GitLab sees traction as it competes against GitHub, Atlassian

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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GitLab is on a $559 million revenue run rate exiting the third quarter with 8,175 base customers. While that quarter is garnering Wall Street attention, enterprises should focus on the competitive dynamics moving GitLab's results as its DevSecOps platform gains traction.

The company reported third-quarter revenue of $149.7 million, up 32% from a year ago, with a net loss of $1.84 a share. Non-GAAP earnings for the third quarter checked in at 9 cents a share.

Gitlab's portfolio includes GitLab Duo, a suite of AI tools, and a platform for agile development, automated software delivery, source code management, security and compliance and continuous integration and delivery.

Constellation Research analyst Holger Mueller said:

"Gitlab is on a path to break half a billion in revenue for the first time in its history, fueled by software powered enterprises. Whether the security angle of its DevOps product is the key driver remains to be seen, as the market and the competition is growing."

Here's a look at why GitLab's third quarter was better than expected.

Jira switching to cloud-only model opens the door. GitLab CEO Sid Sijbrandij said Atlassian Jira customers are evaluating platforms and GitLab is competitive. He said:

"Atlassian’s decision to stop support for its server offering is making customers reconsider what product they use for Enterprise Agile Planning. We are focused on making it easier for these customers to move to GitLab SaaS and self-managed. We recently launched a new Enterprise Agile Planning SKU. Now, GitLab Ultimate customers can easily bring non-technical users into the platform.

Because of that server offering being deprecated, a lot of customers are taking stock of where they're at. It is a natural point for enterprises to evaluate."

AWS and Google Cloud are strong partners and motivated to replace Microsoft's GitHub. Sijbrandij said that it won a third quarter deal with a European telecom against GitHub in partnership with AWS.

With Google Cloud, GitLab is being built into the console. Sijbrandij cited a handful of customer wins against GitHub as enterprises aimed to centralize platforms. Sijbrandij said:

"We've got strong partnerships with both Google and AWS. And they're interested because we help their customers move to their cloud faster. We help them accelerate moving those workloads. And an interesting thing we did with Google recently, we announced at Google Next that they'll be integrating GitLab into their development console for GCP. I think that's a really interesting development that will pay off as many things over the longer term. I think it speaks to the strength of these partnerships together with the AWS example."

• GitHub plans to infuse Copilot throughout its platform

• Constellation ShortList™ DevOps

• Constellation ShortList™ AI-Powered Developer Platforms

• Atlassian acquires Loom for $975 million, will add asynchronous video to platform

Compliance and governance. Sijbrandij said security and compliance integration has become a key selling point. GitHub appears to be landing customers like Lockheed Martin and Carfax because it builds security and compliance directly into workflows.

The ability to set controls and governance frameworks that's integrated into developer workflows has become a selling point. Sijbrandij said:

"With point solutions, developers have to wait on security teams to identify vulnerabilities. Or, if they have access to a security scanner to assess their code, they need to manually copy and paste the scanner results back into their development tool. The result is code that isn’t scanned at the time it’s written. This increases the time required to detect and resolve vulnerabilities."

GitLab has GitLab Dedicated, a single-tenant SaaS platform that gives customers data isolation and residency. Sijbrandij added:

“GitLab is the only DevSecOps platform that brings together security, compliance & governance, AI, and enterprise agile planning. Enterprises face complexity from all directions in the form of rapidly increasing user expectations, more advanced cyber-attacks, and more strict industry regulations. We believe they need GitLab to help them navigate this complexity and realize business value. Our platform improves engineering productivity, reduces software spending."

DevSecOps is a critical category as AI workloads grow. Sijbrandij said that AI and integrating it with developer workflows is also driving sales.

"AI needs to be throughout the life cycle and for multiple things, like only 25% of the time of a developer spends on coding, 75% is other tasks. And as developers get more productive, they write more code, you need to also increase the productivity of security and operations. So, we're focused on making it work throughout the life cycle," he said.

