NoSQL database supplier Couchbase is reporting 20 percent Q2 revenue growth, disappointing analysts who hoped for more.
Revenues of $51.6 million on the quarter, ended July 31, 2024, were above its mid-point guidance and 20 percent more than last year’s Q2. It made a net loss of $19.9 million compared to the year-ago of $20.6 million net loss. The company has been losing money since its June 2021 IPO, and the business has emphasized top-line growth over profitability. Couchbase subscription revenues were $49.3 million, 20 percent higher year-on-year, and annual recurring revenue (ARR) was $214 million, 18.4 percent up on the year.
Matt Cain
President and CEO Matt Cain said: “I’m pleased with our hard work and execution in the quarter. We delivered revenue and operating loss results that exceeded the high end of our outlook, generated strong new business and new logos, and saw a meaningful increase in our Capella mix.” Capella is Couchbase’s NoSQL database service in AWS, Azure, and GCP. The company has just added vector search to the AWS version.
William Blair analyst Jason Ader told subscribers: “Capella continues to be one of the main drivers of growth for Couchbase as Capella now represents 13.5 percent of total ARR and Capella customers represent 31 percent of the installed base. In the second quarter, net new ARR for Capella more than doubled sequentially, with Couchbase posting its single-largest Capella land (mid-six-figure deal) in company history.
“ARR only met guidance and came in slightly below consensus, attributed to large deal timing and higher-than-expected customer churn (two major accounts churned – one expected and the other unexpected and anomalous) and downsell in the quarter (blamed on macro).”
Couchbase, founded in 2011, is one of several NoSQL database suppliers. Others include Amazon’s DynamoDB, HBase, MongoDB, and Redis. Its revenues have grown pretty consistently at 20 percent or so a quarter since its June 2021 IPO.
The losses have been contained between $15 million and $21 million a quarter as revenue has jumped quarter on quarter, apart from a seasonal Q1 dip. When this high growth rate starts to tail off, we can expect management to switch focus from growth to profitability.
Couchbase said it expects next quarter’s revenues to be $50.7 million +/- $400K, a 10.7 percent increase at the midpoint and below its usual growth rate. Couchbase’s results statement said: “The guidance provided above is based on several assumptions that are subject to change and many of which are outside our control. If actual results vary from these assumptions, our expectations may change. There can be no assurance that we will achieve these results.”
Ader said: “Management maintained full-year fiscal 2025 ARR guidance while raising full-year fiscal 2025 revenue guidance by $0.6 million at the midpoint (15 percent year-over-year revenue growth). The full-year ARR guide implies a steep ramp in net new ARR in the fourth quarter ($18 million versus $6 million in the third quarter).”
In Ader’s view, Couchbase’s “valuation keeps us positive on the risk/reward equation for the stock; at the same time, we recognize that some investors may worry that management did not de-risk numbers enough for the second half, especially in view of the mediocre first-half results.”
What is the impact of hyper-personalisation in banking, and what are the use cases that will enhance the overall customer experience?
While front office modernisation will boost revenue, back office modernisation will decrease costs. How can organisations leverage AI to enable both of these drivers?
Banks need to consider end-to-end architecture when it comes to modernisation, so how can organisations approach application transformation in the most effective way possible?
The AI revolution in financial services is upon us. Traditional AI and generative AI have a plethora of use cases for financial institutions – from improved fraud prevention to streamlined operations – but one of the main opportunities it offers lies in the impact on customer experience that these technologies can have. In an era where customer expectations are higher than ever before, AI technologies enable banks to offer tailored, hyper-personalised experiences to their customers, improving both customer satisfaction, lengthening the customer lifecycle, and increasing the overall revenue.
Yet reaping the front office benefits of hyper-personalisation is not possible without the back office. How can financial institutions decrease their reliance on legacy systems in order to leverage the opportunities of AI? Application modernisation is necessary to become agile enough to tailor customer experience and react to an increasingly nimble market. So how can banks effectively modernise without disrupting operations?
Sign up for this Finextra webinar, hosted in association with MongoDB and Microsoft, to join our panel of industry experts who will discuss how financial institutions can achieve hyper-personalised service offerings by leveraging AI and smart application modernisation strategies.
Arizona State Retirement System increased its position in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 0.6% during the second quarter, according to the company in its most recent 13F filing with the SEC. The firm owned 19,981 shares of the company’s stock after buying an additional 129 shares during the quarter. Arizona State Retirement System’s holdings in MongoDB were worth $4,994,000 at the end of the most recent quarter.
Other institutional investors have also added to or reduced their stakes in the company. Transcendent Capital Group LLC acquired a new position in MongoDB during the 4th quarter worth approximately $25,000. MFA Wealth Advisors LLC acquired a new position in MongoDB during the 2nd quarter worth approximately $25,000. J.Safra Asset Management Corp raised its stake in MongoDB by 682.4% during the 2nd quarter. J.Safra Asset Management Corp now owns 133 shares of the company’s stock worth $33,000 after buying an additional 116 shares during the period. Hantz Financial Services Inc. acquired a new position in MongoDB during the 2nd quarter worth approximately $35,000. Finally, YHB Investment Advisors Inc. acquired a new position in MongoDB during the 1st quarter worth approximately $41,000. 89.29% of the stock is owned by institutional investors and hedge funds.
Insider Buying and Selling
In related news, CAO Thomas Bull sold 138 shares of the stock in a transaction on Tuesday, July 2nd. The stock was sold at an average price of $265.29, for a total value of $36,610.02. Following the sale, the chief accounting officer now owns 17,222 shares in the company, valued at $4,568,824.38. The sale was disclosed in a document filed with the Securities & Exchange Commission, which is available at this link. In related news, CAO Thomas Bull sold 138 shares of the stock in a transaction on Tuesday, July 2nd. The stock was sold at an average price of $265.29, for a total value of $36,610.02. Following the sale, the chief accounting officer now owns 17,222 shares in the company, valued at $4,568,824.38. The sale was disclosed in a document filed with the Securities & Exchange Commission, which is available at this link. Also, CFO Michael Lawrence Gordon sold 1,569 shares of the firm’s stock in a transaction dated Tuesday, July 2nd. The shares were sold at an average price of $265.29, for a total value of $416,240.01. Following the sale, the chief financial officer now owns 81,942 shares in the company, valued at approximately $21,738,393.18. The disclosure for this sale can be found here. Over the last quarter, insiders have sold 32,179 shares of company stock worth $8,063,279. Corporate insiders own 3.60% of the company’s stock.
MongoDB Trading Down 2.8 %
NASDAQ:MDB opened at $281.88 on Monday. The company has a market cap of $20.68 billion, a P/E ratio of -100.31 and a beta of 1.15. The stock’s fifty day moving average price is $253.05 and its two-hundred day moving average price is $308.58. MongoDB, Inc. has a 1 year low of $212.74 and a 1 year high of $509.62. The company has a debt-to-equity ratio of 0.90, a current ratio of 4.93 and a quick ratio of 4.93.
MongoDB (NASDAQ:MDB – Get Free Report) last issued its quarterly earnings data on Thursday, August 29th. The company reported $0.70 earnings per share (EPS) for the quarter, topping analysts’ consensus estimates of $0.49 by $0.21. MongoDB had a negative return on equity of 15.95% and a negative net margin of 12.08%. The company had revenue of $478.11 million for the quarter, compared to the consensus estimate of $465.03 million. During the same quarter last year, the business posted ($0.63) earnings per share. The firm’s revenue was up 12.8% compared to the same quarter last year. As a group, analysts expect that MongoDB, Inc. will post -2.67 EPS for the current year.
