Three signs this stock is a long-term winner
Brian Withers (Twilio): Twilio is getting caught up in the tech stock sell-off as investors may be cashing out to move to more conservative investments. This is a short-sighted approach and these sellers are going to miss out on the massive long-term prospects of this tech stock. With a pivot to become a customer engagement platform, the best days for the company are still ahead. Here are three recent events that show why.

The first event was the company’s investor day presentation on Oct. 1, where management shared its long term vision and plans for the next few years. In short, the opportunity to grow is massive. Twilio has just a small portion of the potential market across all its product segments, its customer verticals, and in all regions around the globe. Given this huge opportunity, management committed that it would be able to achieve 30%-plus top-line annual growth for the next four years. This declaration should give investors confidence that the company plans to follow through on its growth plans and will be a much bigger platform in the years ahead.

The second event occurred on Oct. 12 when the company announced its $3.2 billion all-stock acquisition of Segment, a customer data platform company. This merger enables Twilio to tap into a deep set of customer insights to better inform its messaging applications, improve the platform’s ability to engage with customers, and provide more personalized messages. Segment brings an additional $17 billion in addressable market opportunity. With the massive success Twilio’s had in growing its customer base, this merger should help accelerate its top line even more.

The third event was its earnings release on Oct. 26. This report provided the cherry on top of what was an ice cream sundae of an October for the company. The 52% year-over-year growth beat management’s increased guidance to hit a record $448 million in quarterly revenue. This incredible growth was driven by existing customers and new customer acquisitions. Existing customers drove its dollar-based net retention to 137% by expanding into new offerings and growing usage of their existing messaging products. The company added 8,000 customers in the quarter to hit a record 208,000, up 21% year over year. The company projected it would achieve 36% to 38% year-over-year top-line growth in the upcoming fourth quarter, but it’s likely that it will beat those projections, too.

This customer engagement platform is on a roll. The investor day highlighted this growth company still has massive opportunities ahead. Its smart acquisition of Segment expanded its already huge addressable market even more. And its continued solid execution from its most recent earnings call makes this software platform a long-term buy. With the recent sell-off, now’s a good chance to pick up some shares at a discount.

Datadog: Explosive growth at a discount
Danny Vena (Datadog): You’ve likely heard the old saying, “Don’t throw out the baby with the bathwater.” Given the wholesale sell-off within the technology sector over the past several weeks, the saying is particularly applicable to the current situation. The rotation out of tech and software stocks that began in mid-October has resulted in a number of bargains, presenting investors with a number of compelling opportunities. I think that’s the case with Datadog.

The stock had been on a hot streak this year, boasting more than triple-digit gains between January and Oct. 13. Since then the stock has lost more than 25% of its value and nothing about Datadog’s growth story has changed.

The results from earlier this week tell the tale. For the third quarter, DataDog reported revenue of $155 million, up 61% year over year, only a slight deceleration from the 68% top-line growth it delivered in the second quarter. The company also generated a small adjusted earnings per share (EPS) for the quarter of $0.05, not bad for a company that went public just over a year ago. Both metrics sailed past analysts’ consensus estimates, which called for revenue of $144.3 million and EPS of $0.01.

The company also continued its strong track record of attracting new clients, with large, more lucrative enterprise businesses at the top of the list. Customers representing annual revenue of more than $100,000 grew to 1,107, growing 52% from 727 in the prior-year quarter. At the same time, its overall client base climbed to 13,100, up 38%.

Another secret to Datadog’s success is that once customers try out its services, they not only tend to stick around, but also spend more money. The company reported a dollar-based retention rate — which measures the level of spending by existing customers — of above 130% for the 13th consecutive quarter. Put another way, existing clients spent 30% more over the past year than they did in the preceding 12 months.

Datadog’s lofty valuation probably contributed to its decline. Even after its recent haircut, the stock trades for roughly 52 times trailing-12-month sales.

That said, given Datadog’s high-caliber growth, I’d argue it’s worth its premium price tag and it’s just taking a breather before climbing to even loftier heights. The stock might be worth a look at its recent discounted price.

HubSpot’s fantastic year isn’t over yet
Chris Neiger (HubSpot): HubSpot’s software may not be well known to the average investor, but it’s playing a critical role for small businesses, especially during the pandemic. HubSpot’s inbound marketing software helps companies manage their current customer relationships while also providing online tools to acquire new ones.

