Adobe delivers strong Q2, ups outlook on AI monetization, new customers

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Adobe reported a better-than-expected second quarter as the company expanded its customer base due to generative AI features.

The company reported second-quarter earnings of $3.49 a share on revenue of $5.31 billion, up 10% from a year ago. Non-GAAP earnings in the quarter were $4.48 a share.

Wall Street was expecting Adobe to deliver second quarter earnings of $4.39 a share on revenue of $5.29 billion.

Going into the earnings report, Wall Street was most concerned about Adobe's ability to monetize generative AI. Those concerns appeared following Adobe's first quarter report and was only magnified as enterprise software vendors disappointed investors.

Adobe CEO Shantanu Narayen said the company's "highly differentiated approach to AI and innovative product delivery" is delivering value to current customers and attracting new ones.

The company's Digital Media revenue was $3.91 billion, up 11% from a year ago. Most of that was Creative Cloud revenue, but Document Cloud delivered 19% revenue growth compared to a year ago.

Digital Experience revenue was $1.33 billion, up 9% from a year ago.

In prepared remarks, Narayen said:

"In Creative Cloud, we have invested in training our Firefly family of creative generative AI models with a proprietary data set and delivering AI functionality within our flagship products including Photoshop, Illustrator, Lightroom and Premiere."

He added that Firefly has been used to generate more than nine billion images.

As for Document Cloud, Narayen said Acrobat AI Assistant is now available as an add-on subscription for Reader and Acrobat enterprise customers.

Adobe raised its guidance with third quarter revenue of $5.33 billion to $5.38 billion with non-GAAP earnings of $4.50 a share to $4.55 a share. For fiscal 2024, Adobe projected $21.4 billion to $21.5 billion with non-GAAP earnings of $18 a share to $18.20 a share.

Other key points:

• Adobe is extending its applications to integrate third-party multi-modal LLMs.
• The company is seeing early success monetizing AI across its Digital Media and Digital Experience platforms.
• Adobe is seeing strong usage and demand for AI across all customer segments.
• The company said it was seeing new demand for Creative Cloud apps powered by new releases and digital channels.

Epicor beefs up AI-driven ERP vision via acquisition, new launches

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Epicor has acquired two companies in recent months as it rounds out its strategy to infuse artificial intelligence across its ERP platform.

The company, which recently passed the $1 billion mark in annual recurring revenue, on Wednesday announced the acquisition of KYKLO, which provides product information management and lead-gen tools for manufacturers and distributors.

Epicor CEO Steve Murphy said the purchase is part of the company's AI-driven cognitive ERP vision that aims to turn systems of record into an insights engine.

Constellation ShortList™ Enterprise Cloud Finance

KYKLO will complement Epicor's Commerce software with product information, real-time catalogs and content syndication.

The KYKLO acquisition follows last month's purchase of Smart Software, which provides cloud inventory planning and optimization applications. Smart Software was already a Epicor independent software vendor partner and integrated into multiple Epicor ERP modules.

At Epicor's Insights 2024 user conference last month, the company launched its Epicor Grow portfolio, which includes AI and business intelligence tools aimed at the supply chain.

Epicor Grow includes generative AI, machine learning, analytics and natural language processing for more than 200 industry use cases.

The Epicor Grow portfolio includes, Epicor Prism, which is a genAI service across Epicor Industry ERP Cloud, and Epicor Grow AI, which surfaces insights across industries. Arturo Buzzalino, VP of Products and Innovation at Epicor, said Prism is the company's first LLM pipeline.

Epicor also launched Epicor Grow Inventory Forecasting, which leverages forecasting engines from Smart Software, Epicor FP&A and Epicor Grow BI.

The company also launched Epicor Grow Data Platform to manage enterprise data, create pipelines and leverage business intelligence.

Broadcom delivers strong fiscal Q2 on AI demand, splits stock 10-for-1

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Broadcom saw strong AI demand in the fiscal second quarter and said VMware accelerated its software business.

The company reported fiscal second quarter net income of $2.12 billion, or $4.42 a share, on revenue of $12.49 billion, up 43% from a year ago. Non-GAAP earnings were $10.96 a share. The company also said it will split its stock 10-for-1 on July 15.

Wall Street was looking for second quarter non-GAAP earnings of $10.84 a share on revenue of $12.1 billion.

