Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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Salesforce launched the latest installment of generative AI across its clouds with the launch of Sales GPT and Service GPT.

The news follows Salesforce's rollout of AI Cloud this month as well as the Einstein GPT Trust Layer, which enables customers to keep data secure while leveraging large language models. 

What Movies Get Wrong…and Salesforce Gets Right…About AI | Salesforce launches AI Cloud, aims to be abstraction layer between corporate data, generative AI models | Salesforce launches Marketing GPT, Commerce GPT, aims to connect generative AI to ROI

Sales GPT includes the following:

• Auto-generated sales emails personalized for customers based on data.
• Call summaries and transcription for sales calls and follow-up actions.
• A sales assistant to summarize each step of the sales cycle including account resarch, meeting prep and contract drafts.

Service GPT and Field Service GPT will include the following generative AI tools:

• Service replies that are auto-generated with real-time data and personalization.
• Work summaries of service cases and customer engagements.
• Knowledge articles that are auto-generated and updated based on real-time data.
• Mobile work briefings for field service team appointments and summarization of issues.

Sales GPT and Service GPT are expected to be generally available this year.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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Synthetic biology promises to be disruptive across multiple industries because it can be used to develop new biological parts and devices while reengineering existing systems.

DisrupTV caught up with three experts in the field on a recent episode. The roundtable captured some of the promise and peril with synthetic biology, a multidisciplinary field of science focused on reengineering organisms.

Here's a look at the key themes from the DisrupTV discussion and what you need to know.

What is synthetic biology? "Synthetic biology is the ability to design biological systems," said Dr. Megan Palmer, Senior Director for Public Impact at Ginkgo Bioworks and Adjunct Professor of Bioengineering at Stanford University. "We've been able to cut and paste DNA because we know the underlying code biology runs on for the last 50 years or so. Humans have been modifying biology for selective breeding of plants and animals for much longer than that."

"Now scientists and engineers are developing even better tools to be able to read, write, edit and evolve biological systems in ways that are easier, faster, more precise and predictable," said Palmer.

The upshot is that scientists and engineers are unlocking this ability to partner with biology in new ways.

The promise of synthetic biology. Palmer said she considers biology already the most powerful technology on the planet and the ability to program it means "we can use biology to manufacture nearly everything that is currently made with petrochemicals in ways that are more sustainable." Palmer said synthetic biology can impact multiple sectors in the economy such as health, food and manufacturing. There's even potential for data storage using DNA.

8 takeaways from Constellation Research's Healthcare Transformation Summit

Use cases for synthetic biology. Panelists noted a bevy of use cases ranging from space travel to food production to biomanufacturing and sustainability. Dr. Divya Chander, Anesthesiologist, Neuroscientist, and Data Scientist, said synthetic biology could play a role in space travel and "developing astronaut resilience." Chander said:

"We as humans aren't really very good at traveling in space because of microgravity in the radiation environment. There is a possibility of using gene editing tool, which are part of the synthetic biology toolkit, and turn genes on and off to give us more tolerance. Synthetic biology could also enable food production systems in space to improve nutrition and use fewer inputs like water or pesticides. We can even engineer our plants in space to do things like scrub toxins from the environment. CO2 is a big thing for astronauts, but also could do similar things on earth."

Chander said this gene editing could also be good for patients who are undergoing chemotherapy for cancer. With biomanufacturing, synthetic biology could print drugs that are more targeted.

Dr. David Bray, Distinguished Fellow at the Stimson Center and Business Executives for National Security, said sustainability will be a big use case for climate change. "I'm a big believer that the only way we're going to deal with climate change is with synthetic biology," said Bray, who noted that the technology could address the following:

• Clean water.
• Sustainable supply chains.
• Preventing pandemics before they start developing.

"We can organize ourselves in new ways so we can biologize industry instead of just industrializing biology," said Bray.

Chander said other use cases for synthetic biology include:

• Addressing chronic diseases such as cancer and extending longevity.
• Editing biologic machinery to address things like antibiotic resistance.
• Bio manufacturing targeted pharmaceuticals.