With HubSpot focusing on small and medium-sized businesses, you might assume that the company has taken a hit this year as many small businesses have suffered from the negative economic effects of COVID-19. But HubSpot has proved to be very resilient and in the third quarter (reported on Nov. 5) HubSpot’s sales spike 32% and adjusted diluted earnings per share reached $0.28, both of which outpaced the company’s own guidance and beat Wall Street’s expectations.

Even during this tough economic time, HubSpot grew its subscription revenue by 32% in the third quarter and boosted its total customers by an impressive 39% year over year. Part of the company’s success this year came from its ability to work with businesses that were struggling, offering them free premium services or reduced rates in order to help them out. That type of strategy may seem counter intuitive for some businesses, but it’s helped HubSpot build a large customer base that will likely be loyal to the company for years to come.

Even with the difficult economic situation, HubSpot’s management said full-year 2020 revenue will be 28% higher than it was last year. Investors have taken notice of HubSpot’s phenomenal growth this year and have pushed the company’s share price up an impressive 122% year to date.

And while it may seem logical to think that there’s no more room for current investors to benefit, consider that HubSpot’s massive customer growth this year will likely boost sales and earnings down the line. When the pandemic is behind us and companies begin spending more money to grow their businesses, they’ll likely do so using many of HubSpot’s services that helped them get through this difficult year.

Where to invest $1,000 right now
Before you consider HubSpot, you’ll want to hear this.

Investing legends and Motley Fool Co-founders David and Tom Gardner just revealed what they believe are the 10 best stocks for investors to buy right now… and HubSpot wasn’t one of them.

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CATechnologies
Appdynamics
Splunk
Dynatrace
Datadog
BMCSoftware
Sysdig
Signalfx
Wavefront(VMware)
Coscale
Elastic

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Linux
Windows

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LargeEnterprises
SmallandMedium-sizedEnterprises(SMEs)

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Overview

Kong Inc. released version 0.8 of Kong for Kubernetes, its Kubernetes Ingress controller built on top of the Kong API Gateway. The release introduces Knative ingress integration, a new cluster-level Custom Resource Definition (CRD), and annotation changes aimed at simplifying configuration. It also includes breaking changes to path-based routing that existing users will need to address.

What Is Kong for Kubernetes?

Kong for Kubernetes is composed of two tightly coupled components:

• A Kubernetes controller that watches Ingress resources and Kubernetes CRDs, translating them into Kong Gateway configuration.
• The Kong Gateway, an open-source API gateway built on NGINX, which handles the actual processing of incoming API traffic.

Most managed Kubernetes clusters on public clouds default to a vendor-provided Ingress controller tied to the vendor’s load balancer infrastructure. Kong for Kubernetes is an alternative that brings full API gateway capabilities into the Ingress layer, with the added benefit of consistent behavior across cloud providers and on-premises deployments via Kubernetes CRDs.

How Kong Differs from NGINX Ingress Controllers

Although Kong Gateway is itself built on NGINX, Kong for Kubernetes is architecturally distinct from both the community nginx-ingress-controller and NGINX Inc.’s commercial controller. According to Reza Shafii, VP of Products at Kong Inc., the key differentiators are the API management features Kong brings to the Ingress layer:

• OIDC-based authentication
• Advanced rate limiting
• Request caching
• Log and metrics streaming to multiple analytics providers simultaneously
• Support for multiple protocols: REST, GraphQL, gRPC, TCP

Shafii also highlighted performance characteristics cited by Kong Inc., including sub-millisecond latency and support for 25,000+ transactions per second, though independent benchmarks should be consulted before relying on these figures in architectural decisions.

For teams already running Kong Gateway outside Kubernetes, this controller provides a unified API management lifecycle across both Kubernetes and non-Kubernetes workloads — a meaningful operational advantage in hybrid environments.

What’s New in Version 0.8

Knative Ingress Support

The headline feature in 0.8 is support for Knative as an Ingress target. Knative is a Kubernetes-native serverless platform that provides higher-level abstractions for container-based workloads. Its default Ingress backend is Istio, with Gloo as a notable alternative.

Shafii explained the motivation for adding Kong as a Knative Ingress option:

What we have learned from our community and customers is that most use cases don’t require the weight of Istio to run serverless workloads. And in fact, the heavy weight of Istio is one of the reasons that prompted us to initiate the Kuma service mesh project. Kong Gateway’s plugin architecture and focus on extensibility helps keep Knative workloads focused solely on business logic.