Analysts Set New Price Targets
A number of research firms recently weighed in on MDB. Stifel Nicolaus upped their price target on MongoDB from $300.00 to $325.00 and gave the stock a “buy” rating in a research note on Friday, August 30th. Truist Financial increased their price objective on MongoDB from $300.00 to $320.00 and gave the stock a “buy” rating in a research note on Friday, August 30th. JMP Securities reissued a “market outperform” rating and issued a $380.00 price objective on shares of MongoDB in a research note on Friday, August 30th. Piper Sandler increased their price objective on MongoDB from $300.00 to $335.00 and gave the stock an “overweight” rating in a research note on Friday, August 30th. Finally, Canaccord Genuity Group lowered their price objective on MongoDB from $435.00 to $325.00 and set a “buy” rating for the company in a research note on Friday, May 31st. One investment analyst has rated the stock with a sell rating, five have assigned a hold rating and twenty have assigned a buy rating to the company. According to data from MarketBeat.com, the stock has a consensus rating of “Moderate Buy” and an average price target of $337.56.
MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.
Further Reading
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New York, New York–(Newsfile Corp. – September 8, 2024) – WHY: Rosen Law Firm, a global investor rights law firm, reminds purchasers of securities of MongoDB, Inc. (NASDAQ: MDB) between August 31, 2023 and May 30, 2024, both dates inclusive (the “Class Period”), of the important September 9, 2024 lead plaintiff deadline.
SO WHAT: If you purchased MongoDB securities during the Class Period you may be entitled to compensation without payment of any out of pocket fees or costs through a contingency fee arrangement.
WHAT TO DO NEXT: To join the MongoDB class action, go to https://rosenlegal.com/submit-form/?case_id=27182 or call Phillip Kim, Esq. toll-free at 866-767-3653 or email case@rosenlegal.com for information on the class action. A class action lawsuit has already been filed. If you wish to serve as lead plaintiff, you must move the Court no later than September 9, 2024. A lead plaintiff is a representative party acting on behalf of other class members in directing the litigation.
WHY ROSEN LAW: We encourage investors to select qualified counsel with a track record of success in leadership roles. Often, firms issuing notices do not have comparable experience, resources or any meaningful peer recognition. Many of these firms do not actually litigate securities class actions, but are merely middlemen that refer clients or partner with law firms that actually litigate the cases. Be wise in selecting counsel. The Rosen Law Firm represents investors throughout the globe, concentrating its practice in securities class actions and shareholder derivative litigation. Rosen Law Firm has achieved the largest ever securities class action settlement against a Chinese Company. Rosen Law Firm was Ranked No. 1 by ISS Securities Class Action Services for number of securities class action settlements in 2017. The firm has been ranked in the top 4 each year since 2013 and has recovered hundreds of millions of dollars for investors. In 2019 alone the firm secured over $438 million for investors. In 2020, founding partner Laurence Rosen was named by law360 as a Titan of Plaintiffs’ Bar. Many of the firm’s attorneys have been recognized by Lawdragon and Super Lawyers.
DETAILS OF THE CASE: According to the lawsuit, throughout the Class Period, defendants created the false impression that they possessed reliable information pertaining to MongoDB’s projected revenue outlook and anticipated growth while also minimizing risk from seasonality and macroeconomic fluctuations. In truth, MongoDB’s sales force restructure, which prioritized reducing friction in the enrollment process, had resulted in complete loss of upfront commitments; a significant reduction in the information gathered by their sales force as to the trajectory for the new MongoDB Atlas enrollments; and reduced pressure on new enrollments to grow. Defendants misled investors by providing the public with materially flawed statements of confidence and growth projections which did not account for these variables. When the true details entered the market, the lawsuit claims that investors suffered damages.
No Class Has Been Certified. Until a class is certified, you are not represented by counsel unless you retain one. You may select counsel of your choice. You may also remain an absent class member and do nothing at this point. An investor’s ability to share in any potential future recovery is not dependent upon serving as lead plaintiff.
Version 5.0 of the open-source NoSQL distributed database Apache Cassandra is generally available. This new version offers users improved performance, integration of GenAI functionality, and increased data efficiency.
Apache Cassandra 5.0 is the first major release since the introduction of version 4.0 in 2021. The open-source database is a collaborative project of various stakeholders and is supported by major vendors, including DataStax, and offered as managed database solutions within large (public) cloud environments.
The key feature of Apache Cassandra is the large-scale distributed NoSQL database. This database provides companies with fully synchronized nodes in different locations.
The introduction of a new indexing method greatly enhances this distributed functionality in version 5.0. Thanks to Storage Attached Indexes (SAI), developers are no longer bound by strict data models.
Previously, companies had to specify how the data model was built. In version 5.0 of Apache Cassandra, the requirements are loosened with SAI, allowing developers to build a data model, modify it, and easily add an index to make the data model work differently.
The introduction of SAI also replaces the original Secondary Index functionality in Apache Cassandra.
Vector dataype and index feature
This new feature is accompanied by the introduction of vector datatype and index functionality in Apache Cassandra 5.0. This functionality is designed for so-called Approximate Nearest Neighbor searches.
According to the open-source project, this also lays the foundation for developing advanced AI and ML applications. For these applications, developers can better combine Apache Cassandra’s scaling and distribution functionality with advanced search capabilities.
Denser data intensity per node
Apache Cassandra 5.0 also offers a new feature: the “Unified Compaction” strategy. This feature increases data density per node and automatically adjusts data density as clusters grow, improving operational efficiency, especially in large-scale deployments of the NoSQL database.
More data per node means users ultimately need less hardware for a large-scale deployment of the open-source database, lowering operational costs.
Other updates
Further improvements in Apache Cassandra 5.0 include introducing two new data structures: trie memtables and trie SSTables. These ensure that data structures are better synchronized, leading to faster processing and better overall database performance.
By synchronizing data structures from the end user to the (storage) disk, the database performs fewer unnecessary tasks, leading to better performance.
Support for Java Development Kit (JDK) 17 should improve performance by up to 20 percent in some cases, mainly through improved memory management.
Apache Cassandra 5.0 is now available. Work is underway on version 5.1, which has been in development since November. The introduction of full Atomicity, Consistency, Isolation, Durability (ACID) transactions is also planned for the future.
The global Global NoSQL Database market is poised for significant growth between 2024 and 2031, driven by evolving industry dynamics and increasing demand across various sectors. This comprehensive research report, spanning 220 pages, offers in-depth insights and analysis into the market size, trends, and forecasts, segmented by regions, products, applications, and end-users. The report aims to provide valuable information to stakeholders, vendors, and participants within the Global NoSQL Database market, facilitating informed business decisions, strategic planning, and market expansion. With a projected compound annual growth rate (CAGR) expected to be impressive during the forecast period, the Global NoSQL Database market represents a lucrative opportunity for businesses and investors alike.
The report segments the Global NoSQL Database market by region, including North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa. Each region’s market dynamics are explored, highlighting key drivers, challenges, and opportunities. For instance, North America remains a dominant player due to advanced technological adoption and high consumer awareness, while Asia-Pacific is anticipated to witness the fastest growth due to rapid industrialization, urbanization, and rising disposable incomes. Europe’s market is characterized by stringent regulations and a strong focus on sustainability, which shapes the product offerings and strategies of companies operating within this region. The analysis of regional trends provides a holistic view of the global landscape, allowing businesses to tailor their strategies to specific geographic markets.
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The report also covers end-user segmentation, offering insights into the various customer groups that utilize Global NoSQL Database products. Understanding the preferences and purchasing behavior of end-users is critical for companies looking to expand their market presence and enhance customer satisfaction. The report identifies key trends influencing end-user demand, such as the growing emphasis on sustainability, increasing reliance on technology, and the shift toward personalized solutions. By analyzing the end-user landscape, businesses can develop targeted strategies to attract and retain customers, driving growth and profitability in the Global NoSQL Database market.
