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Astronomer, the corporate behind the Apache Airflow-powered knowledge orchestration platform Astro, has secured $93 million in Sequence D funding as enterprises more and more search to operationalize AI initiatives by way of higher administration of their knowledge pipelines.
The funding spherical was led by Bain Capital Ventures, with participation from Salesforce Ventures and current traders together with Insight, Meritech, and Venrock. Bosch Ventures can be in search of to take part within the spherical, reflecting industrial curiosity within the expertise.
In an unique interview with VentureBeat, Astronomer CEO Andy Byron defined that the corporate will use the funding to expedite analysis and growth efforts and develop its international footprint, significantly in Europe, Australia, and New Zealand.
“For us, that is only a step alongside the way in which,” Byron mentioned. “We wish to construct one thing superior right here. I couldn’t be extra enthusiastic about our enterprise companions, our clients, our product imaginative and prescient, which I believe is tremendous sturdy in going after collapsing the information ops market.”
How knowledge orchestration turned the hidden key to enterprise AI success
The funding targets what {industry} analysts have recognized because the “AI implementation hole” — the numerous technical and organizational hurdles that forestall firms from deploying AI at scale. Knowledge orchestration, the method of automating and coordinating advanced knowledge workflows throughout disparate methods, has turn out to be an integral part of profitable AI deployments.
Enrique Salem, Associate at Bain Capital Ventures, defined the essential challenges dealing with enterprises at the moment: “Each firm operates a sprawling, fragmented knowledge ecosystem—utilizing a patchworks of instruments, groups, and workflows that battle to ship dependable insights, creating operational bottlenecks and limiting agility. On the coronary heart of this complexity is orchestration—the layer that coordinates all these transferring items.”
Salem famous that regardless of its significance, “at the moment’s orchestration panorama is the place cloud infrastructure was 15 years in the past: mission essential, but fragmented, brittle and sometimes constructed in-house with restricted scalability. Knowledge engineers spend extra time sustaining pipelines than driving innovation. With out strong orchestration, knowledge is unreliable, agility is misplaced, and companies fall behind.”
The corporate’s platform, Astro, is constructed on Apache Airflow, an open-source framework that has seen explosive development. Based on the corporate’s not too long ago launched State of Airflow 2025 report, which surveyed over 5,000 knowledge practitioners, Airflow was downloaded greater than 324 million occasions in 2024 alone — greater than all earlier years mixed.
“Airflow has established itself because the confirmed de facto commonplace for knowledge pipeline orchestration,” Astronomer SVP of Advertising and marketing Mark Wheeler defined. “Once we have a look at the aggressive panorama within the orchestration layer, Airflow has clearly emerged as the usual resolution for transferring trendy knowledge effectively from supply to vacation spot.”
From invisible plumbing to enterprise AI spine: The evolution of information infrastructure
Astronomer’s development displays a transformative shift in how enterprises view knowledge orchestration — from hidden backend infrastructure to mission-critical expertise that allows AI initiatives and drives enterprise worth.
“BCV’s perception in Astronomer goes approach again. We invested within the firm’s seed spherical in 2019 and have supported the corporate over time, now culminating in main their Sequence D,” Salem mentioned. “Past the spectacular development, Astronomer’s knowledge orchestration has turn out to be much more essential within the age of AI, which requires scalable orchestration and mannequin deployment automation amidst a ballooning sea of information instruments that don’t speak to one another.”
Based on the corporate’s inside knowledge, 69% of shoppers who’ve used its platform for 2 or extra years are utilizing Airflow for AI and machine studying functions. This adoption price is considerably larger than the broader Airflow neighborhood, suggesting that Astronomer’s managed service accelerates enterprise AI deployments.
The corporate has seen 150% year-over-year development in Astro (managed SaaS platform) annual recurring income and boasts a 130% internet income retention price, indicating sturdy buyer growth.
“Whereas market analysts could also be in search of a transparent winner within the cloud knowledge platforms battle, enterprises have clearly chosen a multi-solution technique—identical to they earlier decided that multi-cloud would far outpace standardization on any single cloud supplier,” Wheeler defined. “Main enterprises refuse to lock right into a single vendor, choosing multi-cloud and various knowledge platform approaches to remain agile and make the most of the most recent improvements.”
