Salesforce's $8 Billion Informatica Bid Is Dead — So Why Did It Just Buy Own Company for $1.9 Billion Instead?

The Deal That Wasn't — And the One That Was

Salesforce spent the better part of 2024 in on-again, off-again talks to acquire Informatica, the data-management giant, for a reported $8 billion. The deal collapsed in May 2024 over price disagreements. Then, in September 2024, Salesforce quietly announced it would acquire Own Company — formerly known as OwnBackup — for $1.9 billion in cash. Same strategic anxiety, very different prescription.

The contrast is instructive. Informatica would have been a sprawling, expensive bet on becoming an enterprise data platform. Own Company is a focused, surgical move: a SaaS data protection and management firm that already has deep hooks inside Salesforce's own ecosystem. The price tag is less than a quarter of the Informatica ask. The integration risk is a fraction. And the strategic fit, for where Salesforce is actually going with its Agentforce AI platform, is arguably tighter.

This is the story of why Salesforce's biggest failed deal of 2024 may have done the company a favor — and what the Own Company acquisition reveals about the real infrastructure battle underneath the AI hype.

What Own Company Actually Does

Own Company↗ provides data backup, archiving, seeding, and recovery services for SaaS platforms — primarily Salesforce, but also Microsoft Dynamics 365, ServiceNow, and others. It protects against accidental deletion, ransomware, and compliance failures. The company serves more than 7,000 customers and has processed over 600 billion records to date.

That sounds unglamorous. It is, in the best possible way. Backup and recovery is the kind of infrastructure nobody talks about until something goes catastrophically wrong — and then it's all anyone talks about. But Own Company's pitch has evolved well beyond "we save your data when you mess up."

The company has been building toward what it calls data activation: the ability to not just protect data but make it usable for development, testing, and increasingly, AI model training and fine-tuning within enterprise environments. That last piece is what caught Salesforce's attention.

"The combination of Salesforce and Own will help our customers unlock the full value of their data across every part of their business." — Marc Benioff, Salesforce CEO, in the acquisition announcement↗

Benioff's quote is deliberately vague, as these things always are. But read between the lines: Salesforce needs clean, accessible, governed customer data to make Agentforce work. Own Company is part of the plumbing.

Agentforce Is the Context for Everything

You cannot understand this deal without understanding Agentforce, Salesforce's autonomous AI agent platform launched at Dreamforce 2024 in September. Agentforce is Salesforce's answer to the question every enterprise software company is scrambling to answer: how do you go from "AI assistant" to "AI that actually does things?"

The platform allows companies to deploy AI agents that can autonomously handle customer service tickets, qualify sales leads, process orders, and manage workflows — all grounded in a company's own CRM data via Salesforce's Data Cloud↗. The pitch is compelling. The execution challenge is enormous.

The core problem with autonomous AI agents in enterprise settings isn't the model intelligence — it's the data quality and governance. An agent that acts on stale, incomplete, or improperly permissioned data doesn't just give bad answers; it takes bad actions. In a customer service context, that means wrong refunds. In a sales context, that means contacting prospects at the wrong stage. In a compliance context, that means regulatory exposure.

Own Company's data protection and archiving capabilities directly address this. If Salesforce can offer customers a unified layer that backs up their Salesforce data, ensures its integrity, seeds clean data into development and testing environments, and ultimately feeds governed data into Agentforce agents — that's a genuinely differentiated stack.

The Data Cloud Connection

Salesforce's Data Cloud↗ — formerly Genie — is the real-time data platform that's supposed to unify customer data across sources and power AI applications. As of Dreamforce 2024, Salesforce reported Data Cloud had crossed 1 trillion records processed and was growing at over 130% year-over-year.

Own Company's data archive and recovery capabilities complement Data Cloud directly. Think of it this way: Data Cloud is the engine that ingests and activates data in real time. Own Company is the system that ensures that data is protected, recoverable, and audit-ready. Together, they close a loop that Salesforce's enterprise customers — particularly in regulated industries like financial services and healthcare — have been demanding.

Why Informatica Would Have Been the Wrong Move

Let's revisit the road not taken. Informatica is a legitimate enterprise data management powerhouse. Its Intelligent Data Management Cloud (IDMC)↗ covers data integration, data quality, master data management, and governance at scale. The company had a market cap around $11 billion at the peak of deal speculation.

The strategic logic was obvious: Salesforce wants to be the enterprise AI platform, and to do that it needs to handle data that lives outside Salesforce's own walls — in SAP, Oracle, legacy databases, data warehouses. Informatica would have given it that.

The problems were equally obvious:

• Price: $8 billion for a company growing at roughly 5-7% annually is a tough multiple to justify to shareholders already skeptical of Salesforce's M&A discipline after the $27.7 billion Slack acquisition in 2021.
• Integration complexity: Informatica's customer base overlaps with Salesforce's, but its product architecture is deeply enterprise-on-premises in ways that would have taken years to cloud-native-ify.
• Activist pressure: Salesforce has been under sustained pressure from activist investors including Elliott Management and Starboard Value to prioritize margins over growth-by-acquisition. A mega-deal would have been a political nightmare internally.
• Regulatory scrutiny: An $8 billion data-management acquisition by a dominant CRM vendor would have drawn serious attention from the FTC, particularly in the current antitrust environment.