Broadcom also raised its revenue guidance for fiscal 2024 to $51 billion compared to estimates of $50.28 billion.

CEO Hock Tan said the second quarter results were "once again driven by AI demand and VMware." AI product revenue was $3.1 billion in the quarter.

By unit, Broadcom's semiconductor revenue, which is being driven by AI data centers, was $7.2 billion, up 6% from a year ago. Infrastructure software revenue was $5.28 billion, up 175% from a year ago due to the VMware acquisition.

Nutanix winning deals vs. VMware, but Broadcom punching back with pricing

Broadcom ended the quarter with cash and cash equivalents of $9.81 billion.

Key points from the earnings conference call include:

• VMware revenue in the second quarter was $2.7 billion, up from $2.1 billion in the first quarter. Tan said the integration was going well and the company is making "good progress on the transition. The company has signed up nearly 3,000 of its largest 10,000 customers to deals, mostly multi-year contracts. 
• Tan reiterated that VMware would deliver $4 billion a quarter in revenue, but declined to give a time frame. 
• Networking revenue for Broadcom is expected to grow 40% in the third quarter. Tan said networking products were benefiting from AI data centers. 
• Tan downplayed any Nvidia competition. He said Broadcom uses its IP portfolio to create custom AI accelerators so the companies don't compute much. "We are not competitors to them and don't try to be either," said Tan. "On networking that may be different, but we are approaching it from different angles. We are very deep in Ethernet and have been doing it for over 25 years. It's a natural extension for us to go into AI, but we don't do GPUs. We enable GPUs to work very well."

GM builds its data factory, eyes genAI

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Brian Ames, senior manager of production AI and data products at General Motors, said the company has stood up its data factory and plans to layer in generative AI capabilities in the next year.

Ames' talk at the Databricks Summit conference is one part of a broader effort at GM to improve its data and software game. In February, GM CEO Mary Barra said the company is "determined to get the software right" to deliver good customer experiences. GM is working to improve its software in autos as well as revamp the technology behind Cruise.

In May at an investor conference, Barra noted that artificial intelligence will be critical to Cruise and the company overall. "When you talk about artificial intelligence, the ultimate application of that is autonomy is our Cruise operations that use machine learning and AI. But there's a lot that we're working on to leverage AI in some of the business processes to take cost and complexity out of what we do," said Barra.

With that backdrop, Ames' talk highlighted how companies need to get their cloud and data strategies right before embarking on generative AI.  We've documented this trend repeatedly in customer stories on CVS, Wayfair, Equifax, Intuit, Rocket and JPMorgan Chase to name a few.

• Intuit sees 'green shoots' from its generative AI strategy
• Rocket Companies’ genAI strategy: Playing both the short and the long game
• How Verizon buckets its AI, genAI use cases
• Equifax bets on Google Cloud Vertex AI to speed up model, scores, data ingestion
• JPMorgan Chase CEO Dimon: AI projects pay for themselves, private cloud buildout critical

• Medtronic aims to leverage medical device data for AI-enabled care

Ames said GM's first move was to create an infrastructure that could surface data more easily. "GM has a ton of data. That's not the problem. We had a beautiful on prem infrastructure. Why change? Well, two reasons. Number one was data efficiency. More importantly, the world changed. And GM understood that if we didn't have AI and ML in our arsenal, we could find ourselves at a competitive disadvantage," he said.

GM set out to transform its data infrastructure about 15 months ago to go all cloud. The plan was to build a data insight factory that could democratize information that can be used for safety and vehicle telemetry. "We needed to move from solution silos to single sources of truth with rapid collaboration. We needed to move away from fragmented governance into a simple unified governance. And we felt if we did those two things extremely well, that we'd be able to go from pockets of limited AI and ML execution to really building AI and ML into the DNA of GM," said Ames.

Today, GM has this architecture built on Databricks. GM also built an interface that can distribute insights by brand and product. The killer app for this data platform has been telemetry data from GMs fleet to be used to gauge the health of the autos on the road as well as predictive maintenance and safety. The fleet includes 10s of millions of cars with multiple combination of sensors. The goal? Zero crashes.

Going forward, Ames said GM's plan is to break down silos for various AI and machine learning projects around the company and move them to production faster. "we're reducing the time to insight and we're finding ways to contribute value. In year two, we're going to layer on genAI and perhaps take another step at GM towards our mission of zero crashes," said Ames.

