeeded. Reflex's description puts these details in the application code and its execution history, where a customer's team can inspect them. The financing arrives while Reflex is also hiring for the commercial work around delivery. Current company job listings ask for help turning open-source adoption and founder-led demand into a repeatable sales operation. Rao and Petuskey have a framework already moving through organizations; the expanding team now has to follow it into the projects, cloud accounts and working relationships where a company decides to make that framework part of its business. DevCuration Data Developer Tools funding, last 30 days. DevCuration's funding database tracked 2 Developer Tools rounds totaling $7.4M in disclosed capital over the past 30 days. Recent deals DevCuration covered: