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 via higher administration of their knowledge pipelines.
The funding spherical was led by Bain Capital Ventures, with participation from Salesforce Ventures and present buyers together with Perception, Meritech, and Venrock. Bosch Ventures can be searching for to take part within the spherical, reflecting industrial curiosity within the know-how.
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 broaden its international footprint, notably in Europe, Australia, and New Zealand.
“For us, this is just a step along the way,” Byron mentioned. “We want to build something awesome here. I couldn’t be more excited about our venture partners, our customers, our product vision, which I think is super strong in going after collapsing the data ops market.”
How knowledge orchestration turned the hidden key to enterprise AI success
The funding targets what business analysts have recognized because the “AI implementation gap” — the numerous technical and organizational hurdles that stop corporations from deploying AI at scale. Knowledge orchestration, the method of automating and coordinating advanced knowledge workflows throughout disparate techniques, has turn out to be a vital part of profitable AI deployments.
Enrique Salem, Associate at Bain Capital Ventures, defined the crucial challenges going through enterprises at the moment: “Every company operates a sprawling, fragmented data ecosystem—using a patchworks of tools, teams, and workflows that struggle to deliver reliable insights, creating operational bottlenecks and limiting agility. At the heart of this complexity is orchestration—the layer that coordinates all these moving pieces.”
Salem famous that regardless of its significance, “today’s orchestration landscape is where cloud infrastructure was 15 years ago: mission critical, yet fragmented, brittle and often built in-house with limited scalability. Data engineers spend more time maintaining pipelines than driving innovation. Without robust orchestration, data is unreliable, agility is lost, and businesses fall behind.”
The corporate’s platform, Astro, is constructed on Apache Airflow, an open-source framework that has seen explosive development. In accordance with 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 instances in 2024 alone — greater than all earlier years mixed.
“Airflow has established itself as the proven de facto standard for data pipeline orchestration,” Astronomer CMO Mark Wheeler defined. “When we look at the competitive landscape in the orchestration layer, Airflow has clearly emerged as the standard solution for moving modern data efficiently from source to destination.”
From invisible plumbing to enterprise AI spine: The evolution of knowledge infrastructure
Astronomer’s development displays a transformative shift in how enterprises view knowledge orchestration — from hidden backend infrastructure to mission-critical know-how that allows AI initiatives and drives enterprise worth.
“BCV’s belief in Astronomer goes way back. We invested in the company’s seed round in 2019 and have supported the company over the years, now culminating in leading their Series D,” Salem mentioned. “Beyond the impressive growth, Astronomer’s data orchestration has become even more important in the age of AI, which requires scalable orchestration and model deployment automation amidst a ballooning sea of data tools that don’t talk to each other.”
In accordance with the corporate’s inner knowledge, 69% of consumers 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 its annual recurring income and boasts a 130% internet income retention price, indicating robust buyer enlargement.
“While market analysts may be looking for a clear winner in the cloud data platforms battle, enterprises have clearly chosen a multi-solution strategy—just like they earlier determined that multi-cloud would far outpace standardization on any single cloud provider,” Wheeler defined. “Leading enterprises refuse to lock into a single vendor, opting for multi-cloud and diverse data platform approaches to stay agile and take advantage of the latest innovations.”
Inside Ford’s large AI operation: How petabytes of weekly knowledge energy next-generation automobiles
Main enterprises are already leveraging Astronomer’s platform for stylish AI use circumstances that might be difficult to implement with out strong orchestration.
At Ford Motor Firm, Astronomer’s platform powers the corporate’s Superior Driver Help Programs (ADAS) and its multi-million greenback “Mach1ML” machine studying operations platform.
The automotive large processes a couple of petabyte of knowledge 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 the whole lot from autonomous driving techniques 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 reviews 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. In accordance with Astronomer’s analysis, organizations that set up robust knowledge orchestration foundations are extra profitable at operationalizing AI.
“As more enterprises are running ML workflows and real-time AI pipelines, they require scalable orchestration and model deployment automation,” Salem defined. “Astronomer delivers on this today, and as the orchestrator, is the one system that sees everything happening across the stack — when data moves, when transformations run, when models are trained.”
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 somewhat than simply inner analytics.
This pattern is obvious throughout industries, from automotive to authorized know-how corporations which can 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 techniques from prototypes to manufacturing environments serving 1000’s of customers.
Strategic know-how enlargement: Airflow 3.0 and cloud partnerships place Astronomer for market management
The corporate not too long ago introduced the final availability of Airflow 3.0, which it describes as “the most significant release in Airflow’s history.” The replace introduces a number of transformative capabilities designed particularly for AI workloads, together with the flexibility to run duties “anywhere, any time, in any language.”
“Airflow 3.0 lays the foundation for executing tasks on any machine, on-prem or in the cloud, triggered by events across the data ecosystem,” Byron defined. “It also introduces a proof of concept for defining tasks in languages beyond Python, greatly improving data team agility and facilitating migration from legacy systems to Airflow.”
Astronomer has additionally expanded its business partnerships, not too long ago reaching the Google Cloud Prepared – BigQuery Designation, making its platform out there for buy immediately from the Google Cloud Market. This enables present Google Cloud clients to expedite their buy of Astro and use their present Google Cloud commit credit.
“We’ve just signed an awesome partnership with IBM,” Byron informed VentureBeat. “They’re putting us into their broader data portfolio of products. And we think there’s an awesome opportunity for us, not only in North America, but internationally, to get a lot of momentum with IBM as well.”
Unified DataOps: The following 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 simple bet: Airflow would become the standard for data orchestration,” Salem mentioned. “Today, it runs at over 80,000 companies and drives 30 million downloads a month. We backed Astronomer because they’re not only riding that wave; they’re building the enterprise control plane on top of it.”
For enterprises struggling to understand worth from their AI investments, Astronomer’s development alerts 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 reliable, scalable data infrastructure, we’re doubling down on our investment,” Salem concluded. “Orchestration is just the start. The team at Astronomer are poised to unify the entire DataOps stack.”
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