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This is a historical 2024 snapshot, not a current account of Google Cloud. Through the first part of 2024, Google Cloud’s story was defined by the convergence of rapid AI expansion, custom chips, improving profitability, aggressive partner incentives, customer-switching reforms and two high-profile acquisition efforts that did not close.
Alphabet reported Google Cloud revenue of $10.3 billion in the second quarter of 2024, up 29% year over year, with $1.2 billion in operating income. That financial momentum gave Google more room to invest in infrastructure and AI—but also raised questions about partner economics, regulatory exposure, capacity and long-term customer costs.
This ranking treats “biggest” as a judgment based on strategic significance, financial scale, customer and partner impact, competitive consequences and the likelihood of lasting effects. The ten stories span several businesses: Google Cloud infrastructure, Vertex AI, Workspace, security, startups and Google-wide regulatory matters.
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- Google expanded Gemini and its broader AI portfolio.
- Workspace partner renewal economics changed sharply.
- Reported Wiz and HubSpot acquisition efforts failed to produce deals.
- Google introduced a policy waiving certain egress charges for qualifying migrations.
- Google accelerated investment in TPUs, Axion CPUs, GPUs and data centers.
- Google increased startup credit and support programs.
- Partners received larger incentives for selected generative-AI deployments.
- Antitrust scrutiny widened around Google, AI partnerships and related businesses.
- Google Cloud delivered record revenue and profitability.
- Google-wide layoffs highlighted the importance of distinguishing corporate reductions from Cloud-specific workforce changes.
1. Google turned Gemini into a portfolio-wide Cloud strategy
What happened
Google Cloud Next ’24 made clear that Gemini was not being positioned as a single chatbot. Google integrated Gemini across infrastructure, application development, data, productivity and security.
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The portfolio included Gemini 1.5 Pro and Gemini 1.5 Flash, Gemini Code Assist, Vertex AI grounding, Vertex AI Agent Builder, Gemini in BigQuery, Gemini in Looker, Gemini in Databases, Gemini in Google Workspace, Google Vids, Gemini Cloud Assist, Google Threat Intelligence and Gemini in Security Operations. Google also emphasized access to first-party, open-source and third-party models through its AI platform.
Google’s Next ’24 overview is available in its official announcements roundup.
Why it mattered
The strategic goal was to make Google Cloud useful at every layer of an AI workload: data preparation, model selection, application development, deployment, monitoring, security and productivity. That breadth matters because enterprise AI spending is rarely limited to model inference. Storage, networking, databases, identity, analytics and security can determine the economics of the complete system.
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Who benefited—and what to check
Google-centric data teams and developers gained a more integrated route from BigQuery data to Vertex AI applications. Customers also had more model choice than a strategy based exclusively on Google’s own models would suggest.
Buyers should still compare model pricing, quotas, regional availability, data handling, evaluation quality and portability. A proprietary API can accelerate development while increasing future switching costs.
2. Workspace partner economics were reset
What happened
CRN reported that Google reduced Workspace renewal margins for partners from 20% to 12%, a 40% reduction relative to the previous rate. The same reporting said new Workspace business could receive a first-year margin of as much as 60% in qualifying circumstances.
These are channel-policy claims reported by CRN and Google Cloud partner executives, not figures disclosed in Alphabet’s public financial filings. “Margin,” “rebate,” “commission” and “incentive” may not be interchangeable under individual partner agreements.
Why it mattered
The apparent structure favored acquiring new customers over simply renewing existing ones. That can encourage partners to invest in migration, implementation and new-logo sales, but it can also make recurring renewal revenue less attractive.
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Partners needed to ask:
- Was the incentive available in their country and partner tier?
- Did it apply to direct and indirect partners equally?
- Was the 60% figure gross margin or a separate payout?
- Were expansions, migrations and new logos treated differently?
- What happened after the first year?
For customers, the change was a reminder to separate a reseller’s introductory commercial offer from the cost of long-term administration, support and renewal.
3. The reported Wiz and HubSpot deals did not close
Wiz
Google was reportedly considering Wiz in a transaction valued at approximately $23 billion. Wiz ultimately remained independent. The potential deal would have strengthened Google’s cloud-security, cloud-native application protection and AI-security position.
The significance was not that Google acquired Wiz—it did not. The significance was that Google was reportedly willing to consider one of the largest cloud-focused cybersecurity acquisitions, while the failure showed the difficulty of buying fast-growing security companies at high valuations.
HubSpot
Google also reportedly explored a multibillion-dollar acquisition of HubSpot. The strategic logic would have extended Google’s reach from infrastructure and productivity tools into customer relationship management, marketing and business applications. The reported interest did not become a completed acquisition.
Both cases should be described as reported talks or proposed transactions, not announced acquisitions. The reporting is summarized by CRN.