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With data being critical to the success of artificial intelligence (AI) initiatives, database suppliers such as MongoDB have been doubling down on efforts to help organisations unlock the full potential of AI.
In an interview with Computer Weekly, Boris Bialek, field chief technology officer for industry solutions at MongoDB, sheds light on those efforts, highlighting the database’s flexible document model and real-time data streaming capabilities while addressing data sovereignty requirements.
He also discussed MongoDB’s AI-focused initiatives such as the MongoDB AI applications programme (MAAP) that provides practical guidance and resources for organisations ready to take the next step in their AI journey, the challenges posed by legacy systems and the importance of developer-friendly tools that cater to local languages and empower businesses to build applications tailored for the ASEAN market.
Editor’s note: This interview was edited for clarity and brevity.
Talk to us about the work MongoDB is doing to support ASEAN organisations in their AI journey
It’s important to understand that we’re still in the early stages of AI adoption. Many clients are experimenting, but nonetheless we see the first production implementations happening as well. For us, and this may sound self-serving, data is core to achieving good AI results. It’s a classical thing from the analytics days – if you have garbage data in, you get garbage analytics out. AI is not any different – if you don’t get good data integrated, you get hallucinations and wrong information.
What we have at MongoDB is the document model and we added Vector Search capabilities last year. This allows us to accumulate in the same data set, operational data, metadata, Vector Search data and additional data generated from interactions, which we can put into context with an LLM [large language model] and connect to AI applications in real time. This is our claim to fame and how we differentiate ourselves.
We have a large European retail customer that is storing all their product information in 23 different languages from 15 data sources in one big database, available in real-time. People can start asking questions about calorie counts, for example, and it’s a great solution for consumers who would otherwise have to look for the information on the internet. We also do the same for banks in client relationship management.
We’re seeing more organisations in ASEAN with the mobile-first mentality and aggressively leveraging our capabilities. They’re more willing to try things out, while those in Europe are probably the most conservative, and North America is somewhere in the middle Boris Bialek, MongoDB
We spend a lot of time using AI internally as well. We have millions of databases in operation on our cloud platform, and we’re using AI to optimise them for our clients.
Another area is modernisation – 30% of legacy infrastructure accounts for 80-90% of the cost. During our last earnings call, our CEO announced that a global insurance company is working on moving to our platform in 56 countries.
I’d like to dive into the recent generative AI (GenAI) announcements from MongoDB, particularly the MAAP which aims to help companies build GenAI applications faster and easing some of their challenges. But many companies are still experimenting with GenAI and haven’t scaled up their deployments. What’s your take on that?
You’re right on point. MAAP is there to help them move from experimentation to production. There’s always this question of how to start with dozens of vendors knocking on their doors with different offerings, whether it’s LangChain or Llama, so clients get a little confused. We deliver a more curated approach with certified and qualified reference architectures. We help them with data integration and integration with various components like an embedder and a LLM gateway.
We are also working on a book to define use cases to help clients jumpstart their first projects. There’s a lot of interest in ASEAN around that because many companies, as you pointed out, are experimenting. They’ve come to the point when they’ve experimented for a year and are asking about where to go from there. That’s where MAAP comes in.
What about streaming data?
We’ve just announced our Atlas streaming product which allows you to stream data into the database, process the data in real-time and take immediate action. For example, let’s say you’re paying for something at a store and the cashier system notes that you’re a special client and offers you a two-for-one deal. That would be a real-time AI interaction.
But to do that, the system needs to know you’re at the cashier, what you’re buying and your interests. This is where MongoDB’s real-time capabilities come in. We can embed data, vectorise the information and build real-time vectors. We can even have multiple vectors inside the same document about you as a consumer, your purchases and your loyalty status, and ask an LLM for real-time inputs based on those vectors.
For that to work, real-time data streaming is key. A lot of AI people came from the analytics and enterprise data warehouse world, and this is completely different now that you can have a database that helps you make a decision in 50 milliseconds.
How is MongoDB addressing the need for AI governance?