Growing the ecosystem and next generation. Palmer said iGEM, a non-profit focused on advancing synthetic biology, education and competition, has gone a long way to developing community in the industry. Palmer said iGEM competitions have encouraged students to design biological machines that are modular to solve problems. "Thousands of students across dozens of countries every year are developing biological innovations that are cool technologies, but also bake in social responsibility, safety and security into designs," said Palmer.

Risks with synthetic biology. Bray said it's promising that biology technologies are being democratized, but there will need to be some guardrails. "You know unleashed and craziness is going to happen, but we have multiple revolutions happening in parallel," said Bray, who added that the combination of AI and synthetic biology could be powerful for good uses and bad. "We are going to need the equivalent of smoke detectors for the biological space," he said. "Technologies like synthetic biology will be a tremendous force for good, but we also need to be ready for when some people try to use it for not so good purposes."

According to the US Government Accountability Office, synthetic biology presents safety and security concerns such as biological and chemical weapons and product tampering, environmental effects and public acceptance.

Ethics will be critical, said Palmer. Synthetic biology will require transparency and permission to use an individual's data. Society as a whole will have to think through ethics and biological data best practices, she said. Data trust will also be a key concept since individuals will be data producers. If you are going to read or write from someone's brain, you'll need full consent. The GAO noted that regulatory frameworks will be needed to address future applications of synthetic biology.

Multiple disciplines needed. The panelists said that synthetic biology will need multiple skills and sits at the intersection of data science, biology as well as ethics experts and technologists. Depending on the use case, industry expertise will also be needed.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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Databricks infused its data platform with generative AI capabilities across its Lakehouse Platform with tools to enable customers to leverage large language models (LLMs), federate and govern data and knowledge engines that learn corporate cultures.

The company outlined the news at its Data + AI Summit. Databricks' news follows announcements from Snowflake and MongoDB designed to land more workloads and enable customers to leverage generative AI. Leading up to Databricks' conference, the company announced the acquisition of MosaicML and launch of Lakehouse Apps. The big takeaway is that data platforms and generative AI capabilities are converging.

Constellation Research analyst Doug Henschen summed up the data platform game: "All three (Snowflake, Databricks and MongoDB) want customers to do as much as possible on their platforms so they are invading each other’s turf. But their original (and still predominant) dance partners are data warehouse for Snowflake, data science for Databricks and developers for MongoDB."

Here's a roundup of Databricks' enhancements to its platform.

• The company said it will make it easier to deploy and manage LLMs with Lakehouse AI additions to offer monitoring and governance for LLM development. Databricks is adding Vector Search, a collection of opensource models, LLM-optimized Model Servicing, MLflow 2.5 with LLM tools and Lakehouse monitoring.
• Lakehouse AI will unify the AI lifecycle from data collection and preparation to model development. Databricks Vector Search will manage and automatically create vectors in Unity Catalog. Databricks AutoML will feature a low-code approach to fine tuning LLMs. And Databricks will curate a list of open-source models in its marketplace.
• Databricks outlined MLflow 2.5, a new release of the Linux Foundation open-source project MLflow. Updates include MLflow AI Gateway, which allows developers to swap out backend models and switch between LLM providers, and MLflow Prompt Tools, a no-code set of visual tools.
• Databricks Lakehouse Monitoring will monitor and manage data and AI assets within Lakehouse.
• LakehouseIQ adds a natural language interface to the Lakehouse Platform. LakehouseIQ uses generative AI to understand company specific jargon, data usage and organizational structure to answer questions within the context of a business. The goal is to democratize data analytics across a corporation. According to Databricks, LakehouseIQ will learn from signals embedded in corporate data including schemas, documents, queries, popularity, lineage, notebooks and dashboards.
• Lakehouse Federation in Unity Catalog will include query federation across data assets and platforms outside of Databricks. Databricks is also offering governance outside of its platform via Unity Catalog.
• Delta Lake 3.0, the latest contribution to Linux Foundation's Delta Lake project, will add Universal Format (UniForm), which will allow data stored in Delta to be read as if it were Apache Iceberg or Apache Hudl.

