What are the big 5 AI platforms?

Why "Which AI Platform?" Is Suddenly a Hard Question

A year ago, picking an AI platform meant picking the biggest name and moving on. Today, the numbers tell a different story: enterprise AI spend hit $37B in 2025, a 3.2x jump from the previous year, according to Menlo Ventures' enterprise market analysis. With that much money moving, the stakes of choosing wrong have never been higher.

The market itself is expanding fast. Analysts project the AI platform market will grow from $18.22B in 2025 to $94.31B by 2030, a compound annual growth rate of 38.9%. But growth alone doesn't explain why the decision feels harder. The real culprit is convergence at the top.

Stanford HAI's AI Index Report found that the score gap between the top model and the 10th-ranked model shrank from 11.9% to just 5.4% in a single year — and the top two models are now separated by only 0.7%. When the leaders are nearly indistinguishable on benchmarks, marketing claims and vendor relationships fill the vacuum, leaving buyers to sort through noise.

There's another wrinkle: no official "big 5" list exists. Industry sources name different leaders depending on what they measure. Enterprise LLM API share data puts Anthropic at 40% and OpenAI at 27%, while market-player analyses name Google, Microsoft, and Nvidia among the major platforms. The five names that recur across independent sources — Anthropic, OpenAI, Google, Microsoft, and Nvidia — form the basis of this article's framing, synthesized from market data rather than an official ranking.

Why does this matter to your business? Because 76% of enterprise AI solutions are now purchased rather than built internally, up from a near-even split in 2024. You're not evaluating platforms as a researcher; you're committing budget to one. And the decision is compounding: as OpenAI's chief economist notes, work is shifting "from asking models for outputs to delegating complex, multi-step workflows" — meaning the platform you choose becomes the foundation your agents and automations build on.

A few factors make the choice genuinely confusing right now:

• Different leaders dominate different measures — API share, seat counts, and copilot adoption each crown a different winner.
• Copilots hold 86% of horizontal AI spend, led by ChatGPT Enterprise, Claude for Work, and Microsoft Copilot, so "platform" now means very different things.
• Agent capabilities are evolving faster than governance — only one in five companies has a mature governance model for autonomous AI agents, per Deloitte's enterprise AI survey.
• Whichever platform you pick, its real power comes from the extensions and skills you connect to it — and those vary wildly in quality and safety.

That last point is where the practical leverage lies. Once you've chosen a platform, the question becomes what to attach to it. SkillzBench is building a free, independent directory that benchmarks agent skills — showing each skill's Lift in percentage points from sandbox testing, with security scanning and version pinning — so you can verify what actually helps before it touches your agents. The platform choice is hard; equipping it doesn't have to be.

The Big 5: Anthropic, OpenAI, Google, Microsoft, and Nvidia

No industry body publishes an official "big 5" ranking, but the same five names keep appearing across enterprise market data: Anthropic, OpenAI, Google, Microsoft, and Nvidia. Here's what the evidence actually says about each — and where the numbers come from.

Anthropic has quietly become the enterprise favorite. According to Menlo Ventures' enterprise survey, it commands 40% of enterprise LLM API market share, ahead of OpenAI's 27%. Forbes' AI 50 coverage pegs its valuation at $380B, with $60B in funding and $4.5B in revenue last year source.

OpenAI, meanwhile, leads on raw scale. Its own State of Enterprise AI report counts 1M+ business customers and 7M+ ChatGPT workplace seats, with Enterprise seats up roughly 9x year over year. Note the caveat: these are different metrics (API share vs. seat count), and both sources are vendor- or investor-adjacent.

Google and Microsoft anchor the rest of the model and platform layer:

• Google — named among leading model API providers alongside Anthropic and OpenAI, and a major AI platform market player (https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/; https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-ai-platform-market-113162926.html).
• Microsoft — cited as a major market player pursuing organic and inorganic strategies, with Microsoft Copilot among the copilots leading horizontal AI at 86% share ($7.2B) source.
• Nvidia — named as a major market player, and effectively the infrastructure backbone supplying compute to the other four source.

The market these five compete in is expanding fast: enterprise AI spend hit $37B in 2025, a 3.2x jump from $11.5B in 2024 source. And the frontier is tightening — the gap between the top and 10th-ranked models fell from 11.9% to 5.4% in a single year source.

Whichever platform you choose, agents are where the work happens — and every tool an agent can access expands both its capability and its exposure, as the Frontier Model Forum warns source. That's why SkillzBench is building a free directory and MCP connector that lets you browse, verify, and benchmark agent skills across any of these platforms, so you can see which ones actually help before you deploy them.

How to Choose: What the Data Says About Buying vs. Building

Most teams no longer ask if they should build an AI solution from scratch. The data shows a decisive shift toward procurement, with 76% of enterprise AI solutions now purchased rather than built, up significantly from the previous year’s split. This trend reflects a broader market reality where speed and reliability outweigh the allure of custom development.

Enterprise AI spend surged to $37 billion in 2025, marking a 3.2x year-over-year increase. Within this landscape, specific use cases drive the majority of investment. Coding has emerged as the primary killer app, accounting for $4 billion in departmental spend. Meanwhile, horizontal copilots dominate the general assistant category, capturing 86% of that segment’s $7.2 billion market.

Understanding where your specific needs fit within these categories is crucial for selecting the right platform. The choice often depends on whether you require deep vertical integration or broad horizontal capability.