What we are mostly seeing right now is data governance, specifically data lineage that tells you where a vector came from. For example, for a bank doing credit scoring to say they have 26 databases, 12 copies of the databases and data from an ETL [extract, transform, load] process sends a bad message to regulators.
With MongoDB, they can bring the data together in one document. Inside the document, we can use embedders and build a vector out of that. So, if somebody asks how the vector was generated, we can say this is the inference model XYZ that uses the following data sources. This removes a lot of ambiguity, especially for those in the banking and insurance space.
How is MongoDB addressing the needs of customers with data sovereignty and data residency requirements?
We are probably the database with the most coverage around the globe. We’ve also announced that we’ll bring MongoDB Atlas Search and Atlas Vector Search on-premise. When you have data lineage, the next requirement is data residency and sovereignty.
In Europe, we’re already working with regional sovereign cloud providers like OVHcloud, StackIT and Bundescloud, the German government cloud. We’ve worked in multiple countries with these kinds of systems, and we are very proud of it. We can also do on-premises deployments for clients where they get a self-managed and controlled environment.
One of the challenges in the APAC region is the delayed availability of new services from global technology suppliers. Does MongoDB have a release cadence for new capabilities?
Everything goes out immediately. When we rolled out Vector Search, we had it immediately available globally. The health ministry in Indonesia were very happy to see that they had Vector Search capabilities on MongoDB Atlas. With in-country support for the Atlas platform, it’s easy for customers to add and switch on a feature like Vector Search without redoing the security qualification.
There’s always this question of how to start with dozens of vendors knocking on their doors with different offerings, whether it’s LangChain or Llama, so clients get a little confused. We deliver a more curated approach with certified and qualified reference architecture Boris Bialek, MongoDB
It’s a different discussion if you use a third-party solution or you have LLM integrations outside your country. This is also why we see clients moving to local LLMs, with people training their own models in local languages. And MongoDB works with all of these components out of the box. We don’t need to build anything special because of the document model.
Most people don’t realise that most of the work we do is always multilingual under the hood. We have a developer tool called Compass where you can use natural language to ask questions or perform tasks like building a function to show the top 10 users of a product. You can do this in Bahasa Indonesia, Mandarin and English, and it gives you code afterwards. You can then add the code into your code base. This is where MongoDB is very open to developers.
What challenges do you see customers facing in Asia that might put them at a disadvantage compared to those in other parts of the world in terms of leveraging MongoDB’s capabilities?
We’re seeing more organisations in ASEAN with the mobile-first mentality and aggressively leveraging our capabilities. They’re more willing to try things out, while those in Europe are probably the most conservative, and North America is somewhere in the middle. When I see what some of our clients are building in ASEAN, to be honest, they’re not behind. Take AirAsia, for example. The environment for their consumer app and consumer interactions was built with MongoDB technology, and they have one of the leading apps, in my opinion, globally.
I think Europe is the most behind right now, although it’s ahead with regulations, so we should give them credit on that as well. With the new Dora law, there are very rigid rules around usage of the data of individuals and so on.
But on the application side, what we see in ASEAN is very positive. People see IT as a means of doing business and that helps us as well. There’s a lot going on and people are very focused on moving out of legacy systems to enhance customer experience because the market is very competitive.
Emerald Advisers LLC lifted its holdings in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 87.3% during the second quarter, according to the company in its most recent Form 13F filing with the Securities and Exchange Commission (SEC). The firm owned 7,391 shares of the company’s stock after purchasing an additional 3,444 shares during the quarter. Emerald Advisers LLC’s holdings in MongoDB were worth $1,847,000 at the end of the most recent quarter.