Inside Ford’s large AI operation: How petabytes of weekly knowledge energy next-generation autos
Main enterprises are already leveraging Astronomer’s platform for stylish AI use circumstances that will be difficult to implement with out strong orchestration.
At Ford Motor Company, Astronomer’s platform powers the corporate’s Superior Driver Help Programs (ADAS) and its multi-million greenback “Mach1ML” machine studying operations platform.
The automotive big processes multiple petabyte of information weekly and runs over 300 parallel workflows, balancing CPU- and GPU-intensive duties for AI mannequin growth throughout a hybrid public/non-public cloud platform. These workflows energy all the things from autonomous driving methods to Ford’s specialised FordLLM platform for big language fashions.
Ford initially constructed its MLOps platform utilizing Kubeflow for orchestration however encountered vital challenges, together with a steep studying curve and tight integration with Google Cloud, which restricted flexibility. After transitioning to Airflow for Mach1ML 2.0, Ford experiences dramatically streamlined workflows and seamless integration throughout on-premises, cloud, and hybrid environments.
From AI experiments to manufacturing: How orchestration bridges the implementation divide
A standard problem for enterprises is transferring AI from proof-of-concept to manufacturing. Based on Astronomer’s analysis, organizations that set up sturdy knowledge orchestration foundations are extra profitable at operationalizing AI.
“As extra enterprises are working ML workflows and real-time AI pipelines, they require scalable orchestration and mannequin deployment automation,” Salem defined. “Astronomer delivers on this at the moment, and because the orchestrator, is the one system that sees all the things occurring throughout the stack — when knowledge strikes, when transformations run, when fashions are skilled.”
Over 85% of Airflow customers surveyed anticipate a rise in external-facing or revenue-generating options constructed on Airflow within the subsequent 12 months, highlighting how knowledge orchestration is more and more powering customer-facing functions fairly than simply inside analytics.
This development is clear throughout industries, from automotive to authorized expertise firms which might be constructing specialised AI fashions to automate skilled workflows. These organizations are turning to Astronomer to deal with the advanced orchestration challenges that come up when scaling AI methods from prototypes to manufacturing environments serving hundreds of customers.
Strategic expertise growth: Airflow 3.0 and cloud partnerships place Astronomer for market management
The corporate not too long ago introduced the overall availability of Airflow 3.0, which it describes as “probably the most vital launch in Airflow’s historical past.” The replace introduces a number of transformative capabilities designed particularly for AI workloads, together with the flexibility to run duties “anyplace, any time, in any language.”
“Airflow 3.0 lays the inspiration for executing duties on any machine, on-prem or within the cloud, triggered by occasions throughout the information ecosystem,” Byron defined. “It additionally introduces a proof of idea for outlining duties in languages past Python, vastly bettering knowledge crew agility and facilitating migration from legacy methods to Airflow.”
Astronomer has additionally expanded its {industry} partnerships, not too long ago reaching the Google Cloud Prepared – BigQuery Designation, making its platform obtainable for buy straight from the Google Cloud Marketplace. This permits current Google Cloud clients to expedite their buy of Astro and use their current Google Cloud commit credit.
“We’ve simply signed an superior partnership with IBM,” Byron informed VentureBeat. “They’re placing us into their broader knowledge portfolio of merchandise. And we expect there’s an superior alternative for us, not solely in North America, however internationally, to get a whole lot of momentum with IBM as nicely.”
Unified DataOps: The subsequent evolution in enterprise knowledge administration
Salem believes Astronomer is positioned to redefine enterprise knowledge operations, transferring past orchestration to what the corporate calls “unified DataOps” — a complete method integrating observability, high quality administration, and governance right into a single platform.
“We invested in Astronomer in 2019 with a easy wager: Airflow would turn out to be the usual for knowledge orchestration,” Salem mentioned. “At present, it runs at over 80,000 firms and drives 30 million downloads a month. We backed Astronomer as a result of they’re not solely driving that wave; they’re constructing the enterprise management airplane on prime of it.”
For enterprises struggling to appreciate worth from their AI investments, Astronomer’s development indicators a vital shift in how knowledge infrastructure is constructed and managed — one the place orchestration serves as the inspiration for your complete knowledge stack.
“As AI raises the stakes for dependable, scalable knowledge infrastructure, we’re doubling down on our funding,” Salem concluded. “Orchestration is simply the beginning. The crew at Astronomer are poised to unify your complete DataOps stack.”
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