Haber vs Celonis

Celonis has a disclosed valuation of $13B, while Haber's valuation is not publicly available, making a direct size comparison difficult. Celonis employs 1000+ people.

Celonis has raised more in total funding at $2B, compared to Haber's $35M — a gap of $2B. Combined, the two companies have completed 6 known funding rounds.

Celonis leads with an Awaira Score of 90/100, while Haber sits at 60/100. That 30-point gap reflects real differences in funding, scale, and traction — it's not a vanity metric.

Haber was founded by Prateek Singhal in 2016. Celonis was founded by Alexander Rinke in 2011. Visit each company's profile on Awaira for a full founder biography.

Haber: Haber is an industrial water management AI company that combines IoT sensors, chemical dosing systems, and AI analytics to optimize water treatment, recycling, and consumption in industrial facilities including manufacturing plants, textile factories, hotels, and commercial buildings. The platform continuously monitors water quality parameters and automatically adjusts treatment processes to minimize chemical consumption, energy use, and water waste while maintaining regulatory compliance.\n\nThe company raised approximately $35M in Series B funding from investors including Tata Capital and Mistletoe, and has deployed its water intelligence systems at hundreds of industrial facilities across India, Southeast Asia, and Japan. Haber's autonomous water management approach has demonstrated measurable reductions in water consumption and treatment chemical costs at customer sites.\n\nWater scarcity is one of the most significant industrial and environmental challenges facing India and the broader Asian region, with industrial water consumption under increasing regulatory and social pressure. Haber's AI-driven approach to industrial water efficiency addresses a compliance and cost imperative for manufacturers that cannot reduce output but must demonstrably improve water stewardship. Celonis: Celonis builds a process mining and execution management platform that uses machine learning to extract, visualise, and optimise business processes from event log data in enterprise systems including SAP, Oracle, Salesforce, and ServiceNow. The Munich company platform connects to enterprise software transaction records, reconstructs actual business process flows from data, identifies inefficiencies and compliance deviations, and provides AI-powered recommendations for process improvement.\n\nThe company raised approximately $1.4 billion including a $1 billion Series D round in 2021 from investors including Arena Holdings, Durable Capital, and T. Rowe Price, valuing it at $13 billion and making it one of Germany most valuable private technology companies. Celonis counts over 1,000 enterprise clients including Siemens, Uber, 3M, and Vodafone, with large deployments across order-to-cash, procure-to-pay, and accounts payable processes generating measurable working capital improvements and operational cost reductions.\n\nCelonis created the process mining software category and competes against SAP Signavio, IBM Process Mining, and Minit in a market it largely invented. The company has expanded from analytical process mining tools into execution management, embedding AI recommendations directly into enterprise workflows rather than producing reports for human review. This execution layer differentiates Celonis from pure analytics vendors and creates a stickier product that becomes embedded in operational processes rather than consumed as a standalone analysis tool.

Celonis got there first, launching in 2011 — that's 5 years of extra runway. Haber didn't arrive until 2016. In AI, that kind of head start means more training data, deeper customer relationships, and a bigger talent moat.

Haber has about 100-500 employees; Celonis has about 1000+. A bigger team usually means more revenue or heavier VC backing, but in AI, small teams can build at massive scale.

Yes — they're direct rivals. Both Haber and Celonis compete in Enterprise AI, targeting many of the same buyers. If you're evaluating one, you should be looking at the other.