Several other institutional investors have also modified their holdings of MDB. Transcendent Capital Group LLC acquired a new stake in shares of MongoDB in the 4th quarter valued at about $25,000. MFA Wealth Advisors LLC acquired a new stake in MongoDB in the 2nd quarter valued at approximately $25,000. J.Safra Asset Management Corp lifted its stake in MongoDB by 682.4% in the 2nd quarter. J.Safra Asset Management Corp now owns 133 shares of the company’s stock valued at $33,000 after acquiring an additional 116 shares in the last quarter. Hantz Financial Services Inc. acquired a new position in MongoDB during the 2nd quarter worth approximately $35,000. Finally, YHB Investment Advisors Inc. acquired a new stake in shares of MongoDB in the first quarter valued at approximately $41,000. Institutional investors own 89.29% of the company’s stock.
MongoDB Price Performance
MDB traded up $5.53 on Monday, reaching $287.41. The stock had a trading volume of 383,388 shares, compared to its average volume of 1,510,465. The company has a current ratio of 5.03, a quick ratio of 5.03 and a debt-to-equity ratio of 0.84. The company has a market capitalization of $21.23 billion, a price-to-earnings ratio of -103.00 and a beta of 1.15. MongoDB, Inc. has a fifty-two week low of $212.74 and a fifty-two week high of $509.62. The firm has a 50-day moving average of $253.05 and a 200-day moving average of $307.74.
MongoDB (NASDAQ:MDB – Get Free Report) last posted its earnings results on Thursday, August 29th. The company reported $0.70 earnings per share for the quarter, topping analysts’ consensus estimates of $0.49 by $0.21. The firm had revenue of $478.11 million for the quarter, compared to analyst estimates of $465.03 million. MongoDB had a negative net margin of 12.08% and a negative return on equity of 15.06%. The company’s revenue for the quarter was up 12.8% compared to the same quarter last year. During the same period in the previous year, the firm posted ($0.63) EPS. As a group, sell-side analysts anticipate that MongoDB, Inc. will post -2.46 EPS for the current year.
Insider Buying and Selling at MongoDB
In related news, Director Dwight A. Merriman sold 3,000 shares of the business’s stock in a transaction on Tuesday, September 3rd. The shares were sold at an average price of $290.79, for a total value of $872,370.00. Following the transaction, the director now owns 1,135,006 shares in the company, valued at $330,048,394.74. The transaction was disclosed in a document filed with the SEC, which is available through this link. In other news, Director John Dennis Mcmahon sold 10,000 shares of MongoDB stock in a transaction on Monday, June 24th. The shares were sold at an average price of $228.00, for a total value of $2,280,000.00. Following the completion of the sale, the director now owns 20,020 shares in the company, valued at approximately $4,564,560. The transaction was disclosed in a document filed with the Securities & Exchange Commission, which is accessible through this hyperlink. Also, Director Dwight A. Merriman sold 3,000 shares of the company’s stock in a transaction dated Tuesday, September 3rd. The shares were sold at an average price of $290.79, for a total transaction of $872,370.00. Following the transaction, the director now owns 1,135,006 shares in the company, valued at approximately $330,048,394.74. The disclosure for this sale can be found here. In the last 90 days, insiders sold 32,179 shares of company stock valued at $8,063,279. 3.60% of the stock is currently owned by company insiders.
Analyst Upgrades and Downgrades
A number of analysts recently issued reports on MDB shares. Needham & Company LLC lifted their target price on MongoDB from $290.00 to $335.00 and gave the company a “buy” rating in a research report on Friday, August 30th. Tigress Financial dropped their price objective on MongoDB from $500.00 to $400.00 and set a “buy” rating on the stock in a research note on Thursday, July 11th. Scotiabank raised their target price on shares of MongoDB from $250.00 to $295.00 and gave the company a “sector perform” rating in a research note on Friday, August 30th. JMP Securities reaffirmed a “market outperform” rating and set a $380.00 price target on shares of MongoDB in a research report on Friday, August 30th. Finally, Sanford C. Bernstein increased their price target on shares of MongoDB from $358.00 to $360.00 and gave the company an “outperform” rating in a research note on Friday, August 30th. One analyst has rated the stock with a sell rating, five have given a hold rating and twenty have issued a buy rating to the company’s stock. Based on data from MarketBeat, the stock presently has a consensus rating of “Moderate Buy” and an average target price of $337.56.