Expansion beyond developers. GitLab's enterprise planning suite is attracting usage beyond developers. Business users that have to interact with developers and engineers can use portfolio and project management tools without getting bogged down.

Microsoft updates Copilot with ChatGPT-4 Turbo

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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Microsoft Copilot is being upgraded to OpenAI's GPT-4 Turbo model as the software giant rolls out a series of generative AI updates. The move comes as Microsoft and OpenAI move to put the Sam Altman kerfuffle in the rear-view mirror.

In a blog post, Microsoft said Copilot will soon be able to answer with OpenAI's latest model, which is being tested and will roll out more widely in the weeks ahead.

What remains to be seen is whether these model updates affect usage or become a simple behind-the-scenes rollout. With model choice becoming more of a concern, enterprises are likely to wonder about model updates and what it would take to swap out foundational models if needed.

For now, the Copilot model updates land between the OpenAI soap opera and AWS' launch of its Q generative AI assistant that runs across its services.

• AWS, Microsoft Azure, Google Cloud battle about to get chippy
• How much generative AI model choice is too much?
• Microsoft launches Azure Models as a Service
• Microsoft launches AI chips, Copilot Studio at Ignite 2023

In addition to GPT-4 Turbo upgrades, Copilot will get OpenAI's new DALL-E 3 Model for images and capabilities that combine GPT-4, vision with Bing image search and web search data.

Microsoft also said it is testing Code Interpreter, a service that will provide more accurate calculations, coding, data analysis and visualization. Code Interpreter is in limited testing too. Bing will also get Deep Search, which will use GPT-4 to provide better search results for complex topics.

Also see: How Generative AI Has Supercharged the Future of Work | Generative AI articles | Why you need a Chief AI Officer | Software development becomes generative AI's flagship use case | Enterprises seeing savings, productivity gains from generative AI | Work in a generative AI world will need critical, creative thinking

IBM, Meta form AI Alliance: What can it accomplish?

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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IBM and Meta have formed the AI Alliance with 50 founding members aiming to provide "open and transparent innovation" in artificial intelligence. The catch is that many of the big names--Google, Nvidia and Microsoft--driving the AI advances so far aren't among the founding members.

The concept behind the AI Alliance is notable in that the group wants to bring international players, academics, researchers, governments and developers together to provide an open science and technology ecosystem. The tension around technologies like generative AI revolves around for-profit motives vs. doing what's right for humanity. Security expert Bruce Schneier just penned a long missive about AI and trust worth a read.

And IBM and Meta have a history of contributing to open source as well as initiatives such as Open Compute Project. However, it remains to be seen what the AI Alliance ultimately accomplishes without some key AI leaders.

Also see: How Generative AI Has Supercharged the Future of Work | Generative AI articles | Why you need a Chief AI Officer | Software development becomes generative AI's flagship use case | Enterprises seeing savings, productivity gains from generative AI | Work in a generative AI world will need critical, creative thinking

The founding members and collaborators include the following: AMD, Anyscale, CERN, Cerebras, Cleveland Clinic, Cornell University, Dartmouth College, Dell Technologies, EPFL, ETH, Hugging Face, Imperial College London, Intel, INSAIT, Linux Foundation, MLCommons, MOC Alliance operated by Boston University and Harvard University, NASA, NSF, Oracle, Partnership on AI, Red Hat, Roadzen, ServiceNow, Sony Group, Stability AI, University of California Berkeley, University of Illinois, University of Notre Dame, The University of Tokyo, Yale University and others.

Constellation Research analyst Andy Thurai was quick to note who was missing. He said:

"Major A list players such as AWS, Databricks, Snowflake, Dataiku, Data Robot, Domino Data Lab,  Scale.AI, NVIDIA, Microsoft, OpenAI, Google, Anthropic, Cohere, AI 21 Labs and many others are missing from the list."

Like any alliance, federation or task force by any other name, the jury is out until something actually happens. The AI Alliance indicated that it will be action oriented and focus on "fostering an open community and enabling developers and researchers to accelerate responsible innovation in AI while ensuring scientific rigor, trust, safety, security, diversity and economic competitiveness."

