APAC AI Agent Infrastructure: 8 Players 2026
Eight AI agent infrastructure companies in APAC: funding, AI differentiation tiers, data moat analysis, acquirer landscape, and M&A valuation benchmarks.
AI agent infrastructure is the fastest-growing segment of enterprise software M&A in 2025 and 2026. The structural driver is straightforward: corporations across every sector have concluded that AI agent capability is a build-or-buy question, and that the buy path is faster, cheaper, and less risky than building from scratch. The result is that the 8–20 companies building the foundational layers of agentic AI, including the models, the orchestration platforms, and the deployment infrastructure, are now attracting acquisition interest from buyers who had no M&A thesis for this category two years ago.
In Asia Pacific, the landscape is compressed into three geographies: China, which has produced the largest cluster of foundation model and agent platform companies; India, which has built enterprise-grade agent deployment infrastructure serving APAC financial institutions and global enterprises; and Korea, which has produced the most technically credible APAC-native multilingual model optimised for the region’s enterprise stack. Japan and Southeast Asia are primarily acquirer geographies rather than builder geographies in this vertical, with Japanese conglomerates, Korean chaebols, and Singapore sovereign vehicles deploying capital into APAC agent infrastructure.
This analysis covers eight companies that represent the clearest M&A and investment signal in the APAC AI agent infrastructure category: who they are, what their AI actually does at an infrastructure level, where their data moat sits, what acquirers are willing to pay, and what founders should understand about their exit pathway.
Why AI Agent Infrastructure Is the Highest-Leverage M&A Category in 2026
Two structural forces have converged to make agent infrastructure the most active M&A vertical in enterprise AI.
The capability gap between large and small enterprises is becoming an acquisition driver. Large corporations in financial services, healthcare, and manufacturing have discovered that their internal AI teams can deploy foundation models for single-step tasks but cannot architect reliable multi-step agent workflows at enterprise scale. The orchestration layer, context management, tool-calling reliability, and memory architecture required for production-grade agents is a different engineering problem from fine-tuning a model. Companies that have solved that problem at scale have become acquisition targets.
Inference cost pressure is consolidating the market faster than expected. Running AI agents at enterprise scale is expensive. A large financial institution deploying agent-assisted compliance review across millions of documents faces inference costs that make gross margin management a board-level issue, not a product team issue. Infrastructure companies that have built proprietary efficiency layers, including model distillation, speculative decoding, KV cache optimisation, and fine-tuned sub-models for specific vertical tasks, have a defensible cost advantage that compounds over time. That cost advantage is worth acquiring.
According to CB Insights, global investment in AI agent infrastructure exceeded $4.2 billion in 2025, growing from $890 million in 2023. APAC represented approximately $1.8 billion of that figure, driven predominantly by Chinese foundation model rounds and Indian enterprise AI platform investments.
The Comparison: Eight AI Agent Infrastructure Players
The following eight companies were selected based on: genuine AI agent infrastructure capability (not application-layer AI with agent features added), disclosed funding above $50 million or strategic importance to the APAC acquirer landscape, and direct relevance to the M&A buyer universe active in APAC in 2026.
| Company | Country | Founded | Total Funding | Est. Valuation | Sub-vertical |
|---|---|---|---|---|---|
| ByteDance Coze | China | 2023 (product) | Parent ByteDance | N/A (product) | Agent dev platform |
| SuperAGI | India | 2023 | ~$8.5M | Undisclosed | Enterprise AI agents |
| Zhipu AI | China | 2019 | ~$340M | ~$1B+ | Foundation models + enterprise |
| Moonshot AI | China | 2023 | ~$1.1B | ~$3B | Long-context LLMs |
| Minimax | China | 2021 | ~$600M | ~$2.5B | Multimodal foundation models |
| Kore.ai | India/US | 2014 | ~$250M | ~$1B | Enterprise conversational AI |
| 01.AI | China | 2023 | ~$200M | Undisclosed | Yi models + enterprise AI |
| Upstage | South Korea | 2020 | ~$105M | Undisclosed | Solar LLM + APAC enterprise |
AI Differentiation Tier: What Each Company’s Infrastructure Actually Does
The critical M&A question for agent infrastructure is not how much AI a company claims, but whether the infrastructure is the product or a feature layered onto an existing business.
