Databricks is putting more weight behind Singapore as large companies across Asia shift from experimenting with artificial intelligence to trying to run it safely inside their core operations.
The US data and AI company said it will invest more than US$350 million in Singapore over the next three years, expand into a new 32,000-square-foot regional headquarters, and grow its local workforce from about 250 people to more than 500.
The new office at IOI Central Boulevard Towers will quadruple Databricks’s current Singapore footprint and serve as its Asia Pacific and Japan hub.
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The move comes at a moment when AI adoption in Southeast Asia is entering a more difficult phase. Over the past two years, banks, telcos, insurers, logistics firms and government agencies have tested generative AI through pilots, chatbots and internal productivity tools. The harder question now is whether these systems can be trusted with sensitive enterprise data, regulatory scrutiny and real business workflows.
That is the gap Databricks wants to occupy.
“Organisations across the region are moving quickly from AI pilots to production, but doing so successfully requires trusted data and context, strong governance, and control over models and costs,” said Simon Davies, SVP and GM of Databricks Asia Pacific and Japan.
Why Singapore matters
Singapore has long positioned itself as a regional command centre for enterprise technology, helped by its concentration of banks, multinational headquarters, government-backed digital infrastructure and deep pool of technical talent. Its National AI Strategy has also made AI a policy priority, with an emphasis on responsible deployment rather than unfettered experimentation.
For global software companies, that combination is useful. Singapore is small enough to test with government and regulated industries, but connected enough to influence technology buying decisions across Southeast Asia, India, Japan, Australia and the wider Asia Pacific region.
Databricks’s investment reflects that role. The company said the expanded headquarters will include training and collaboration facilities for customers, partners and learners to develop data and AI skills, test use cases, and move projects into production.
That focus on skills is not incidental. Across Southeast Asia, many companies still struggle with fragmented data, legacy systems and a shortage of engineers who understand both data infrastructure and AI deployment. The excitement around generative AI has often run ahead of the readiness of internal systems.
A chatbot is relatively easy to launch. A governed AI agent that can retrieve the right internal information, take action, respect permissions, and operate within budget is much harder.
From data lakehouse to AI agents
Databricks built its business around the “lakehouse” idea, which combines elements of data lakes and data warehouses so companies can store, manage and analyse large volumes of data in one place. That foundation has become more important as businesses try to build AI tools on top of their own data rather than rely only on public models.
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The company is now pushing a set of products aimed at what it sees as the next phase of enterprise AI. Lakebase, its serverless Postgres database, is designed to provide a fast and secure operational database layer for AI agents. Genie acts as an AI coworker that helps users query business data and get answers grounded in enterprise context. Unity Gateway provides governance, model routing and cost controls across different AI models, tools and agents.
Put simply, Databricks is betting that enterprises will not rely on a single AI model or vendor. Instead, they will need systems that let them choose between models, control access to data, monitor usage, and avoid runaway computing costs.
That message is likely to resonate in sectors such as financial services and telecommunications, where Southeast Asia has some of its most aggressive AI adopters but also some of its strictest compliance requirements. A regional bank, for example, may want AI systems to help with fraud detection, customer service or wealth management, but it must also ensure that customer data is protected, outputs are explainable, and regulators can audit what happened.
Customers in regulated sectors
Databricks said its customer base in the region now includes iFAST Corporation, Singapore Customs and Singtel. They join other organisations using its platform, including Airwallex, CPF Board, GovTech Singapore, LG Electronics, Standard Chartered and Toyota.
The mix is notable because it spans both private and public sector users. In Singapore, government agencies have been active in adopting data platforms and AI tools, but public-sector deployments typically require a higher bar for governance, security and accountability. Winning such customers can help enterprise software companies build credibility in other regulated markets in the region.
Singapore Customs, for instance, operates in an area where data quality, cross-border coordination and risk detection are central. Telcos such as Singtel sit on vast network and customer datasets, which can support everything from service optimisation to fraud prevention. Financial platforms such as iFAST need to balance personalisation and automation with compliance.
These are not the low-stakes use cases that defined the first wave of generative AI trials. They are closer to the infrastructure layer of the economy.
A crowded enterprise AI race
Databricks is not alone in chasing this opportunity. Its closest global rival is Snowflake, which has been expanding from cloud data warehousing into AI and application development. The large cloud providers are also formidable competitors: Microsoft is bundling Fabric, Azure AI and OpenAI services into its enterprise stack; Google Cloud combines BigQuery with Vertex AI; and AWS offers a broad set of data and machine learning tools through services such as Redshift, Bedrock and SageMaker.
In Southeast Asia, this rivalry is intensified by the fact that many large enterprises already buy from multiple cloud vendors. Rather than replacing existing systems outright, Databricks will often need to fit into hybrid environments where CIOs are trying to avoid lock-in while still moving fast on AI. Its pitch around openness, governance and model choice is aimed squarely at that concern.
The company’s global scale gives it a strong starting point. Databricks says more than 20,000 organisations worldwide use its platform, including 70 per cent of the Fortune 500. But Southeast Asia is not a simple copy of the US or Europe. Markets differ sharply in cloud maturity, data regulation, talent availability and AI readiness.
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That makes Singapore a logical base, but not the whole story. The bigger test will be whether Databricks can use its expanded presence there to support customers across more complex regional markets, from Indonesia and Thailand to Vietnam, Malaysia and the Philippines.
Its US$350 million commitment suggests the company expects enterprise AI spending in Asia to deepen, not fade, after the initial hype cycle. The bet is that companies will move from asking what generative AI can do to asking how they can run it reliably, securely and affordably.
For Southeast Asian enterprises, that second question is where the real work begins.
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