Focus areas for AI Alliance include:

• Developing benchmarks and evaluation standards for responsible development of AI systems as well as tools for safety, security and trust.
• Advance an ecosystem of open foundation models with diversity on many fronts.
• Create an AI hardware accelerator ecosystem.
• Support AI skills and research.
• Develop educational content and resources.

Meta and IBM's experience in open source as well as open hardware could come in handy for kicking off AI Alliance. Simply put, the group is worth watching pending further developments.

Thurai said it's unclear what AI Alliance will ultimately accomplish. He said:

"There are many tools and software components, both open source and commercial already available focused on AI Alliance initiatives. Depending on what is measured there are standards out there. For example, tools like GAIA, MLPerf, and other tools to measure F1 score, model performance, accuracy, and drift are available. I am not sure what the alliance is offering at this point. There are some promises made which are yet to be seen. Tools like Fairlearn, AI fairness 360, Accenture Fairness Tool, etc. to measure model fairness are already available. Some popular benchmarking tools include DAWNBench, GLUE, and SQuAD.”

IBM launches latest Heron quantum processor, IBM Quantum System Two

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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IBM launched its IBM Quantum System Two as well as its next-generation quantum processor as the company touts what's possible today with quantum computing.

At the company's IBM Quantum Summit in New York, Big Blue launched IBM Quantum Heron, a 133-qubit processor that's designed to deliver performance and low error rates relative to its predecessor.

Three Heron processors will power the IBM Quantum System Two, which is the company's first modular quantum computer. Modular quantum processors are being pursued by a bevy of industry players as quantum computing is likely to blend with supercomputing at first. Quantum System Two is located in Yorktown Heights, NY.

IBM also outlined its development roadmap to 2033 with a focus on the quality of gate operations, quantum circuits and running at scale.

Constellation Research CEO Ray Wang said:

“Quantum computers need quantum chips and IBM Quantum System Two addresses the need for faster and more precise error mitigation and correction before we achieve mass adoption. The path to the Blue Jay system by 2033 has been one of IBM's most consistent achievements in computing.”

IBM demonstrated how its 127-qubit IBM Quantum Eagle processor can power systems used as scientific tools to solve chemistry, physics and materials problems.

With IBM Quantum Heron, IBM is planning to use a 5x performance boost with improved error rates to build out a fleet of systems. These systems will mostly be available to customers via cloud platforms. In many respects, quantum computing is entering a more accessible era. 

• AWS launches Braket Direct with dedicated quantum computing instances, access to experts
• Will IonQ make quantum computing enterprise relevant in 2025?
• Quantum Computing Platforms
• Quantum Computing Software Platforms
• Quantum Full Stack Players
• Why your quantum computing vendors are going to look familiar

Meanwhile, IBM outlined a Quantum Development Roadmap with future processor plans and systems. Here's the roadmap (larger format).

IBM also detailed a software stack that will use generative AI and watsonx to make quantum software programming easier. IBM is taking Qiskit 1.0 and adding Qiskit Patterns, which enables developers to create code more easily. Combined with Qiskit Runtime and Quantum Serverless and IBM is looking to create an entire quantum computing stack for multiple scenarios.

MongoDB Atlas Vector Search, Atlas Search Nodes generally available

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

MongoDB announced the general availability of MongoDB Atlas Vector Search and MongoDB Atlas Search Nodes in the latest in a series of announcements to enable developers to more easily build generative AI applications.

The general availability of MongoDB Atlas Vector Search and Atlas Search Nodes follows up from a June launch where the company set out to enable AI and large language model (LLM) workloads.

Customers using MongoDB Atlas Vector Search and Atlas Search Nodes include AT&T Cybersecurity, Pathfinder Labs and UKG.

Since that launch MongoDB has had a steady cadence of launches. For instance, at AWS re:Invent, the company said it would integrate MongoDB Atlas Vector Search with Amazon Bedrock and announced it would optimize Amazon CodeWhisperer suggestions for MongoDB developers.