Tier 1: The infrastructure is the product (remove it, the business disappears)
ByteDance Coze is the largest agent development platform in Asia Pacific. Coze allows developers and enterprises to build AI agents by connecting foundation models to tools, APIs, knowledge bases, and memory systems without writing model-level code. As of early 2025, the platform had over 4 million registered developers and was processing hundreds of millions of agent interactions per month. Coze’s differentiation is distribution: it inherited ByteDance’s global developer network, which no independent agent platform in APAC can replicate. The parent company’s infrastructure investment also means Coze agents run on ByteDance’s proprietary model serving infrastructure, giving it a cost structure advantage over platforms running on third-party API providers. Coze is not an acquisition target as a standalone entity, but it establishes the benchmark for what a mature agent platform looks like.
Moonshot AI’s Kimi is structured around one technical bet: that context window scale is the most important dimension of LLM capability for enterprise agent use cases. Kimi was among the first models to offer 128,000-token, then 1 million-token, context windows as a production API. The strategic thesis is that multi-step agent workflows over long documents, multi-session conversations, and full codebase analysis are the enterprise use cases that generate the most value, and that these use cases require context windows that most smaller models cannot support. Moonshot AI has raised approximately $1.1 billion from investors including Alibaba, Xiaomi, and Meituan, at a reported valuation above $3 billion. Its infrastructure moat is not the context window itself (Gemini and Claude have matched it) but the corpus of enterprise workloads processed through Kimi that enables ongoing fine-tuning for the specific document types APAC enterprises use.
Zhipu AI’s GLM series occupies the foundation model layer with a specific enterprise deployment philosophy: open-weight models that enterprises can self-host, combined with a commercial API offering and enterprise integration services. The ChatGLM open-source model has accumulated over 10 million downloads on Hugging Face and domestic model hubs, creating a developer ecosystem that feeds enterprise sales. Zhipu has raised approximately 2.5 billion RMB from Tencent, Alibaba, and Meituan, and its AgentBench deployment framework allows enterprise customers to build multi-agent workflows on top of GLM-4. The infrastructure advantage is the combination of open-weight model distribution (which creates lock-in through fine-tuning and customisation) and a managed API service (which captures enterprise customers who want performance without infrastructure maintenance).
Tier 2: The infrastructure materially transforms what the business offers
Minimax has built a multimodal foundation model infrastructure covering text, voice, and video generation, with each modality serving as a component in agent workflows that require natural interaction beyond text. Its Hailuo AI video generation model is one of the highest-quality in APAC by third-party evaluation benchmarks. Its MiniMax-Text-01 model supports 1 million token context. The differentiation is multimodal completeness: Minimax can power agent interfaces that communicate through voice (call centre agents), generate visual output (AI design workflows), and handle long document processing (enterprise analysis agents) within a single infrastructure layer. Minimax has raised approximately $600 million and is valued at around $2.5 billion.
Kore.ai has spent over a decade building enterprise conversational AI infrastructure for the financial services sector, which is the most demanding enterprise vertical for agent reliability, compliance, and audit requirements. Its platform handles the orchestration of multi-turn conversations, integration with core banking and CRM systems, and compliance logging required by financial regulators across APAC. Over 300 enterprise customers across financial services use Kore.ai infrastructure, including Standard Chartered, Citibank, and HDFC Bank. The company raised a $150 million Series D led by NVIDIA in 2024, bringing total funding to approximately $250 million. The NVIDIA relationship is significant: it signals that NVIDIA views Kore.ai as infrastructure for deploying NVIDIA-powered agentic systems in enterprise, which may inform future strategic or M&A activity.