What customers and investors could infer
Google appeared interested in buying capabilities that could accelerate cloud growth beyond raw compute: security, business software, customer relationships and recurring application revenue. The failed efforts also highlighted valuation, regulatory and integration risks.
4. Google changed the economics of leaving Cloud
What happened
Google Cloud announced that qualifying customers migrating entire workloads away from Google Cloud could avoid certain data-transfer or egress charges. The policy was designed to reduce one of the financial barriers to switching providers.
That does not mean all outbound data transfer became universally free. Eligibility can depend on the workload, service and migration circumstances. Customers retaining Google Cloud services, moving only selected data or falling outside the policy’s conditions may still face charges.
What the policy does—and does not—solve
Even when an egress charge is waived, a migration can involve:
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- Application refactoring and engineering labor.
- Storage retrieval and inter-region transfer costs.
- Destination-cloud compute, storage and networking charges.
- Licensing and support changes.
- Downtime, testing, compliance review and operational risk.
- Data gravity from BigQuery, databases, identity systems and analytics pipelines.
Customers should review the applicable policy and service-specific terms through Google Cloud pricing rather than treating the announcement as a blanket removal of every exit cost.
5. Custom chips and data-center infrastructure became central to the strategy
TPUs and AI Hypercomputer
Google announced Trillium, its sixth-generation TPU, as part of an integrated AI Hypercomputer architecture combining hardware, software, networking and consumption models. Google said Trillium offered 4.7 times the peak compute per chip of TPU v5e and was more than 67% more energy-efficient in its own comparison.
Those are Google’s benchmark claims, not independent test results. Trillium was announced in May 2024; its general availability came later in December 2024, so a mid-2024 roundup should not describe it as generally available at that time. The launch announcement is documented by Google Cloud.
Axion
Google also introduced Axion, its first custom Arm-based data-center CPU. Google claimed up to 30% better performance than the fastest general-purpose Arm instances available in the cloud, up to 50% better performance than comparable x86 virtual machines and up to 60% better energy efficiency than comparable x86 VMs.
Again, these are vendor comparisons. The strategic importance was broader: custom silicon can help Google control performance, energy use, supply, capacity and workload economics while reducing dependence on outside suppliers.
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Google continued to offer NVIDIA GPUs and announced future GPU support, including the Blackwell roadmap. For buyers, advertised chip performance is only one factor. Quota, region, reservation terms, networking, software compatibility and actual availability can matter more than a peak benchmark.
6. Google expanded credits for startups
CRN reported that the Google for Startups Cloud Program offered up to $200,000 in credits over two years, with up to $350,000 for qualifying AI startups, alongside training, technical assistance, product discounts and go-to-market support.
Those are historical 2024 figures, not guaranteed current terms. Startups should confirm eligibility and conditions on the Google for Startups page.
The important questions were not just the headline amount
- Could credits be used for GPUs, TPUs, model calls, storage, networking and support?
- When did the credits expire?
- Did credits guarantee scarce accelerator capacity? Usually, credits and capacity are separate issues.
- What would the architecture cost after the credits ended?
- Could the startup export its data, models and containers?
- Would the startup need a paid support plan?
Credits can reduce the cost of experimentation, but they can also conceal an expensive production architecture. A startup should build a post-credit budget before committing deeply to a platform.
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7. Google made generative AI more lucrative for selected partners
Alongside the tougher Workspace renewal economics, Google reportedly increased incentives for partners delivering generative-AI solutions—by as much as 10 times in selected programs. Google also expanded AI specializations, training, delivery resources and technical boot camps.
The “10×” figure should not be read as a universal increase in every partner payment. Program scope, geography, partner status, workload type and customer eligibility mattered.
The strategic shift
The apparent direction was from traditional resale toward consumption-led services: help customers adopt Vertex AI, modernize data platforms, build agents and operate AI workloads. That can create services revenue, but it requires presales investment, technical staff, certifications and ongoing delivery capability.
The partner tension was clear. New AI business could be highly incentivized while Workspace renewals reportedly became less attractive. Partners needed to distinguish one-time acquisition incentives from recurring gross margin and test what remained after introductory payments expired.
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8. Antitrust scrutiny extended beyond Google Cloud
During 2024, regulatory attention included the August U.S. search-advertising ruling, European Digital Markets Act activity, FTC inquiries into major AI investments and partnerships, and U.K. scrutiny of Google’s relationship with Anthropic.
These were not all Google Cloud-specific enforcement actions. Their relevance was indirect but important. Remedies affecting AI partnerships, investment structures, distribution or Google’s broader technology ecosystem could influence Cloud’s product strategy, capital allocation and relationships with model companies.