MongoDB steps up generative AI rollout across platform

Those horizontal services are also being complemented by MongoDB's focus on industry specific use cases for healthcare, public sector, manufacturing and automotive as Atlas ramps.

MongoDB, along with companies such as Databricks and Snowflake, are aiming to help developers and enterprises leverage real-time data to build generative AI applications. The argument is that AI applications can't be built efficiently without strong data strategies. Atlas Vector Search and Atlas Search Nodes are designed to give enterprise the capability to search real-time data efficiently.

Key points about MongoDB's two additions:

MongoDB Atlas Vector Search, which is an integrated vector database for MongoDB. Developers can use one API to build generative AI applications across AWS, Microsoft Azure and Google Cloud without duplicating and synchronizing data. MongoDB Atlas Vector Search also enables enterprises to use retrieval-augmented generation (RAG) with pre-trained foundation models.

Atlas Vector Search integrates with models from LangChain, Cohere, OpenAI, LlamaIndex, Hugging Face and Nomic.

MongoDB Atlas Search Nodes provide dedicated infrastructure to manage workloads using MongoDB Atlas Vector Search and Atlas Search independent of operational nodes of the database. This workload isolation performs better at scale and optimizes costs.

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

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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This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly.

UiPath's bets paying off: Here are the key Q3 takeaways

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

UiPath is gaining momentum as high profile partnerships, AI and automation and a focus on large enterprises and industries pay off.

The automation platform company reported better-than-expected third quarter sales of $326 million, up 24% from a year ago, and annual recurring revenue of $1.38 billion. UiPath reported a net loss in the third quarter of $31.54 million, or 6 cents a share. Non-GAAP earnings in the quarter were 12 cents a share.

For the fourth quarter, UiPath projected revenue between $381 million to $386 million.

Constellation Research analyst Holger Mueller said:

"UiPath is on strong growth trajectory, fueled by the need of enterprises to streamline processes. The current AI hype has helped UiPath further and that is slowly but steady growing its way to productivity."

Mueller added that if UiPath Co-CEOs Rob Enslin and Daniel Dines can continue growing revenue faster than costs they'll delight investors.

Indeed, UiPath closed a record number of third quarter deals over $1 million in ARR. Customers with $1 million or more in ARR grew 31% to 264, while customers with $100,000 or more in ARR increased to 1,974.

Here's a look at the key takeaways from UiPath's third quarter results.

UiPath landing more C-level conversations. Enslin, an SAP and Google Cloud alum, knows how to sell and he's revamped UiPath's sales approach to focus on value, platform plays and prioritizing "organizations that have a meaningful runway to invest in enterprise automation over the long-term." Partnerships with SAP and Deloitte are also putting UiPath on the CXO radar earlier in the budget cycle.

"Because we are having these conversations in the boardroom to the C-level suite, we are much earlier in the budget cycles than we previously been," said Enslin.

Simply put, much of UiPath's success can be attributed to a better go-to-market ground game. UiPath has a large enterprise installed base that started with robotics process automation (RPA) and is receptive to UiPath's automation platform and other tools such as document understanding, test suite and process mining.

UiPath adds AutoPilot generative AI to its automation platform: Here's what it means | Every vendor wants to be an automation platform | Constellation ShortList™ Robotic Process Automation

UiPath's SAP partnership showing early returns. The SAP-UiPath partnership is still young, but the combination has yielded some benefits. Notably, UiPath is involved in more transformation discussions with SAP and systems integrators. The primary use case highlighted is the automation of testing in big SAP environments.

SAP buys LeanIX, aims to couple it with Signavio, system transformation

Industry-focused use cases. UiPath is landing large healthcare and federal government deals. Enslin walked through multiple customer references--some named and others not. He cited a large non-profit US health system that has garnered more than $250 million in ROI since starting in 2018 with RPA. That health system is now using document understanding, process mining and centralized automation on UiPath.

UiPath's Enslin noted that UiPath has launched industry playbooks and 70 solution accelerators in its marketplace. Government customers cited were Veterans Affairs, Coast Guard, the IRS, Department of Homeland Security and US Department of Agriculture.