More:

• AI infrastructure is the new innovation hotbed with smartphone-like release cadence
• Don't forget the non-technical, human costs to generative AI projects
• GenAI boom eludes enterprise software...for now
• The real reason Windows AI PCs will be interesting
• Copilot, genAI agent implementations are about to get complicated
• Generative AI spending will move beyond the IT budget
• Enterprises Must Now Cultivate a Capable and Diverse AI Model Garden
• Secrets to a Successful AI Strategy
• Return on Transformation Investments (RTI)
• Financial services firms see genAI use cases leading to efficiency boom

Databricks launches Data Intelligence Platform, melds data, AI workflows

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Databricks is adding generative AI capabilities via Mosaic AI across its data and AI platform, up its data warehousing game and get more out of data via business intelligence tools. The launches come a week after Databricks acquired Tabular.

At its Databricks Summit, which arrives a week after Snowflake's customer conference, the company laid out a strategy that revolves around tightly coupling data management and AI. What Databricks is building now are the tools to add governance to data and AI and deliver real insights.  The idea is that large language models (LLMs) will allow you to interact with data with simple queries.

In a briefing, Databricks officials noted that about 85% of the generative AI experiments in enterprises fail to make it to production. The problem in a nutshell revolves around cost at scale, data privacy and getting the right answers out of models. It is not the model, but what model delivers the best results for your data.

If you zoom out, the battle between Snowflake and Databricks boils down to this: Snowflake needs to prove it can evolve from a data warehouse and data management vendor to an AI platform. Databricks is a data and AI platform that can also offer data warehouse and business intelligence capabilities. Nevertheless, Constellation Research analyst Doug Henschen boiled down the race. "Databricks is leading on AI and genAi, but It has a lot to prove on data warehousing and is behind on data marketplace and data apps," he said. 

With generative AI, Databricks is creating a Data Intelligence Platform that includes Delta Lake, a unified data storage system, Tabular, which will bridge Databricks with the Iceberg crowd, the generally available Unity Catalog, and Mosaic AI, Databricks SQL, dashboards and other tools. Databricks Data Intelligence Platform will be 100% serverless.

The vision and Databricks strategy

During a keynote, Databricks CEO Ali Ghodsi said data and AI is converging. "In the last 18 months, every CEO from a Fortune 500 company or small company I've talked with thinks that data and AI is going to be super strategic for them over the next five years. They think that that's how they're going to win," said Ghodsi. "That's going to be the main differentiating factor whether it's the financial sector, retail,  media, healthcare or in the public sector. Doesn't matter, all of it. It's going to be data and AI."

Ghodsi said that the last 18 months has only increased the pressure to bring use cases into production. He said: 

"There's a food fight inside organizations over who owns an AI. That's number one. Number two, everybody's worried about security and privacy of their data with genAI. They are worried about security and privacy for the whole data estate. And that data estates today is super fragmented."

Ghodsi said the fragmentation can be solved by storing data in open formats so enterprises don't hand data to vendors that'll only lock them in. "Our vision starts with the lakehouse. We said stop giving your data to vendors. It doesn't matter if it's a proprietary data warehouse in the cloud, Snowflake or Databricks. Don't give it to us either."

Databricks acquisition of Tabular was designed to create a USB-like standard for data. Ghodsi said interoperability between Delta Lake and Iceberg will solve a lot of enterprise problems. He added that Databricks will work with the communities in Delta Lake and Iceberg to bring formats together over time. He likened data lakehouse formats as a Betamax vs. VHS type of challenge. 

The other broad theme from Databricks was combining its Lakehouse platform with Mosaic AI. Data intelligence will enable enterprises to ask common questions with genAI and create a AI-meets-business intelligence format. "That's what the whole company is working on," said Ghodsi. 

Nvidia CEO Jensen Huang also showed up at the keynote with Databricks CEO Ali Ghodsi and delivered a few interesting nuggets:

• Huang argued that open source LLMs "were probably the most important events this year" because it enables enterprises to better leverage genAI. 
• GenAI will have the most impact on customer service. "Customer service represents probably several trillion dollars worth of expenses and every company is deciding between the chatbot or the customer service agent. It is partly about the fact that you could automate but it's mostly about the data flywheel," said Jensen. "You want to capture the engagement for the data flywheel. We're going to have proactive customer support."
• AI factories shouldn't be built near populations where the energy grid is already challenged. "Earth has a lot more energy," said Huang. "It's just in the wrong places."

News overview

The news out of Databricks Summit adds up to building out this data and AI platform that's accessible. Here's a look at a few of the announcements.

• Mosaic AI Agent Framework is in preview and includes tools to build an agent application, evaluate at them with humans and LLM judges and deployment tools with real-time APIs.
• Mosaic AI Model Training to fine tune small open-source models. Databricks said customers in private preview can use smaller models and reduce costs while reducing latency. Some customers are seeing 10x improvements in inference costs and 2x improvements in latency.
• A text-to-image model trained on Shutterstock data by Mosaic AI.

• Support for usage tracking, rate limits and guardrails as well as hooks into Unity Catalog.
• Databricks SQL is now 70% faster and Databricks showed comparisons for its price/performance vs. Snowflake
• Databricks AI/BI with dashboards generally available. A tool called Genie so you can query data is in public preview. To date, Databricks hasn't tried to tackle BI use cases, but going forward BI will be a focus.

• Genie will learn from your data and semantics and feature an ensemble of AI agents to leverage Unity Catalog metadata. Genie will also query data across all workloads and related assets, remember and learn and seek clarifications.
• Lakeflow Connectors will ingest data from SaaS applications and databases. Lakeflow won't have all of its components right away, but Databricks said there will be a steady cadence over the next 12 months. The aim is to simplify the data engineering process.
• Unity Catalog OSS, which will be an open catalog that's available now and combines data and AI with interoperability across formats, open APIs and governance.
• Enhanced Federation that includes Lakehouse Federation to connect data sources to Unity Catalog with policies and Hive Metastore Federation, which can read/write for internal or external Hive Metastore or AWS Glue.
• Secure Collaboration via Clean Rooms and Foreign Catalog Sharing.
• Business Metrics, which will pull from your lakehouse assets, leverage a central inventory of certified metrics and make them accessible.

The launches across Databricks Summit have a heavy dose of combining genAI and data warehousing as well as its usual data engineering fare. The case Databricks is making is that it can consolidate your data platforms and silos. In the end, Snowflake and Databricks will compete for customers. 

Constellation Research analyst Holger Mueller said:

"CxOs always need to remind themselves where the vendor came from. Snowflake came out of the data warehouse and showed it could add cloud elasticity. A large part of Snowflake's success was the familiarity with data warehousing. Databricks lived in the big data and cloud world from its inception in a model less familiar to CxOs. If Snowflake manages to add good enough lakehouse capabilities soon, it will win as it has the transactional data. If Snowflake is slow or fumbles, it's Databricks' game to win because the ability to master large amounts of unstructured data is the harder engineering challenge and Databricks has mastered it."

More Databricks:

• Databricks acquires Tabular to bridge Iceberg formats with its lakehouse
• Databricks launches DBRX LLM for easier enterprise customization
• What to Look for in Analytical Data Platforms for a Cloud-Centric World
• Databricks acquires Arcion for $100 million
• Databricks adds on to the Lakehouse, acquires MosaicML for $1.3 billion
• Databricks launches Lakehouse Apps, aims to be development platform
• JPMorgan Chase: Digital transformation, AI and data strategy sets up generative AI

Splunk adds genAI tools, more Cisco touchpoints across observability and security

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Splunk launched a new set of generative AI tools across its products, security operations additions, and data management applications as well as more Cisco integrations.

The announcements made at Splunk's .conf24 conference come a week after Cisco's customer conference where the two companies outlined integrations to connect observability platforms. At Cisco Live, the companies announce a new single sign-on system that streamlines workflows between Cisco AppDynamics and Splunk and Splunk Log Observer Connect for Cisco AppDynamics. Cisco AppDynamics will also integrate with Splunk Enterprise, Splunk Cloud and Splunk ITSI. Overall, Cisco and Splunk will look to unify the observability experiences across both platforms as they ultimately integrate them.

Observability and security customers of both Splunk and Cisco are watching integrations closely as well as clues to how the platforms will come together. A Splunk report found that the total cost of downtime for Global 2000 companies is $400 billion annually.

Cisco Q3, Q4 outlook better as company preps Splunk integration

Here's what Splunk announced at .conf24:

• Splunk added generative AI tools for Observability, security and IT Service Intelligence. AI Assistant in Observability Cloud adds a natural language interface for engineering teams to detect and correct issues. AI Assistant in Security brings genAI to workflows in a move to speed up analyst investigation. Splunk AI Assistant in SPL makes the insights from the company's unified security and observability platform more understandable to customers.
• Advanced AI for IT Service Intelligence has a new Configuration Assistant and gets Drift Detection for KPIs and Adaptive Thresholds for entities.
• Splunk Enterprise 8.0 adds a bevy of security operations center advances to simplify how analysts detect, investigate and respond to threats.
• Federated Analytics enables customers to analyze data directly where it resides starting with Amazon Security Lake.
• Splunk Attack Analyzer, Splunk Enterprise Security and Splunk SOAR customers will see integrations that leverage Cisco Talos threat intelligence.
• Splunk's Data Management portfolio will get Pipeline Builders to enable customers to filter, mask and transform data to simplify processing and Ingest Processor, which will give customers the ability to convert logs to metrics and route them to Splunk Observability Cloud, Splunk Platform or Amazon S3.

Constellation Research's take

Constellation Research analyst Andy Thurai said:

"The natural language interface Splunk AI assistant for SPL can be very useful to power Splunk users. SPL is not easy to write and needs an expert level understanding to write it. By providing a natural language interface AI assistant, Splunk/Cisco hopes to democratize the SPL creation.

Natural language queries of incident, related observability data, ITSI, and finding fixes quicker can be good. However, I have found some of Splunk's closest competitors are way ahead of them in this regard.

Data ingestion pipeline and log/data optimization are areas where competitors have an advantage over Splunk, even after these announcements.

The Log Observer connect as a full two-way centralized log mover could be powerful with all telemetry in one place, but I do anticipate scaling issues. But my guess is Log Observer is a temporary fix to connect log and observability clouds.

Splunk's AI announcements today are nothing earth shattering and competitors already are ahead. Cisco/Splunk now has a problem of integrating Splunk Observability Cloud, Log Enterprise, AppDynamics, and ThousandEyes, and Network Observability data into one meaningful solution. While Cisco and Splunk all service portions customer needs well, the combined solutions is going to take a while to build. I estimate that it might take two years or longer. 
 
There are also overlapping solutions such as FSO, RUM, synthetic monitoring, incident intelligence, and logs which that all need to be redesigned.
 
I still stand by my original comments that it might take two years for this to come to fruition at the earliest. It is going to be difficult to decide which architecture will win and if all can be cloud or hybrid. The good thing is that they have one chief product officer who will drive the product and strategy. But it is too early to make a call. I haven't seen enough yet."

More Cisco and Splunk:

• Cisco closes Splunk purchase, previews integrations ahead
• Cisco to cut 5% of workforce, Q3 outlook weak
• Cisco acquires Splunk in $28 billion observability, AI and cybersecurity play

• Cisco is buying Splunk for $28B in cash. What does it mean for you as a customer?

Oracle partners with Google Cloud, takes on OpenAI workloads

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Read more

Cloud hell may have just frozen over as Oracle and Google Cloud outlined a partnership that gives enterprises an option to combine Oracle Cloud Infrastructure and Google Cloud.

For any enterprise technology veteran who remembers Oracle and Google duking it out in court over Android and Java, the partnership is almost like a trip through bizarro world. Oracle also has a similar partnership with Microsoft Azure. To that end, OpenAI is also now buying capacity from Oracle via the OCI and Azure tie-up.

Simply put, Oracle Cloud has nailed being an efficient cost-effective infrastructure and has aligned up with all the large players not named Amazon Web Services (AWS). Should a similar deal be cut with AWS and OCI hell will really freeze over. Oracle CTO Larry Ellison jabs at AWS as much as he did SAP back in the day.

Nevertheless, Ellison wouldn't rule out a AWS partnership either. 

"Customers are using multiple clouds not only infrastructure clouds, but they might have Salesforce applications or Workday applications or they use multiple cloud clouds in their in their business right now. We think that these are all the clouds become interconnected. We are thrilled we have that connection with Microsoft and the same thing with Google Cloud. We'd love to do the same thing with AWS. 