Doug Henschen's take:

• Databricks is, first and foremost, a platform for data scientists and it’s used by many of its 10,000+ customers as a platform for a significant chunk, if not a majority, of their data. Databricks is doing everything it can do to enable those customers to innovate with their data using AI, ML, and analytics, and it’s doing a great job of it.
• Databricks has spend the last three years building up the warehouse side of its Lakehouse platform, but this year the generative AI tsunami has rightly refocused Databricks on what has always been its greatest strength: data science including ML and AI. It’s very clear to me that Databricks customers are building AI models with Databricks today and there’s a deep well of capabilities that are generally available and now-emerging capabilities that are just becoming GA or are in public preview and very close to becoming GA.
• What was very clear during today’s Databricks keynote is how far along Databricks customers such as JPMorgan Chase, Jet Blue Airways and Rivian are in building innovative ML, AI and even generative AI capabilities using Databricks. A bunch of new enablers were announced today, several of which are already in public preview and are expected to go GA this year.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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Snowflake and Nvidia said they're integrating Snowflake Data Cloud and Nvidia NeMo, a platform for large language models (LLMs), so enterprises can build custom generative AI applications.

The news, outlined during the kickoff of Snowflake Summit 2023, enables Snowflake customers to combine their proprietary data with foundational LLMs within Snowflake Data Cloud. Snowflake said it will host and run NeMo in its Data Cloud and include NeMo Guardrails, which ensures applications line up with business specific topics, safety and security.

Also see: Snowflake launches Snowpark Container Services, linchpin to generative AI strategy

Vendors have been racing to enable enterprises to combine their data with LLMs in a secure way. Salesforce has a trust layer to keep customer data cordoned from LLMs and Oracle is planning a similar service. Enterprise technology buyers have been wary of the compliance and privacy issues with building generative AI applications. Meanwhile, Snowflake rivals MongoDB and Databricks are also targeting LLM data workloads. Databricks doubled down on LLMs with the $1.3 billion acquisition of MosaicML.

With Nvidia NeMo, Snowflake customers can use their accounts to create custom LLMs for chatbots, search and summarization while keeping proprietary data separate from LLMs.

Snowflake CEO Frank Slootman said the partnership with Nvidia will add high performance machine learning and AI to Snowflake's platform. Nvidia CEO Jensen Huang said the partnership with Snowflake will "create an AI factory" for generative AI enterprise applications.

For enterprises, the Snowflake and Nvidia alliance may make it easier to tune custom LLMs for specialized use cases. This approach was outlined recently by Goldman Sachs CIO Marco Argenti.

Snowflake Data Cloud offers industry specific versions across financial services, manufacturing, healthcare, retail and other verticals. With Nvidia, Snowflake's bet is generative AI applications will proliferate across industries.

More:

• Enterprise tech buyers wary of generative AI hype, security
• How 4 CEOs are approaching generative AI use cases in their companies
• Greystone CIO Niraj Patel on generative AI, creating value, managing vendors
• Accenture's Paul Daugherty: Generative AI today, but watch what's next

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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Your quantum computing vendors may look a lot like your cloud, data center and supercomputing providers today as Microsoft, IBM and Intel all had quantum related announcements in recent days.

The big question is whether smaller quantum vendors will be able to deliver the breakthroughs that can propel them to the big leagues. Constellation Research's Shortlists for quantum computing platforms, software and full stack providers include a mix of traditional vendors and startups.

Recent events include:

• Microsoft unveiled Azure Quantum, which included three items. First, Azure Quantum Elements aims to integrate high-performance computing, AI and quantum to accelerate research. Not surprisingly, Microsoft outlined Copilot in Azure Quantum, which takes natural language to chemistry and material science problems. And finally, Microsoft outlined a roadmap to build a quantum supercomputer that includes a new metric (rQOPS or Reliable Quantum Operations Per Second) and plans for a hardware protected qubit. "Our goal is to compress the next 250 years of chemistry and materials science progress into the next 25," said Microsoft CEO Satya Nadella, who acknowledged the effort was just the beginning.
• Intel made its new 12-qubit quantum chip called Tunnel Falls available to university and federal research labs. Intel added that it is collaborating with the Laboratory for Physical Sciences (LPS) at the University of Maryland and College Park's Qubit Collaboratory (LQC) on quantum computing research. Nvidia also has staked out quantum as a growth market because there will be quantum accelerators too. 
• IBM Quantum and UC Berkeley outlined the progress being made for complex physical simulations on the 127-qubit IBM Quantum Eagle processor. The goal of the project was to reduce the noise in quantum computing and reduce errors. As noted by Constellation Research analyst Holger Mueller, IBM has a mature ecosystem surrounding its quantum efforts.