MongoDB, Inc, together with its subsidiaries, provides general purpose database platform worldwide. The company provides MongoDB Atlas, a hosted multi-cloud database-as-a-service solution; MongoDB Enterprise Advanced, a commercial database server for enterprise customers to run in the cloud, on-premises, or in a hybrid environment; and Community Server, a free-to-download version of its database, which includes the functionality that developers need to get started with MongoDB.
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Before you consider MongoDB, you’ll want to hear this.
MarketBeat keeps track of Wall Street’s top-rated and best performing research analysts and the stocks they recommend to their clients on a daily basis. MarketBeat has identified the five stocks that top analysts are quietly whispering to their clients to buy now before the broader market catches on… and MongoDB wasn’t on the list.
While MongoDB currently has a “Moderate Buy” rating among analysts, top-rated analysts believe these five stocks are better buys.
HelixML have announced their Helix platform for Generative AI is production ready at version 1.0. Described as a ‘Private GenAI Stack’ the platform provides an interface layer and applications that can be connected to a variety of large language models (LLMs). It can be used to prototype applications, starting with just a laptop; with all components version controlled to ease subsequent deployment and scaling of apps that prove useful. There’s also heavy emphasis on evaluations (evals) as the substitute for tests in the non deterministic domain of LLMs.
Helix was launched in December 2023, and found some initial traction with a German company concerned about compliance with European Union regulations. In a ‘vision’ blog post titled ‘The Open Source AI Revolution: How Regulation Is Reshaping Enterprise GenAI‘ HelixML co-founder and CEO Luke Marsden explains that ‘Open Source models can be run locally to avoid sensitive data being shared with US based service providers’. He goes on to note that such models now offer similar capabilities to those of frontier proprietary models a short while ago, so for many use cases it’s possible to achieve similar performance without having to sacrifice control over data.
Having set out to facilitate fine-tuning of models the HelixML team discovered that was of less interest to customers; mainly due to retrieval-augmented generation (RAG) gaining traction as a quicker way to accomplish the same outcomes. So Helix 1.0 ships with RAG capabilities. Though Marsden notes:
I believe fine-tuning will make a comeback when people get further down the line and want to optimize specific systems that use large general purpose LLMs (e.g 70B) and “distill” a fine-tuned LLM in 3B so they can scale to production traffic without much cost. We still support fine-tuning in the product, and we’ll get there…
Marsden has also published a ‘product’ blog post ‘Announcing Helix 1.0 – Secure Local GenAI for Serious People’ that runs through the implementation details of the platform: its architecture, the interface layer, applications, and choice of underlying LLMs. The platform has been written in Golang, and is deployed as a set of containers, so it’s sympathetic to well established industry norms around deployment and operations.
By building what amounts to integration middleware for GenAI based applications Helix doesn’t have to involve itself in the high cost aspects of the domain – building infrastructure and training models. But they don’t have the space to themselves, with competitors like Griptape selling a similar story. Marsden concludes his ‘vision’ post with a sober forecast for 2025 that holds a final note of optimism for the longer term:
Yes, there’s a trough of disillusionment coming for GenAI. But through the trough of disillusionment comes the plateau of productivity. GenAI is just mathematical models, and models don’t generalize beyond their training data. This stuff is not going to take over the world. The capabilities will plateau. Nevertheless, super-human scale knowledge processing capabilities will change the business world for good.
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Chris Swan
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Arizona State Retirement System increased its position in shares of MongoDB, Inc. (NASDAQ:MDB – Free Report) by 0.6% during the second quarter, according to the company in its most recent 13F filing with the SEC. The firm owned 19,981 shares of the company’s stock after buying an additional 129 shares during the quarter. Arizona State Retirement System’s holdings in MongoDB were worth $4,994,000 at the end of the most recent quarter. 