We think we should be interconnected to everybody. And that's what we're attempting to do within our multi cloud strategy. I think that's what customers want. I'm optimistic that that's the way the world will settle out. We'll get rid of these fees for moving data from cloud, the cloud. And all the clouds will be interconnected."

Here are the details of the Oracle and Google Cloud tie-up followed by the OpenAI deal and the not-too-surprising strong cloud results.

Google Cloud and OCI

Google Cloud's Cross-Cloud Interconnect will be initially available in 11 global regions so customers can deploy general purpose workloads without data transfer charges. Later in 2024, the companies said Oracle Database @ Google Cloud will be available with the database, network, feature and pricing at OCI.

Ellison said the deal with Google Cloud is good for joint customers. Sundar Pichai, CEO of Google and Alphabet said the partnership will combine Oracle's database and applications with its AI layer.

Oracle Database @ Google Cloud would give customers direct access to OCI database services deployed in Google Cloud data centers. That combination would enable Vertex AI and Gemini from Google Cloud to be leveraged without latency. For Google Cloud, the Oracle partnership can embed its AI services into more enterprises. Customer references cited by Google Cloud for AI adoption include Bayer, Best Buy, Discover Financial and TD Bank to name a few. Also see what Equifax and Wayfair have done with Google Cloud.

Google Cloud Next 2024: Google Cloud aims to be data, AI platform of choice | Google Cloud Next: The role of genAI agents, enterprise use cases

The two companies said they will jointly sell Oracle Database @ Google, provide support and a unified experience and then provide a bevy of services to mix and match.

Australia East (Sydney), Australia Southeast (Melbourne), Brazil East (São Paulo), Canada Southeast (Montreal), Germany Central (Frankfurt), India West (Mumbai), Japan East (Tokyo), Singapore, Spain Central (Madrid), UK South (London), and US East (Ashburn) are the regions that will initially launch. 

Constellation Research analyst Holger Mueller said the Google Cloud and Oracle partnership makes sense. 

"When something works, tech vendors copy the playbook: In this case it is the Oracle / Microsoft partnership that allows to run OracleDB @ Azure from the Azure console. Now the same gets repeated for Google Cloud. This is great news for enterprises, and good for Google Cloud and Oracle. And it makes it harder for cloud databases to compete with Oracle for building Next Generation Applications."

Other details about the Google Cloud and OCI partnership include:

• In 11 regions, customers can move data across Google Cloud and Oracle. 
• Joint customers will be able to access and deploy Exadata and Autonomous Database later this year through Google Cloud. 
• Customers can bring their licenses and get access to support. 
• If you use your GCP commitment to consume extra data, OCI will credit you toward support costs. 
• Field teams are aligned. 
• Oracle services across the board will be used in Google Cloud including Oracle apps. 
• The two clouds will be available in one console. 
• Interconnects will be up to 100 gigabit per second.
• Google customers can take their license and deploy on Google Cloud if self managed. 
• Pricing parity will be available on both clouds. 

Oracle CEO Safra Catz said the Google Cloud deal highlights broader traction and the ability to integrate applications as well as cloud infrastructure. 

"A very large enterprise tech company signed a contract in Q4 for over $600 million where we will be helping them transform their operations with Fusion to enable them to become more agile, faster, growing and more profitable. We will replace out many of our competitors products." 

OpenAI buys OCI capacity

OpenAI will use OCI for additional capacity in a partnership with Microsoft Azure and Oracle. Microsoft is already using OCI for additional workloads.

"We are delighted to be working with Microsoft and Oracle. OCI will extend Azure's platform and enable OpenAI to continue to scale," said Sam Altman, OpenAI CEO, in a statement.

OpenAI needs all the capacity it can get. The company is scaling fast, constant training models and needs to serve 100 million monthly users with its ChatGPT services.

Ellison said Oracle ramped up the Azure partnership in the fourth quarter with 11 of the 23 planned OCI datacenters inside Azure going live.

• Oracle, Microsoft launch Oracle services within Azure data centers
• Oracle adds generative AI features into Fusion Cloud, expands Azure partnership into Europe
• Oracle launches OCI Generative AI service, plans to embed throughout databases, applications
• Palantir will move workloads to Oracle Cloud as both court governments and enterprises
• Microsoft uses Oracle Cloud Infrastructure for Bing conversational workloads

Ellison outlined some of the OpenAI partnership details. 