Now it's not like startups are being lapped. IonQ last week announced its IonQ Forte system was commercially available. IonQ has a partnership with Dell Technologies and is available on all three major cloud providers (AWS, Google Cloud, Azure). The company just raised its 2023 bookings growth to $45 million to $55 million. In its first quarter, IonQ had revenue of $4.3 million.

But overall, it's telling that quantum computing seems to be driven by established enterprise technology players with the biggest R&D budgets. It's really hard to sneak up on big tech these days.

Mueller said the verdict is still out on whether the big enterprise tech players will all pivot to quantum. CIOs could explore Quantinuum, formed by the combination of Honeywell Quantum and Cambridge Quantum, technically isn't an IT vendor. He said:

"Clear trend: It will be the first enterprise tech that will be practially only availalble in the cloud. With that every cloud vendor needs to play to remain relevant. But we are still in basic tech phase. Who will win? It is VHS vs Betamax." 

Short version: You're not quite ready to buy into quantum computing at scale just yet.

Kirk Bresniker, Hewlett Packard Labs Chief Architect and HPE Fellow, said in a tech talk at HPE Discover that quantum computing will require decades of hard engineering work to be mainstream. However, quantum computing will have role in a hybrid supercomputing approach.

"HP Labs is here to partner and apply engineering expertise to make this process real," said Bresniker. He acknowledged that quantum computing is still early in its development--akin to vacuum tubes in old classical computers--but can accelerate. "We're looking to partner to give enterprises a better set of information so they can reason over this quantum future. You want to make reasoned investments in these technologies over time," he said.

His architecture slide is worth checking out from a vision perspective.

Bresniker said HPE is betting that supercomputing will evolve with an architecture that includes CPUs, GPUs, various accelerators and quantum computing to tackle problems.

For now, quantum computing is worth experiments and use cases in select industries. TCS recently noted how it is using IBM Quantum infrastructure for financial advisor scenarios. Financial services and life sciences are obvious areas for quantum computing.

For now, quantum computing is clearly in the press release stage, but there seems to be consensus around the following:

• Quantum computing will likely be consumed through the cloud.
• Select industries should explore use cases.
• Key metrics on how to measure efficiency and performance are being debated.
• Quantum computing will be part of what's emerging as a hybrid supercomputing approach.
• Projections about how quantum computing will scale are often based on assumptions of breakthroughs. However, you can't predict breakthroughs.

In the meantime, enjoy the parade of quantum computing announcements flying by.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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Databricks said it will acquire MosaicML, which is a generative AI platform specializing in large language models (LLMs), for $1.3 billion.

The news lands as data platforms such as Snowflake, Databricks and MongoDB race to provide ways for enterprises to build their own generative AI models while keeping corporate data secure. The data platform game is focused on fast training of LLMs and models with strong data governance.

Related:

• MongoDB launches Atlas Vector Search, Atlas Stream Processing to enable AI, LLM workloads

MosaicML is best known for its MPT LLMs. For instance, MosaicML has more than 3.3 million downloads of MPT-7B and MPT-30B LLMs.

Databricks will take MosaicML and integrate its models into Databricks Lakehouse. According to Databricks, MosaicML will enable customers to train LLMs in hours not days and for "thousands of dollars, not millions."

Constellation Research analyst Doug Henschen said:

“Databricks has spent the last few years building up the house side of its Lakehouse platform, but the company’s beginnings were as a data science platform. It can’t afford to lose its distinction and differentiation as a platform for data science, so the acquisition of MosaicML makes complete sense. What’s more, it’s a good fit in terms of company culture and location.”