Tier 3: Agent capability is a layer added to an existing product
SuperAGI is an open-source autonomous AI agent framework that has evolved into a commercial enterprise platform. Its SuperSales product deploys AI agents for outbound sales workflows, with agents autonomously researching prospects, drafting and sending email sequences, scheduling meetings, and updating CRM records. The infrastructure differentiator is reliability at the task execution layer: multi-step autonomous workflows that are commercially reliable enough for enterprise sales teams to deploy without constant human supervision. SuperAGI has raised approximately $8.5 million in early-stage funding, which understates its strategic value relative to the market position it has built through open-source distribution.
01.AI was founded in 2023 by Kai-Fu Lee with the specific thesis that high-quality, openly distributed models would be the foundation for APAC enterprise AI. The Yi model series (Yi-34B, Yi-1.5) has performed competitively on international benchmarks and has gained adoption across enterprise AI deployments in China and among APAC enterprises seeking an alternative to US foundation models. 01.AI has raised approximately $200 million and is backed by Alibaba, NVIDIA, and Sinovation Ventures. The infrastructure contribution is a well-maintained, commercially licensed foundation model with enterprise support, which gives acquirers a ready-made AI backbone without the model training infrastructure required to build equivalent capability internally.
Upstage developed the Solar LLM family specifically for multilingual APAC enterprise use cases, with particular focus on Korean, Japanese, and Southeast Asian language performance in document-heavy enterprise workflows. Solar Mini and Solar Pro models are optimised for financial document analysis, legal document processing, and customer service automation in APAC enterprise contexts. Upstage raised $105 million in a Series B from SoftBank Ventures Asia, DSC Investment, and others in 2024. Its enterprise deployment includes financial institutions in Korea, Japan, and the Middle East, and it maintains an active commercial relationship with several Japanese systems integrators who are using Solar as the embedded model layer in their enterprise AI products.
Data Moat Analysis: What Creates Defensible Value
The conventional framing of data moats in AI asks whether a company has proprietary training data that cannot be replicated. In agent infrastructure, the more relevant question is where the data accumulates and compounds.
Conversation data as a proprietary signal. Enterprise agent platforms that process millions of employee-customer, employee-system, and agent-task interactions accumulate a dataset that no new entrant can replicate. Kore.ai’s decade of financial services conversation data encompasses the specific question types, document structures, regulatory language patterns, and escalation triggers that characterise banking, insurance, and wealth management conversations. A new entrant building a competing platform starts without this corpus. For M&A purposes, this data asset is worth quantifying: the number of unique interaction types processed, the diversity of enterprise contexts covered, and the fine-tuning advantage it creates are all diligence questions that affect valuation.
Long-context processing data. Moonshot AI and Minimax have accumulated enterprise document processing data through their API usage that gives them insight into how APAC enterprises structure long-form documents, what information retrieval patterns their workflows require, and which document types generate the most complex agent tasks. This processing data shapes future model training and enables optimisations that API-only infrastructure cannot replicate without the same volume of enterprise usage.
Multilingual fine-tuning data. Upstage’s Solar LLM has been fine-tuned on multilingual APAC data that creates performance advantages in Korean, Japanese, and Southeast Asian languages that are not easily replicated by models fine-tuned on primarily English corpora. For Japanese and Korean enterprise acquirers, the ability to deploy a model that was explicitly designed for their language stack, rather than one that treats Japanese or Korean as a secondary language after English and Chinese, is a meaningful differentiator.
