Elastic’s (NYSE:ESTC) valuation has tended to trail peers with comparable financial performance due to the company lacking a compelling narrative. This has changed over the past year with the growing use of LLMs, particularly in search use cases. As a result, Elastic’s share price has moved significantly higher, although the company’s valuation still looks reasonable relative to peers.
Elastic’s financial performance has been respectable in recent quarters, with growth stabilizing and margins continuing to improve, but investors will want generative AI to drive a growth reacceleration in coming months if the stock is to move higher though. There is uncertainty in this regard, both in respect to the magnitude and timing of any AI related demand surge.
I previously suggested that a tepid demand environment coupled with elevated investor expectations would make things difficult for Elastic going forward and this has proven to be the case so far, with the stock down approximately 10%.
Market Conditions
Elastic has stated that cloud consumption patterns have stabilized, which is suggestive of a healthier demand environment. Companies remain focused on costs though, which could be considered beneficial for Elastic as its platform allows customers to consolidate spend. While Elastic has suggestion that some of its customers are consolidating spend on its platform, growth and expansion both remain fairly soft.
Demand really depends on the specific market though, as Elastic has exposure to search, observability and security. Search is seeing a resurgence in interest driven by the capabilities of LLMs. Security spending has been resilient due to the importance of security and an ever-evolving threat landscape.
Elastic has suggested that competitive dynamics remain stable, which is interesting from a security perspective as the soft demand environment appears to be pressuring some companies.
Generative AI
Elastic is well positioned to capitalize on new search capabilities enabled by LLMs. This appears to be one of the early generative AI use cases seeing broader adoption. Customers are utilizing Retrieval Augmented Generation (RAG) in generative AI applications, with Elastic’s technology helping to:
- Deliver relevant content
- Maintain security and confidentiality
- Reduce costs
Longer context windows could eventually present a challenge to RAGs. Elastic doesn’t believe that this will be the case though because RAG is less expensive and new proprietary data requires a solution that can provide up to date, contextual and accurate results.
Recent commentary by Accenture (ACN) suggests that while customers are beginning to deploy meaningful amounts of money into generative AI, this is still largely exploratory, and most companies do not have the technology infrastructure and employees to fully realize the value of AI. This is an opportunity for modern data infrastructure vendors, like Elastic, MongoDB (MDB), Confluent (CFLT), Databricks and Snowflake (SNOW). Data infrastructure will presumably be one of the first areas to see a meaningful increase in spend if generative AI begins to scale in production.
Elastic added several hundred Elasticsearch Relevance Engine (ESRE) customers in the third quarter. ESRE allows customers to build generative AI applications without needing to train their own models. While this obviously could be a long-term tailwind, Elastic doesn’t expect generative AI to be a growth driver in the near term. Uncertain macroeconomic conditions are also causing customers to prioritize spending, with a focus on near-term ROI.
There is also the specter of competition from pure play vector database vendors. While vector database competition is likely to increase over time, Elastic has stated that customers are now realizing the advantages of its platform relative to competitors. Customers need scalable solutions with a broad set of enterprise features, including hybrid search, document level permissions, security and the ability to create vector embeddings.
MongoDB’s vector search product was made generally available in December and is likely to prove competitive. The company is hoping its tightly integrated solution will appeal to customers, although MongoDB lacks capabilities like generating embeddings.
SIEM
Elastic has really begun to lean into the AI opportunity in recent communications, which is understandable but not necessarily bullish. Security is currently contributing the majority of its revenue, and performance here is questionable.
Elastic has stated that it is displacing legacy log analytics and SIEM vendors and that customers are consolidating on its platform for observability and security use cases. Elastic offers scale and speed in security analytics and the company believes that its AI assistant is making its solution more compelling. Elastic’s Frozen Tier is also potentially appealing in an environment where customers are focused on cost.
Elasticsearch Query Language (ESQL) is also making it easier for customers to migrate to the company’s platform. ESQL was launched in November and approximately 1,000 customers have already tried it.