The $1.3 billion price tag is inclusive of retention packages. Databricks said it expects the entire MosaicML team to join the company. Retaining MosaicML's team will be critical as Databricks integrates and scales the combined platform.

In a blog post MosaicML said it started talking to Databricks about partnerships, but it became clear the effort would scale better combined. MosaicML said:

“Generative AI is at an inflection point. Will the future rely mostly on large generic models owned by a few? Or will we witness a true Cambrian explosion of custom AI models that are built by many developers and companies from every corner of the world? MosaicML’s expertise in generative AI software infrastructure, model training, and model deployment, combined with Databricks’ customer reach and engineering capacity, will allow us to tip the scales in the favor of the many.”

• Enterprise tech buyers wary of generative AI hype, security
• How 4 CEOs are approaching generative AI use cases in their companies
• Goldman Sachs CIO Marco Argenti on AI, data, mental models for disruption
• Greystone CIO Niraj Patel on generative AI, creating value, managing vendors
• Accenture's Paul Daugherty: Generative AI today, but watch what's next

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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IBM said it has acquired Apptio, which makes IT management and optimization software, for $4.6 billion. Big Blue said the move will bolster its IT automation offerings.

Vista Equity Partners bought Apptio in 2018 for $1.94 billion.

IBM said it will combine Apptio with its IT automation software including Turbonomic, Instana and AIOps and Watsonx AI platform. Enterprises are increasingly looking to automate their IT operations and maximize financial returns (FinOps). IBM said that Apptio will bring anonymized IT spending data to provide insights.

Big Blue noted that it is at the early stages of integrating Apptio and developing roadmaps. 

Constellation Research CEO Ray Wang said:

"It’s sign of the times. Companies want to know how to manage their cloud budgets and Apptio is one of the tools with cost management tools and technology portfolio management or FinOPs. IBM is betting that customers will want to buy software to manage cloud costs and tech spending."

Apptio has more than 1,500 customers and has integrations with multiple IT vendors including Amazon Web Services, Microsoft Azure, Google Cloud Platform, ServiceNow, Salesforce, Oracle, SAP and others.

IBM launches Watsonx, an AI platform with open source models, governance

With the Apptio Purchase, IBM will own three core SaaS offerings including.

• ApptioOne, which tracks hybrid cloud spend management and optimization.
• Apptio Cloudability, which provides public cloud spend management and optimization visibility.
• Apptio Targetprocess, which aligns IT projects with business outcomes.

IBM said the plan is to scale Apptio's products via Red Hat, IBM's portfolio of software and AI products and IBM Consulting. 

Here's a look at Apptio's platform. 

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.  Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…...

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MongoDB added Atlas Vector Search and Atlas Stream Processing to its MongoDB Atlas platform along with other enhancements as it aims to be the top choice for data application developers.

The news, announced at its MongoDB.local NYC developer conference, highlights the race for enterprise developers looking to create modern applications that can readily incorporate generative AI capabilities at scale.

MongoDB's announcements come days after Databricks launched Lakehouse Apps to broaden its development platform ambitions. In addition, Snowflake will unveil updates at its Snowflake Summit next week. Snowflake CEO Frank Slootman last month promised "significant product announcements" at Snowflake Summit.

Dev Ittycheria, CEO of MongoDB, said during a keynote that developers spend most of their time working with data instead of creating software. Multiple clouds, endpoints and data stores have also made development more complicated. Streaming data technologies are also heterogenous. "AI is about building smarter and more intelligent applications," said Ittycheria. "There has been an explosion of AI companies running and building apps on MongoDB. We believe there are 1,500 companies building AI workloads on MongoDB today."

MongoDB Atlas Vector Search will bring generative AI capabilities to the Atlas platform by bringing forward highlight relevant information retrieval and personalization.

Doug Henschen, analyst at Constellation Research, put Atlas Vector Search in context:

"Vector Search isn't a generative AI capability on its own, it's an enabler for companies interested in developing their own generative AI capabilities. In announcing this feature, which is entering public preview, Mongo DB is joining a group of leading-edge data platform companies that have recently made, or are about to make, vector-search-related announcements."