M&A Deal Log: Precedent Transactions in AI Agent Infrastructure
The following transactions establish valuation and strategic precedent for AI agent infrastructure acquisitions.
| Transaction | Date | Value | Strategic Logic |
|---|---|---|---|
| SAP / WalkMe | 2024 | $1.5B | Enterprise AI adoption agent layer; 9,000+ enterprise customers |
| Salesforce / Tenyx | 2024 | Undisclosed | Conversational AI for customer service agent automation |
| NVIDIA / Run:ai | 2024 | $700M | AI infrastructure management and workload orchestration |
| SoftBank / Perplexity | 2025 | $500M investment | Agentic AI search; Japan market deployment and strategic relationships |
| ServiceNow / multiple AI workflow companies | 2024–25 | Various | Embedding agent automation into enterprise workflow platform |
| Workday / Evisort | 2024 | Undisclosed | AI contract intelligence and agent-assisted document management |
The SAP/WalkMe acquisition at $1.5 billion is the most relevant precedent for enterprise agent platform M&A. WalkMe’s platform directs enterprise users through workflows using digital guidance, overlay agents, and automation triggers, which is structurally similar to the enterprise agent orchestration products that Kore.ai and Upstage are building. The $1.5 billion price reflected approximately 10x revenue on WalkMe’s last reported ARR, with the strategic premium driven by the 9,000+ enterprise customer base that gave SAP an immediate deployment network.
The SoftBank/Perplexity investment is the clearest signal of Japanese strategic interest in agentic AI infrastructure. Masayoshi Son has publicly articulated a thesis that artificial superintelligence will emerge within years, and SoftBank’s capital deployment reflects that thesis. For APAC AI agent infrastructure companies, SoftBank is the most active strategic investor and a plausible acquirer for the right enterprise AI platform.
Acquirer Landscape: Who Is Buying APAC AI Agent Infrastructure
The acquirer universe divides into four groups with distinct strategic logics.
Japanese systems integrators and conglomerates. NTT Data, Fujitsu, NEC, Hitachi, and Recruit Holdings all have enterprise AI integration mandates and active acquisition searches for AI infrastructure companies that can serve their Japanese enterprise customer bases. Their specific requirement is multilingual capability (Japanese-first is non-negotiable), enterprise-grade reliability, and a deployment model compatible with Japan’s generally conservative IT procurement environment. Korean-origin and Indian-origin companies with strong Japanese enterprise track records are preferred targets. Chinese-origin companies face additional scrutiny under Japan’s economic security framework.
Korean conglomerates. Samsung SDS, LG CNS, SK Telecom, and Kakao Corp have all signalled intent to acquire AI infrastructure capability to embed into their enterprise services. Samsung’s acquisition of Oxford Semantic Technologies in 2023 demonstrated willingness to acquire at relatively early stages when the technology is foundational. The strategic logic for Korean conglomerates is similar to Japanese integrators: they need AI infrastructure that performs in Korean-language enterprise contexts, which is a requirement that most US and European AI infrastructure companies do not satisfy well.
Singapore sovereign and strategic investors. Temasek and GIC have invested in multiple APAC AI infrastructure companies as financial investors, with portfolio company M&A creating occasional secondary paths. GovTech Singapore has an active interest in AI infrastructure that can be deployed in government and regulated enterprise contexts. Singtel’s enterprise AI services division is an emerging strategic acquirer for AI platforms that serve the telecommunications and enterprise services verticals.
US enterprise software platforms expanding into APAC. Salesforce, ServiceNow, Workday, and SAP are all expanding their APAC AI agent deployment and have demonstrated willingness to acquire infrastructure companies that accelerate regional capability. Their acquisition thesis prioritises: existing enterprise customer bases in APAC, clean IP with no data localisation complications, and founding teams willing to operate post-acquisition within their product organisation structures.