Google was not ordered in 2024 to break up Google Cloud, and this roundup should not convert investigations or potential remedies into completed enforcement outcomes. The historical regulatory picture is summarized in CRN’s coverage.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Google Cloud delivered record sales and profitability
The confirmed numbers
Alphabet reported the following for the second quarter of 2024:
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| Measure | Q2 2024 | Comparison |
|---|---|---|
| Google Cloud revenue | $10.3 billion | Up 29% year over year |
| Operating income | $1.2 billion | Up from $395 million a year earlier |
| Illustrative annualized run rate | Approximately $41.2 billion | $10.3 billion multiplied by four |
The $41.2 billion figure is a calculation from one quarter, not a reported full-year revenue result. The official figures appear in Alphabet’s Q2 earnings materials and its earnings release.
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- This USB drive provides plug and play simplicity with the included 18 inch USB 3.0 cable
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Market share requires a definition
CRN cited industry research placing Google Cloud at approximately 12% of global cloud services in Q2 2024. That is a secondary industry estimate, not a market-share figure reported by Alphabet. Readers should check whether “cloud services” means infrastructure services, infrastructure-as-a-service, public cloud or another category before comparing it with reported segment revenue.
Google Cloud’s reported segment includes more than narrow infrastructure revenue. It includes infrastructure, applications, platform services and other offerings. Market-share estimates and company revenue therefore should not be treated as directly interchangeable.
10. Google-wide layoffs did not equal a clearly identified Cloud-wide reduction
Google announced layoffs and reductions in parts of the wider company during 2024. However, the public record did not establish a comparably large, clearly identified Google Cloud-wide layoff round.
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That is a qualified observation—not proof that no Google Cloud employees were affected. Global headcount by Cloud function is not fully disclosed, and layoffs, hiring slowdowns, reorganizations and role transfers can appear differently in public reporting.
The practical interpretation is that Google Cloud’s growth and profitability appeared important enough for the business to remain an investment priority, even as Alphabet managed costs elsewhere. It should not be interpreted as evidence that Cloud was insulated from restructuring or workforce changes.
What these stories reveal about Google Cloud
AI monetization was the central test
Google launched a broad AI stack, from chips and models to agents, data tools, security and Workspace. The financial evidence showed strong Cloud growth and profitability, while Google also said AI was contributing to Cloud demand. But the cited earnings materials did not provide a granular, audited breakdown of AI revenue. Product-launch volume should therefore not be confused with separately measured AI sales.
Partner economics were moving toward consumption
The combination of aggressive new-business AI incentives and weaker reported Workspace renewal economics suggested a preference for partners who could generate new workloads, migrations and consumption. That may benefit technically capable integrators, but it creates risk for resellers dependent on predictable renewal income.
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Portability improved without eliminating lock-in
Egress reform reduced one switching barrier for qualifying migrations. It did not remove application dependencies, data gravity, operational knowledge, destination costs or the effort required to rebuild AI pipelines. A customer can have a cheaper exit and still face a difficult exit.
Practical implications for buyers
For cloud customers
- Compare total cost after credits, introductory pricing and free trials expire.
- Check TPU and GPU availability, quotas and regions before designing around a particular accelerator.
- Evaluate Gemini, open-source and third-party models rather than assuming one model will fit every workload.
- Review data residency, identity, auditability and model-data handling requirements.
- Document what the egress policy covers and separately estimate migration labor, retrieval, downtime and destination-cloud costs.
- Test model export, containerization and data extraction before production lock-in becomes expensive.
For partners
- Separate new-logo incentives from recurring renewal margin.
- Confirm whether payments are rebates, commissions, discounts or gross-margin improvements.
- Model revenue after the first year and after credits or incentives disappear.
- Clarify customer ownership, billing, support escalation and renewal responsibility.
- Consider the cost of AI certifications, presales staffing and delivery capacity.
For startups
- Build a post-credit unit-economics model before accepting a large credit package.
- Do not assume credits guarantee GPU or TPU capacity.
- Maintain a fallback model and serving path where practical.
- Ask what technical support is included and what requires a paid plan.
- Test portability before usage becomes concentrated in proprietary services.
Bottom line
Google Cloud entered the AI era in 2024 with stronger financial momentum, a wider AI product portfolio and greater control over custom infrastructure. Its $10.3 billion quarterly revenue and $1.2 billion operating income showed that the business was no longer defined only by investment and losses.
But the same strategy carried risks: uncertain AI economics, scarce infrastructure, partner dissatisfaction, regulatory scrutiny, high migration complexity and failed attempts to buy strategic capabilities. The most important 2024 development was therefore not any single Gemini feature or chip. It was Google Cloud’s attempt to turn an end-to-end AI platform into durable, profitable growth while changing how customers, partners and competitors related to the business.
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