In addition, MongoDB Atlas Stream Processing will surface high-velocity streams of complex data. Atlas Stream Processing, which is in private preview, will enable enterprises to leverage large language models (LLMs) and process streams of real-time data in one unified experience.

Henschen said Atlas Stream Processing is a key addition for MongoDB. He said:

"Atlas Stream Processing is the most important announcement at this week’s event, with Vector Search being the second most important announcement in my book. Low-latency workloads and requirements are only becoming more prevalent, so MongoDB really had to step up on this front if it is to live up to the company’s billing as a "developer data platform." Rival data platforms associated with analytics, such as Snowflake and Databricks, have already addressed real-time needs, so MongoDB is filling a competitive gap."

Vector Search and Stream Processing are likely to appeal to developers building AI-based applications. MongoDB said Beamable, Pureinsights, Anywhere Real Estate and Hootsuite are building next-gen applications with the new Atlas capabilities.

To round out the Atlas updates, MongoDB also added Atlas Search Nodes with dedicated resources for search workloads, efficiency improvements with MongoDB Time Series collections and new Atlas Data Federation for queries and isolating workloads on Microsoft Azure.

For MongoDB, the race to build enterprise-grade generative AI apps is an opportunity to grow its multi-cloud developer data platform. Although enterprises aren't scaling LLMs and generative AI applications yet, the interest is there.

• Enterprise tech buyers wary of generative AI hype, security
• How 4 CEOs are approaching generative AI use cases in their companies
• Goldman Sachs CIO Marco Argenti on AI, data, mental models for disruption
• Greystone CIO Niraj Patel on generative AI, creating value, managing vendors
• Accenture's Paul Daugherty: Generative AI today, but watch what's next

To capitalize on the generative AI and LLM interest, MongoDB is looking to address the following with Atlas Vector Search:

• Provide the flexibility to store and process different types of data. LLMs require data in the form of vectors to represent data types such as text, images and audio.
• Store vectors so LLMs can use them without needing a specialized database that lacks integration with existing technology stacks.
• Enable developers to deploy new workloads such as semantic search, text and image search and personalized product recommendations in one platform.
• Provide developers with the ability to augment pre-trained generative AI models with their own data.
• Integrate frameworks such as open source LangChain and LlamaIndex and use them so developers can access LLMs from partners.

With Atlas Stream Processing, MongoDB is looking to do the following:

• Provide developers with real-time streaming data from IoT devices, browsing and inventory feeds to create real-time experience and optimize on the fly.
• Leverage streaming data without specialized programming languages, APIs and drivers.
• Give developers one interface to extract insights from streaming data across multiple data types, connectors and technologies.

While Atlas Vector Search and Atlas Stream Processing were the headliners, MongoDB had a series of other launches. Here's the breakdown.

• MongoDB Atlas Search Nodes give developers dedicated resources so enterprises can scale search workloads independent of the database.
• MongoDB Time Series collections provide options to modify data that has already been ingested. Time Series collections will also improve storage efficiency and query speeds.
• The company is adding Microsoft Azure support to MongoDB Atlas Online Archive and Atlas Data Federation to go with Amazon Web Services. Support for Microsoft Azure Blog Storage means MongoDB customers can work with Azure and AWS datasets.
• MongoDB launched MongoDB Relational Migrator, a tool that streamlines the process of migrating applications and legacy databases.
• Google Cloud Vertex AI LLMs will integrate with MongoDB Atlas so developers can use Google Cloud foundational models across MongoDB Atlas Vector Search.
• The company outlined MongoDB Atlas for Industries, which is a set of integrated tools for vertical use cases. The first industry targeted by MongoDB Atlas for Industries is financial services.
• MongoDB also outlined additional programming language support for deploying MongoDB Atlas on AWS, the Kotlin Driver for MongoDB for server-side applications and more streamlined functionality for Kubernetes and Python.

Bottom line: MongoDB sent the message that developers can leverage the Atlas platform for generative AI capabilities. Henschen said:

"I think the 5% of companies that are innovators and the next 20% to 25% of companies that are fast followers will be the ones that are most interested in these features. It promises to make MongoDB stickier for developers at these companies as they now know they can turn to MongoDB, a tool they already know and love, for help in developing generative AI capabilities."