• Coding and Development: If your primary goal is accelerating software engineering, prioritize platforms with strong developer ecosystems. Anthropic and OpenAI lead this space, with tools that integrate directly into IDEs and version control systems.
• Horizontal Copilots: For general productivity across marketing, sales, and support, look at the big three: ChatGPT Enterprise, Claude for Work, and Microsoft Copilot. These solutions offer the broadest utility but require careful data governance.
• Agentic Workflows: If you are delegating complex, multi-step tasks, you need an agent-capable platform. This is where the gap in governance becomes critical, as only 20% of companies currently have mature models for autonomous agents.

The rise of agentic AI introduces new security considerations that traditional software purchases do not face. The Frontier Model Forum notes that every tool an agent can access expands both its capability and its exposure to risk. As enterprises move from asking models for outputs to delegating workflows, the supply chain of these agents becomes a critical attack vector.

This is where the concept of verified agent skills becomes essential. As you evaluate platforms like Claude, Codex, or Gemini, you must consider how you will extend their capabilities. SkillzBench is building a free, independent directory and MCP connector designed to address this exact gap. By pinning skills to specific commit SHAs and running them through sandboxed benchmarks, the platform aims to help teams verify that third-party extensions are secure and effective before deployment.

For now, the strategic advice remains clear: buy, don't build, for core AI capabilities. Focus your internal engineering resources on integrating these purchased platforms into your unique business logic. As the ecosystem matures, the ability to safely extend these platforms with vetted, high-lift skills will be the differentiator between successful adoption and costly failure.

The Missing Piece: Agent Skills and the Security Gap

Every AI platform in the big five is racing toward the same destination: agents that don't just answer questions but execute entire workflows. The problem is that nobody has built the connective tissue to make those agents trustworthy.

OpenAI's Chief Economist describes the coming shift "from asking models for outputs to delegating complex, multi-step workflows" — and that changes the risk calculus entirely. When an agent chains together dozens of tools and instructions, the quality of each individual step determines whether the workflow succeeds or fails silently.

Agent skills — those SKILL.md instruction packages that extend Claude Code, Codex, Cursor, Gemini CLI, and chat-based agents — are what turn a capable model into a capable operator. But they also introduce a supply-chain problem that most teams haven't confronted yet.

The Frontier Model Forum puts it bluntly: every tool an agent can access expands both its capability and its exposure. Prompt injection, memory poisoning, and tool supply chain compromise are all live attack vectors — and they compound when agents chain multiple skills together.

The scale of the governance gap is staggering. Deloitte's research finds that only one in five companies has a mature governance model for autonomous AI agents, even as worker access to AI rose 50% in 2025.

The OWASP Agentic Skills Top 10 — the first comprehensive security framework for agent skills across all major platforms — documents ten critical risk categories. That framework matters because it treats skills as a distinct attack surface, separate from the model or the platform beneath them.

What enterprises actually need before they delegate those multi-step workflows is visibility into which skills are safe and which actually improve results. That's the gap SkillzBench is building to close: pinning every skill version to a commit SHA, security-scanning each one, and benchmarking skills in a sandbox to publish the measured Lift in percentage points — always shown separately from trust ratings.

• Commit-SHA pinning so you know exactly which version of a skill your agent is running
• Security scanning and license checks before a skill ever reaches production
• Sandboxed benchmarking that tests each skill with and without the same model, publishing the Lift in percentage points
• A free MCP connector that lets you toggle skills per agent — the agent only sees what you chose

The model layer is commoditizing fast — the gap between the top and tenth-ranked models narrowed from 11.9% to 5.4% in a single year, per the Stanford AI Index. The differentiator is no longer the platform alone. It's the verified, benchmarked skill layer you build on top of it.

Your Action Plan: Picking a Platform and Extending It Safely

Choosing among Anthropic, OpenAI, Google, Microsoft, and Nvidia starts with matching the platform to your actual workload, not leaderboard scores. The frontier is crowded: the score gap between the top and 10th-ranked models fell from 11.9% to 5.4% in a single year, and the top two models are separated by just 0.7%, according to the Stanford AI Index. Model differences that small rarely decide business outcomes.

Start with a simple audit of your use case:

• Coding dominates enterprise AI spend at $4B, or 55% of departmental AI budgets, and half of developers now use AI coding tools daily.
• Copilots lead the horizontal AI layer with 86% share ($7.2B), led by ChatGPT Enterprise, Claude for Work, and Microsoft Copilot.
• Agent platforms hold a smaller but fast-growing 10% share, and OpenAI's chief economist predicts a shift "from asking models for outputs to delegating complex, multi-step workflows."

Next, evaluate the ecosystem, not just the model. Since 76% of enterprise AI solutions are purchased rather than built, per Menlo Ventures' market data, what matters is how well a platform extends: APIs, copilots, and agent skills that fit your workflows. Governance matters too — Deloitte finds only one in five companies has a mature governance model for autonomous agents, so favor platforms that make access controls manageable.

Finally, extend your chosen platform carefully. The Frontier Model Forum warns that "every tool an agent can access expands both its capability and its exposure," and OWASP has documented the ten most critical security risks across all major AI agent platforms. Agent skills are the connective tissue across all five platforms, but quality varies wildly — that's exactly the problem SkillzBench is building against: a free directory of agent skills where every skill is version-pinned, security-scanned, and benchmarked in a sandbox, with its Lift published separately in percentage points.

If you're ready to go further, browse and test free skills on SkillzBench's directory to see which extensions actually lift your agents' performance — and join the waitlist for its free MCP connector and benchmarks, so your chosen platform gets only the skills you've verified.