+15.1% (FY ended 2025-12-31 vs FY ended 2024-12-31) 10-K
Annual net income
132,170,000,000 USD (FY ended 2025-12-31, attributable to the company) 10-K
Annual operating margin
32.0% (FY ended 2025-12-31)
Forward P/E
Unavailable: no dated forecast source
Gloria daily close for session Oct 28, 2026. Market cap and trailing P/E use this close with the SEC share total and trailing diluted earnings, each dated next to its figure. Annual revenue, revenue growth, net income and margin show their own fiscal period and link the 10-K. Current context may postdate this article.
Alphabet Inc. and its peers
Company
Price
Market cap
P/E (trailing)
Forward P/E
Annual revenue (USD)
Annual revenue growth
Annual net income (USD)
Operating margin
Alphabet Inc.
GOOGL · Price dated Oct 28, 2026 · tokenized GOOGLx
3,333,751,000FY ended 2025-12-31, attributable to the company · 10-K
75.8%FY ended 2025-12-31
Missing figures stay unavailable. Forward P/E requires a dated source. Annual revenue, revenue growth, net income and margin come from each company's own 10-K, in US dollars, and each row shows that fiscal year. Revenue growth compares the fiscal year with the prior year stated in the same 10-K. Net income is the full year amount attributable to the company, or available to common stockholders where the filing states only that, or consolidated net income. Each row names its basis. Trailing P/E reads "Not meaningful" when the dated trailing diluted EPS is a loss or zero. An operating margin appears only when the filing states both the operating income and the revenue for the same year.
Sources: SEC filings, Gloria daily closes and CoinGecko. Oldest record refresh: Oct 28, 2026, 5:46 PM EDT. Refresh is not a price or filing date. Each row shows its price date and its annual fiscal period. Peers use industry code and company size, or editorial selection. Market cap multiplies that row price by the company share total the latest SEC filing states for every share class together. Trailing P/E divides it by trailing diluted earnings from SEC filings. Rolled earnings per share are approximate because share counts can change between periods. Both appear only when the SEC data carries its date, so they are estimates at one price date, not synchronized filing valuations. Not investment advice.
Token vs underlying stock
Current comparison for GOOGL. Historical observations may postdate this article.
Split-adjusted regular-session stock closes from the existing Gloria shared history, paired only with an observed token price within 45 minutes of 16:00 New York time. Up to 60 matched sessions. No interpolation or missing-date fill. Weekends and known early closes are omitted. Token units are not adjusted for issuer multipliers or dividend treatment. This compares quoted prices, not fair value, executable premiums or arbitrage returns. Solid line: token. Dashed line: stock. Exact quote times and values appear below. Historical observations. Retrieval refers to the token source. Token quote times use UTC. The fixed cohort shows its oldest member fetch.
Alphabet token vs Alphabet stock: exact source values
Quoted token and share units can have different rights, multipliers and dividend treatment. This is not an executable premium or arbitrage signal.
Google Cloud has launched Knowledge Catalog with expanded semantic model support from Looker, Snowflake, and SAP. It is a direct bet that the next enterprise AI bottleneck is context, not raw model horsepower.
The pitch is simple. AI agents are only as smart as what they know about your business. Google wants to be the company that tells them.
What Google actually shipped
Knowledge Catalog officially debuted on April 22, 2026. It is a rebrand and upgrade of what Google previously called the Dataplex Universal Catalog.
Google describes the product as a Gemini-powered “universal context engine.” Think of it as a reference librarian sitting between a company’s data and the AI agents trying to use it.
The catalog pulls together technical and semantic metadata from Google Cloud services and from partner platforms. Technical metadata covers things like table structures and data types. Semantic metadata covers meaning: what a “customer” or “active account” actually refers to inside a specific company.
Knowledge Catalog uses AI-driven insights to serve up what Google calls governed context. The goal is fewer hallucinations and more accurate agents. Teams reach that context through search and through APIs.
Later updates in October 2026 added more machinery. On October 22, Google introduced self-learning context enrichment and a revamped Knowledge Studio, both in preview.
The self-learning feature is designed to spot gaps on its own by watching how agents and users interact. It then pulls verified definitions from sources such as documents and repositories.
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On October 26, LookML semantic language indexing reached general availability for Looker Core. The feature includes live syncing of changes.
LookML is the modeling language Looker customers use to define business metrics. Indexing it brings deterministic business logic into Google’s context graph.
The Snowflake and SAP angle
The partner integrations may be the most strategically interesting piece. Knowledge Catalog now works with Snowflake and SAP for governed data sharing and unified semantic context across systems.
Crucially, Google says this happens without replicating data. The integrations support zero-copy access, meaning the data stays where it lives while the catalog reads its meaning.
The benchmark claims
Google is making bold performance claims, and they come with an important caveat: the numbers are from Google’s own internal benchmarks.
According to Google, agents using Knowledge Catalog achieve nearly four times greater accuracy. The company also cites up to 33% lower latency and a 75% decrease in token usage.
Each figure targets a different pain point. Accuracy is about trust. Latency is about user experience. Token usage is about the bill.
What this means for enterprises and the cloud race
For enterprise data teams, the immediate question is practical. Companies already running Looker, Snowflake, or SAP alongside Google Cloud get a more direct path to feeding those definitions into AI agents.
The LookML general availability is the most concrete win today. Organizations that already invested years in building Looker semantic models can now reuse that work for agent context, rather than rebuilding it from scratch.
The self-learning enrichment and Knowledge Studio remain in preview. That means they are not yet production-grade commitments, and teams should treat them as features to test rather than infrastructure to depend on.
Disclosure: This article was edited by Diego Almada Lopez. For more information on how we create and review content, see our Editorial Policy.