Valuation Benchmarks by Business Model
AI agent infrastructure valuation varies substantially by business model type, and the appropriate multiple depends on where in the infrastructure stack the company sits.
| Business Model | ARR Multiple | Key Driver | Key Risk |
|---|---|---|---|
| Foundation model API (Chinese, 100M+ context) | 10–20x ARR | Enterprise NRR, context window scale, model benchmark performance | US export control complexity for cross-border acquirers |
| Enterprise agent platform (SaaS, 100+ customers) | 8–15x ARR | NRR above 120%, vertical specialisation, compliance capability | Multi-step reliability at scale; LLM provider dependency |
| Open-source model with commercial tier | 6–12x ARR | Developer ecosystem size, enterprise adoption rate, fine-tuning services | Commoditisation as base models improve; GitHub fork risk |
| Multilingual LLM (APAC-focused) | 6–10x ARR | Language benchmark performance, enterprise deployment references | Language model quality gap narrowing vs US models |
| AI compute infrastructure | 4–8x revenue | GPU cluster utilisation rate, inference efficiency advantage | Commodity compute pricing pressure from hyperscalers |
The highest multiples in the category are commanding by enterprise agent platforms with verified NRR above 120%, which indicates that customers are expanding usage as they deploy additional agent workflows. Kore.ai’s financial services customer base, if its NRR is in this range, would support the upper end of the enterprise agent platform multiple range. Foundation model companies in China are valued at the high end when their enterprise API ARR is growing at 100%+ annually and the model maintains benchmark leadership.
What APAC AI Agent Infrastructure Founders Should Understand About Their Exit
“Daniel Bae of Amafi Advisory observes: ‘The acquirer conversation for APAC AI agent infrastructure has become materially more sophisticated in the past eighteen months. Buyers are no longer asking whether the technology works. They are asking whether it works reliably enough to run in a regulated enterprise environment without daily human intervention, whether the inference cost structure supports viable margins at scale, and whether the founding team’s departure would impair the model’s ongoing improvement. Those three questions determine whether a transaction gets done and at what price.’”
Three preparation priorities for founders considering exits in this category.
Quantify your inference cost per unit of enterprise value delivered. Acquirers are increasingly modelling gross margin trajectories based on current inference cost, expected query volume growth, and the efficiency roadmap that the founding team can credibly execute. A company with strong ARR growth but deteriorating gross margins as agent usage scales is a structurally weaker acquisition than its headline revenue growth rate suggests. Founders who can present a credible inference cost reduction roadmap (quantization, caching, fine-tuned sub-models) have materially stronger negotiating positions.
Document the multilingual enterprise performance data. For APAC acquirers, benchmark performance on English-only evaluation sets is insufficient. Founders should maintain and present performance data on Korean, Japanese, Mandarin, Cantonese, and Southeast Asian language tasks that reflect the document types and interaction patterns of their enterprise customers. This data is often not published in model cards but is available internally, and presenting it proactively shortens due diligence by weeks.
Understand the regulatory exposure profile of your target acquirers. For Chinese AI companies, the acquirer universe for cross-border transactions has narrowed due to US export control restrictions on AI model weights and the economic security frameworks of Japan and Korea. Understanding which acquirers face US FOCI (Foreign Ownership, Control, or Influence) review and which have clean regulatory exposure is critical for structuring the process correctly. Korean and Indian AI infrastructure companies face fewer cross-border regulatory complications and typically have a broader acquirer pool.
Amafi Advisory advises AI company founders and corporate acquirers on M&A, fundraising, and strategic positioning across Asia Pacific. For AI agent infrastructure companies considering exits or acquisitions, our senior team covers the buyer landscape across Japanese conglomerates, Korean chaebols, and US enterprise software platforms. Contact our team to discuss your situation, or read more about our sell-side M&A advisory and buy-side acquisition advisory practices.
Related analysis: For a broader view of agentic AI’s impact on M&A deal workflows, see Agentic AI in M&A: Autonomous Deal Execution. For AI infrastructure valuation context, the Inference Cost and Context Window glossary entries cover the two technical metrics that most directly affect agent infrastructure gross margins and valuation. For the mechanism through which agent infrastructure companies create proprietary capability on top of foundation models, see the Fine-Tuning glossary entry, which covers fine-tuning dataset defensibility and its role in AI acquisition diligence. For the vocabulary of agentic architectures as they appear in enterprise AI due diligence, see the Agentic Workflow glossary entry, covering task completion rates, tool integration depth, and interaction log data moats.