Document Understanding a focus area. UiPath's Document Understanding product line revolves around getting RPA to recognize documents, classify them and process them. The idea is Document Processing can automate paperwork tasks. UiPath has now launched Intelligent Document Processing (IDP), which is an AI-powered feature where generative AI can annotate documents, classify them and extract information to answer questions and summarize.

The term document understanding was mentioned 16 times during UiPath's third quarter earnings conference call. For context, RPA received 7 mentions and process mining had two. There's a reason Document Understanding is getting some play. Dines said:

"Our next-gen IDP permits almost anyone to train Specialized AI models for specific domains and document types, and our internal benchmarking shows that our next-gen IDP experience accelerates model training time by up to 80% from a week to a day for complex scenarios, or down to minutes for simpler forms."

AI and automation are connected and giving UiPath a boost. Enslin said CXOs realize that AI by itself won't generate the returns without automation. He added that in many cases, AI spending is going along with automation conversations.

He said:

"The AI communication that's happening in the market allows us to have meaningful conversations with customers and they see why the platform is relevant. Customers can really look at processes and tasks and understand how to drive value with the automation platform."

Dines added:

"AI plus automation is the thing that drives the biggest outcomes for our customers."

Related:

• Celonis launches Process Intelligence Graph, makes case process enables automation, AI applications
• Celonis acquires Symbio, aims to meld AI, process intelligence in one package
• Microsoft officially enters process mining, automation ring with Power Automate Process Mining

Dell Technologies delivers mixed Q3, but strong demand for AI-optimized servers

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

Dell Technologies' infrastructure unit saw strength in AI-optimized servers, but revenue in the third quarter for the data center group was down 12% from a year ago.

The results highlight the moving parts for data center vendors. AI-optimized gear is selling well as traditional storage and server systems lag. HPE saw similar issues in its quarter.

Overall, Dell Technologies reported third quarter earnings of $1 billion, or $1.36 a share, on revenue of $22.3 billion, down 10% from a year ago. Non-GAAP earnings were $1.88 a share.

Wall Street was expecting Dell Technologies to report third quarter earnings of $1.45 a share on revenue of $22.9 billion.

In a sign of its revenue model transformation, Dell said it had remaining performance obligations of $39 billion. Recurring revenue in the quarter was up 4% from a year ago and deferred revenue was up 7%.

Infrastructure group revenue in the third quarter was $8.5 billion, down 12% from a year ago. Server and networking revenue was $4.7 billion and storage revenue were $3.8 billion.

According to the company, AI optimized servers accounted for 33% of total server orders revenue. Demand was driven by AI focused cloud providers and companies in key verticals. AI optimized server backlog nearly doubled in the third quarter from the second quarter.

Dell's PC business had third quarter revenue of $12.3 billion, down 11% from a year ago. The company said an aging installed PC base and AI-enabled systems from Intel, AMD and Windows on ARM should drive a refresh cycle.

Jeff Clarke, chief operating officer of Dell Technologies, said the company expects fiscal 2025 to show revenue growth as systems are upgraded for generative AI use cases.

• Lead times for generative AI systems extending into 2024

Clarke said in prepared remarks:

"The demand environment for traditional servers improved over the course of the quarter, and demand for AI servers continues to be strong across a wider range of customers. Demand for storage was down, as expected."

He added that Dell Technologies was seeing sequential growth in AI optimized services. Clarke said the company "began to convert more PowerEdge XE9680 backlog into revenue."

"For the quarter, we shipped over half a billion dollars of AI optimized servers, including our XE9680, XE9640, XE8640 and R750 & 760xa servers," said Clarke, who added that "demand remains well ahead of supply."

Clarke was optimistic about AI driving demand for Dell systems.

"AI continues to dominate the technology and business conversation. Customers across the globe are turning their operations upside down to see how they can use generative AI to advance their businesses in meaningful ways. These AI initiatives are being driven at the CEO and board levels. And as a result, we are at the front of a significant TAM expansion."