Own Company sidesteps every one of these landmines. It's a $1.9 billion deal — digestible. It's already Salesforce-native — minimal integration friction. It's operationally focused rather than platform-competitive — no antitrust story. And it strengthens the AI narrative without requiring Salesforce to explain why it's spending Slack-sized money again.

"Salesforce has learned from the Slack experience. The Own acquisition is about filling a specific gap in the AI data stack, not buying a new product category and hoping it works out." — Brent Leary, CRM industry analyst and founder of CRM Essentials↗

Market Structure: Who Else Is Playing This Game

Salesforce isn't the only enterprise AI platform scrambling to lock up data infrastructure. The competitive dynamics here are worth mapping.

Microsoft has been integrating its data protection and governance capabilities directly into Microsoft 365 Copilot and Azure AI, leveraging Purview for compliance and governance. Its advantage: it already owns the productivity layer where most enterprise data lives. Its disadvantage: it's trying to be everything to everyone, and the integration quality is uneven.

ServiceNow — which competes with Salesforce in workflow automation — has been building out its own AI platform, Now Assist, and has made data governance a core part of its enterprise pitch. Own Company's support for ServiceNow data is notable: Salesforce now owns a tool that also protects a key competitor's data, which creates interesting leverage.

Veeva Systems, dominant in life sciences CRM, has been quietly building its own data resilience capabilities rather than relying on third parties — a sign that data protection is becoming table stakes for any serious enterprise platform.

The broader pattern: every major enterprise software platform is realizing that AI differentiation lives in the data layer, not the model layer. The models are increasingly commoditized. The moat is clean, governed, accessible enterprise data.

What Salesforce Paid and What It Got

Let's put the $1.9 billion price in context:

• Own Company's last known valuation was approximately $3.35 billion at its 2021 Series E funding round, when it raised $240 million led by Salesforce Ventures (yes, Salesforce was already an investor).
• The acquisition price represents a significant markdown from that peak — a function of the broader SaaS valuation reset between 2021 and 2024.
• Own Company had raised approximately $500 million in total venture funding across its lifetime per Crunchbase data↗.
• Salesforce is paying roughly 6-8x ARR based on estimates of Own Company's revenue, which is reasonable for a profitable SaaS infrastructure business in the current environment.

For Salesforce, the calculus is straightforward: it gets a proven product with 7,000+ enterprise customers, deep Salesforce-ecosystem integration, a data activation capability that feeds directly into Agentforce, and a team that already knows how to operate inside the Salesforce orbit. The deal closed in Q4 2024.

Key Strategic Assets Salesforce Acquired

• Data Protect: Automated backup and recovery for Salesforce orgs, covering Sales Cloud, Service Cloud, and other core products
• Data Archive: Long-term data retention and compliance archiving, critical for financial services and healthcare customers
• Data Seed: The ability to populate sandbox and development environments with realistic, anonymized production data — directly relevant for AI model testing
• Secure Connect: Data access and integration capabilities that allow enterprises to work with their Salesforce data in external tools without compromising security
• 7,000+ enterprise customer relationships across multiple SaaS platforms

The Regulatory Backdrop

It's worth noting that this deal happened against a backdrop of intensifying scrutiny on enterprise software consolidation. The FTC's ongoing focus on tech M&A↗ and the EU's Digital Markets Act have made large-scale acquisitions increasingly expensive from a regulatory standpoint — not just in cost, but in time and management distraction.

Salesforce's choice of a $1.9 billion deal over an $8 billion one isn't just financial conservatism. It's regulatory pragmatism. Deals below roughly $2 billion rarely trigger the kind of extended second-request reviews that can drag on for 12-18 months and consume executive bandwidth. In a year when Salesforce needs to execute on Agentforce, that matters.

The broader lesson for enterprise AI M&A: the era of the transformative mega-deal is giving way to a period of targeted capability acquisition. Buy what fills a specific gap. Avoid what invites a regulator's call.

What Happens Next

Salesforce has telegraphed its integration roadmap in broad strokes: Own Company's capabilities will be folded into the Salesforce platform and offered as premium add-ons to enterprise customers, particularly those using Data Cloud and Agentforce. Expect Own Company's data seeding and archiving capabilities to show up as native features in Salesforce's AI development tools within the next 12-18 months.

The bigger question is whether Salesforce's AI platform strategy actually works. Agentforce is a bold bet, and the early enterprise customer feedback from Dreamforce 2024 was cautiously positive. But the gap between demo and production deployment for autonomous AI agents is wide, and Salesforce will need every piece of its data infrastructure — including what it just bought from Own Company — to perform reliably at scale.

For now, the $1.9 billion deal looks like one of the smarter moves Salesforce has made in years: focused, fairly priced, strategically coherent, and quietly essential to the AI platform ambitions that Marc Benioff has been selling loudly to Wall Street all year.

The boring infrastructure bet might just be the most important one.