บริษัท Next Palantir กำลังถูกสร้างขึ้นแล้ว เพียงแต่คุณยังไม่สามารถซื้อได้: 3 บริษัท AI ที่น่าจับตามองในปี 2026

While Palantir Technologies (PLTR) trades near all-time highs at premium multiples, a new generation of enterprise AI platforms is scaling rapidly, and may offer investors a more attractive entry point into the same trillion-dollar opportunity.
Palantir just delivered what many investors viewed as one of the strongest recent earnings reports in enterprise software. In Q4 2025, the company posted 70% year-over-year revenue growth, with U.S. commercial revenue surging 137% and total contract value reaching approximately $4.3 billion. Management issued full-year 2026 guidance of roughly 61% revenue growth, implying about $7.2 billion in revenue. CEO Alex Karp described the company as "an n of 1."
He may be right. But the market has priced in that story aggressively.
At current prices near $152 per share, Palantir trades at approximately 45x forward revenue based on 2026 guidance, and roughly 73x trailing 2025 revenue, a multiple that leaves limited margin for error and demands sustained execution across multiple years. For investors who missed the Palantir trade, or who want more favorable risk-adjusted exposure to enterprise AI, the question becomes: which companies are building the next Palantir?
We identified three private companies combining Palantir-like ambitions with valuations that may not yet fully reflect their long-term potential. None is publicly traded today, but each represents a distinct bet on who will control enterprise AI infrastructure over the next decade.
WHAT MAKES A "NEXT PALANTIR"?
Palantir's moat rests on three pillars: deeply embedded enterprise software that is difficult to replace, a government and defense franchise with high barriers to entry, and an AI platform that transforms data into operational decision-making. The companies below attack different parts of this equation. None is a direct replica of Palantir, but each is building a durable, high-margin position within the same enterprise AI ecosystem.
"The question for investors is not whether enterprise AI is real, it is. The question is whether Palantir at current multiples is the most efficient way to own that trend."
Founding in 2013 by the original creators of Apache Spark at UC Berkeley, Databricks built the data lakehouse category from scratch and now provides core data and AI infrastructure for a significant portion of large enterprises, including a majority of the Fortune 500.
Annualized Revenue: Reported at over $5B | YoY Growth: Reported at 65%+ | Subscription Gross Margin: Reported above 80%
Databricks is arguably the most compelling pre-IPO AI infrastructure story of 2026. The company has surpassed a $5 billion annualized revenue run rate while maintaining strong growth, high subscription gross margins, and positive free cash flow. By comparison, Palantir grew 56% in 2025 and is guiding approximately 61% growth in 2026. Databricks is operating at comparable or faster growth rates, at a larger private-market scale, and has not yet entered public markets.
The company recently raised a significant funding round with participation from major institutional investors including Microsoft, BlackRock, Blackstone, JPMorgan, Goldman Sachs, and the Qatar Investment Authority. Reported valuations exceed $100 billion, with some estimates placing it above $130 billion. CEO Ali Ghodsi has stated that an IPO in 2026 is not ruled out, though no filing has been made as of March 2026.
The Palantir comparison: Palantir sits at the decision layer, helping organizations act on data. Databricks sits beneath it, owning the data layer itself. With over 20,000 customers and rapidly expanding AI-driven revenue, the company is positioning itself as foundational infrastructure for enterprise AI. Its continued expansion into databases and AI-native tooling puts it in more direct competition with legacy platforms like Oracle and SAP.
Bull Case: Growth rates comparable to or exceeding Palantir, at a significantly lower implied multiple. A public listing could reprice the entire enterprise AI infrastructure category.
Key Risks: Pre-IPO access is limited to accredited investors. Competition from Snowflake, Google BigQuery, and AWS remains intense. Leadership changes, including the departure of key AI executives, introduce some uncertainty heading into a potential IPO year.
Bottom Line: Public market investors can gain indirect exposure through Microsoft (MSFT), which participated in the latest funding round. Databricks is widely viewed as one of the most anticipated IPO candidates in enterprise software.
#2 GLEAN Private | Series F | Valuation: Industry estimates suggest approximately $7B+
Founding in 2019 by Arvind Jain, a former Google Distinguished Engineer and co-founder of Rubrik, Glean addresses a persistent enterprise problem: employees spend significant time searching for information that already exists internally. Glean connects data across enterprise applications into a unified, permissions-aware knowledge layer, allowing employees to query company information using natural language.
ARR: Reportedly surpassed $200M | Growth: Approximately doubled within the past year
Glean has stated it crossed $200 million in annual recurring revenue in early 2026, roughly nine months after reaching $100 million. A recent funding round reportedly led by Wellington Management at a valuation estimated above $7 billion drew participation from Sequoia, Kleiner Perkins, and General Catalyst. The company has been recognized by industry analysts for innovation in agentic AI and cited by Bloomberg among notable AI startups to watch in 2026.