It is probably reasonable to assume the LLMs will reduce switching costs for tools that use proprietary languages. Elastic should benefit from this in the near-term at the expense of legacy vendors, but it also likely reduces the attractiveness of the category as a whole over time.
Financial Analysis
Elastic’s revenue increased 19% YoY in the third quarter to 328 million USD, driven by 29% Elastic Cloud growth. Elastic Cloud contributed approximately 43% of total revenue in the quarter. Subscription revenue was 308 million USD, up 20% YoY, and Professional services revenue increased 7% YoY to 20 million USD.
EMEA was an area of strength in the third quarter. Elastic has relatively large exposure to Europe, which could help explain Elastic’s poor performance in 2022 and the increase in growth in late 2023.
Fourth quarter revenue is expected to be in the 328-330 million USD range, representing 18% YoY growth at the midpoint. This seems overly conservative given the stable demand environment and Elastic’s recent growth reacceleration. I expect fourth quarter revenue to come in closer to 339 million USD.
Figure 1: Elastic Revenue Growth (source: Created by author using data from Elastic)
Elastic has suggested that demand from SMBs remains soft, which is indicated by anemic customer count growth. Elastic has shifted its focus to larger customers though, with growth there better, albeit still soft.
Table 1: Elastic Customer Count (source: Created by author using data from Elastic)
Elastic’s net expansion rate was 109% in the third quarter, in line with expectations. This is a backward looking metric though and hence will likely improve as customer optimization efforts ease and impact of generative AI demand begins to trickle down.
Figure 2: Elastic Customers (source: Created by author using data from Elastic)
The number of job openings mentioning Elasticsearch in the job requirements has been fairly stable over the past 12 months, albeit at a fairly depressed level. This could indicate that net customer additions will remain weak in the near-term.
Figure 3: Job Openings Mentioning Elasticsearch in the Job Requirements (source: Revealera.com)
Elastic’s subscription gross profit margin has been fairly stable, even as the cloud business has become increasingly important, which is a positive. Elastic is launching a serverless offering which could be a minor drag on margins in the short term though. Elastic delivers RAG functionality on CPUs and hence generative AI is not expected to be a margin headwind.
Elastic’s services gross margin has begun to fall again though, which I consider indicative of either a softening demand environment or increased competition.
Figure 4: Elastic Gross Profit Margins (source: Created by author using data from Elastic)
Elastic’s operating profit margin has improved significantly over the past few years and the company is now closing in on GAAP profitability.
Figure 5: Elastic Operating Profit Margins (source: Created by author using data from Elastic)
Most of the recent gains in profitability have been driven by R&D expenses. The lack of operating leverage in sales and market expenses is understandable in the current environment but somewhat concerning.
Elastic expects to increase its investments in 2024 in order to capitalize on the generative AI opportunity. This will presumably limit further improvements in profitability in the near-term, dependent on revenue growth.
Figure 6: Elastic Operating Expenses (source: Created by author using data from Elastic)
The number of job openings doesn’t indicate a surge in investment at this stage though.
Figure 7: Elastic Job Openings (source: Revealera.com)
Conclusion
On the surface Elastic’s valuation doesn’t seem to imply particularly high expectations, but the company has consistently traded at a discount to peers with similar growth and margins in the past. This appears to be due to the fact that Elastic’s search platform hasn’t resonated with investors until recently.
Generative AI hype has propelled Elastic’s stock higher, but current growth expectations will be hard for the company to fulfill. The pace of net customer additions remains weak, as is expansion within existing customers, neither of which are suggestive of a growth reacceleration.
While generative AI will likely provide a tailwind at some point, customers will need to move behind exploration projects, which will take time. Elastic is also facing increased competition in security, threatening its primary source of revenue. This creates an unfavorable setup, particularly if macro conditions weaken or if interest rates remain elevated.
Figure 8: Elastic Relative Valuation (source: Created by author using data from Seeking Alpha)
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