"We're building a very large data center with lots of Nvidia chips. The new Nvidia chips and new Nvidia interconnect liquid cooled. These are primarily for training. The training goes beyond languages because they're neural networks trained with not just language but math and masses of images as well. That's a very different problem than answering a question posed by someone. Everyone that's big is going to be training their models on imaging. That's a huge amount of additional data and training and we're right in the middle of it."

The Google Cloud, OpenAI deal trump earnings miss

Oracle also reported fourth-quarter earnings that missed expectations. The company reported fourth-quarter earnings of $1.11 a share on revenue of $14.3 billion, up 3% from a year ago. Non-GAAP earnings were $1.63 a share.

Wall Street expected fourth quarter earnings of $1.65 a share on revenue of $14.57 billion.

Cloud revenue in the fourth quarter was $5.3 billion, which missed estimates of $5.45 billion. Nevertheless, cloud infrastructure revenue in the fourth quarter was up 42% from a year ago. Cloud application revenue was $3.3 billion, up 10%.

For fiscal 2024, Oracle delivered net income of $10.5 billion, or $3.71 a share on revenue of $53 billion.

Oracle CEO Catz said the company "signed the largest sales contracts in our history—driven by enormous demand for training AI large language models in the Oracle Cloud." Remaining performance obligations exiting the fourth quarter was $98 billion, up 44% from a year ago. She did note that currency fluctuation continues to be a wild card to results.  

In fiscal 2025, Catz said Oracle will continue to gain workloads. "I also expect that each successive quarter should grow faster than the previous quarter—as OCI capacity begins to catch up with demand," said Catz. "In Q4 alone, Oracle signed over 30 AI sales contracts totaling more than $12.5 billion—including one with Open AI to train ChatGPT in the Oracle Cloud."

AWS, Microsoft Azure, Google Cloud battle about to get chippy

"Customer conversations are now absolutely only focused on our cloud services," said Catz. "It is all about our comprehensive, highly differentiated and secure cloud offering. Customers have progressed from their initial curiosity about Oracle Cloud into full blown rollout."

Catz said that OCI consumption revenue would have been higher if it weren't for supply side constraints. 

"As on-premise databases migrate to the cloud, either to OCI directly or using database Azure or Google Cloud. we expect these cloud database services will be that third leg of revenue growth alongside OCI," she said.  

Other items:

• Oracle won't break out Cerner's impact. Catz would typically give guidance that excluded Cerner. 
• As for the outlook, Catz said OCI capacity will meet demand and boost revenue each quarter in fiscal 2025. 
• OCI will grow faster in fiscal 2025 than the 50% this year. 
• Capital expenditures will double in fiscal 2025. 
• Oracle will hone its outlook going forward in October at Oracle World. 
• Revenue in the first quarter will grow 6% to 8%. 
• Total cloud revenue will be 21% to 23% in the first quarter. 
• Non-GAAP EPS is expected to grow between 11% to 15% and be between $1.33 and $1.37.

What's next? Bigger and smaller OCI data centers

Ellison said OCI's integrated stack is uniquely suited for AI. "When you charge by the minute, faster also means less expensive. OCI trains large language models several times faster and at a fraction of the cost of other clouds," he said. "The operating system and the database are fully autonomous."

Ellison said Oracle also occupies less space with OCI and that's why it can park infrastructure in Azure and Google Cloud facilities.  He said:

"We talked for a while about our ability to build very small data centers when needed. Virtually any one of our customers could choose to have the full Oracle Cloud in their data center. We can start very small." 

Ellison, however, noted that Oracle will also go big. He said:

"We're now bringing 200 megawatt data centers online. We are literally building the smallest most portable, most affordable cloud data centers, all the way up to 200 megawatt data centers, ideal for training very large language model and keeping them up to date. This AI race is gonna go on for a long time. It's not a matter of getting ahead in AI, but you also have to keep your model current. And that's going to take larger and larger data centers. Some of the data centers we have that we're planning are absolutely even bigger."
 

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• AI infrastructure is the new innovation hotbed with smartphone-like release cadence
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• The real reason Windows AI PCs will be interesting
• Copilot, genAI agent implementations are about to get complicated
• Generative AI spending will move beyond the IT budget
• Enterprises Must Now Cultivate a Capable and Diverse AI Model Garden
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• Financial services firms see genAI use cases leading to efficiency boom