The Palantir comparison: Palantir focuses on high-level operational decision-making, typically within government and large enterprise. Glean targets a broader layer, every knowledge worker within an organization, embedding intelligence into everyday workflows across industries. The total addressable market may be larger and the deployment friction is considerably lower.
Glean's customer base has expanded beyond technology into finance, retail, manufacturing, and healthcare, sectors that map closely to the professional demographics of this readership. Bull Case: Approximately 2x revenue growth within a year places Glean among the faster-growing enterprise SaaS companies at this stage. Its architecture, built around permissions, compliance, and enterprise data integration, aligns well with the shift toward agentic AI systems.
Key Risks: Microsoft 365 Copilot, Amazon Q, and Google Agentspace are targeting the same use cases with bundled pricing and the significant advantage of existing enterprise relationships. Middleware businesses have historically faced margin pressure when hyperscalers move into adjacent markets.
Bottom Line: At an estimated valuation above $7 billion on reportedly over $200 million in ARR, Glean is not inexpensive, but the multiple is arguably more defensible than Palantir's given the pace of growth. A future public offering would likely depend on continued expansion toward several hundred million in ARR.
#3 SCALE AI Private | Meta-Backed | Valuation: Reportedly approximately $29B
Founding in 2016 by Alexandr Wang, who dropped out of MIT at 19, Scale AI became a key player in the AI ecosystem by providing high-quality training data used to develop machine learning models, recruiting and managing contractors worldwide to label and quality-check the data that teaches AI systems how to think.
2024 Revenue: Reportedly approaching $1B | Government Contracts: Reportedly exceeding $300M in active DoD engagements
In mid-2025, Meta Platforms made a major strategic investment in Scale AI, reportedly acquiring a substantial non-voting stake and valuing the company at approximately $29 billion. Following the transaction, founder Wang transitioned to a role at Meta focused on AI strategy. Reports subsequently emerged suggesting that several major commercial customers reevaluated their relationships with Scale, citing concerns that may have included data governance and competitive considerations, though the motivations behind individual decisions have not been uniformly confirmed. The company also undertook a workforce reduction during this period, according to published reports.
The Palantir comparison is strategic rather than operational. Palantir operates at the decision layer. Scale AI operates at the training data layer, the foundational input that powers AI systems. As demand for high-quality, human-annotated data increases, this layer could become strategically critical. Scale's involvement in U.S. defense-related AI programs, including reported DoD engagements valued above $300 million in aggregate, places it in adjacent competitive territory to Palantir's government franchise.
Company representatives told CNBC in late 2025 that its data business grew on a monthly basis following the Meta transaction, and that its applications business showed meaningful acceleration in the second half of 2025 relative to the first half. In early 2026, Scale launched a new research division focused on agentic AI systems and robotics.
Bull Case: A structurally important position in the AI training data supply chain that is difficult to replicate. Government demand is increasing. The long-term scarcity of high-quality expert-annotated data may strengthen competitive advantages over time.
Key Risks: Reports of reduced engagement from several major commercial customers represent a meaningful revenue concentration risk. Leadership transition following Wang's move to Meta introduces continuity questions. Regulatory bodies in certain jurisdictions have reportedly initiated reviews related to the Meta transaction, though outcomes remain uncertain. No IPO timeline has been announced.
Bottom Line: Scale AI represents a high-risk, high-upside position on the long-term importance of proprietary training data in AI. The events of 2025 introduced real uncertainty into a business that had previously shown exceptional commercial momentum. Public market investors may consider Meta (NASDAQ: META) as a vehicle for indirect exposure.
THE BOTTOM LINE
Palantir is a genuinely exceptional business. But at premium revenue multiples, it is pricing in a high degree of sustained execution over the next decade. Databricks offers the most compelling large-scale pre-IPO infrastructure play. Glean represents a fast-growing bet on enterprise AI adoption at the workflow level. And Scale AI is a more complex but potentially critical player in the AI training data supply chain.
None is a direct substitute for Palantir, but together they reflect the broader question facing investors after Palantir's breakout performance: is there a more efficient way to own the enterprise AI opportunity?
Disclosure: This article is for informational purposes only and does not constitute investment advice. Always conduct your own due diligence before making investment decisions. Past performance is not indicative of future results. ________________________________________________________________________________________
Kirsten Co, MS, MBA, is the CEO of K&Company, where she works with AI startups to land and retain enterprise customers. With 15 years across enterprise sales, business development, and operations in the US, Asia Pacific, and Europe, and a Master's in Global Security and Cybercrime from NYU, she contributes to Insider Monkey covering enterprise AI adoption, go-to-market strategy, and private AI companies worth watching for investors.