Reza Shafii, VP of Products, Kong Inc.

For teams running Knative who find Istio’s operational overhead excessive, Kong for Kubernetes 0.8 offers a lighter alternative with the full plugin ecosystem of Kong Gateway available at the Ingress layer.

New Cluster-Level CRD

Version 0.8 introduces a new cluster-scoped Custom Resource Definition. Cluster-level CRDs are not namespaced, meaning they apply across the entire cluster rather than within a specific namespace. This is useful for global configuration that should not be replicated per namespace — for example, cluster-wide plugin settings or global rate limiting policies.

Annotation Updates

Several annotations have been updated or deprecated in this release to reduce configuration complexity. Users upgrading from previous versions should review the official changelog carefully, as some previously used annotations will no longer function as expected.

Breaking Changes: Path-Based Routing

Version 0.8 includes breaking changes to path-based routing behavior. Any existing Ingress resources that rely on path routing should be tested against the new release before upgrading in production. The full list of breaking changes and migration guidance is available in the official changelog.

Using Cloud Load Balancers with Kong

Kong for Kubernetes does not replace cloud load balancers — it complements them. Shafii described the recommended deployment pattern:

Cloud load balancers provide a way to balance traffic across multiple Kong Gateway nodes, and Kong Gateway nodes help manage traffic to multiple services within a cluster. For most users, we recommend the use of load balancers such as those of AWS or GCP to front Kong Gateways. This provides an endpoint inside the virtual private network in the cloud, which can then be exposed to other networks, partner networks, or directly on the internet.

Reza Shafii, VP of Products, Kong Inc.

In practice this means the typical production topology is: cloud load balancer (AWS ELB/ALB, GCP Load Balancer) → Kong Gateway nodes → individual Kubernetes services. This separates concerns cleanly: the cloud LB handles cross-node traffic distribution and TLS termination at the network edge, while Kong handles API policy enforcement inside the cluster.

Observability and Metrics

Kong for Kubernetes exposes metrics at two levels:

• Traffic metrics (per route and per service): HTTP status codes, error rates, throughput, latency, and ingress/egress bandwidth.
• Gateway health metrics: connections served, active connections, shared memory usage, and cache hit ratio.

These metrics can be exported to the following backends:

• Prometheus
• Datadog
• StatsD
• Zipkin
• Jaeger

This level of observability is comparable to what Istio and Gloo offer, making Kong for Kubernetes a viable choice for teams that need distributed tracing and metrics aggregation without adopting a full service mesh.

Upgrade Considerations

• Review the official changelog for the complete list of deprecated annotations and breaking changes before upgrading.
• Test all Ingress resources that use path-based routing in a staging environment first.
• If you use Knative, verify compatibility with the specific Knative version you are running, as Knative’s own Ingress API has evolved across releases.
• Plan for any CRD migration required by the new cluster-level resource introduced in this release.

Summary

Kong for Kubernetes 0.8 is a meaningful release for teams that want full API gateway capabilities — authentication, rate limiting, protocol flexibility, and rich observability — directly at the Kubernetes Ingress layer. The addition of Knative support gives serverless workloads a lighter-weight Ingress alternative to Istio. However, the breaking changes to path routing mean existing deployments require careful testing before upgrading.

Strong Q4 2019 Results Signal Growing Demand for Cloud Observability

Datadog (DDOG) reported exceptionally strong results for the fourth quarter of 2019, providing tangible evidence that organizations of all sizes are increasingly selecting its monitoring and analytics platform — with demand concentrated heavily in cloud environments.

Datadog’s Q4 revenue jumped 84% year-over-year to $113.6 million, beating consensus analyst estimates by approximately 11%. By comparison, competitor New Relic (NEWR) reported 23% revenue growth to $153 million in the same quarter, while Dynatrace (DT) grew revenue 25% to $143.3 million, with annual recurring revenue (ARR) growth of 44%.

Rapid Customer Growth Across All Segments

Datadog closed Q4 2019 with approximately 10,500 total customers, representing 36% year-over-year growth. The company added roughly 1,000 net new customers in the quarter alone — nearly twice the number added in the same quarter the prior year.

Customer penetration was relatively balanced across enterprise, mid-market, and SMB segments. Key metrics from the period include:

• Average enterprise ARR: $230,000 — up 44% year-over-year
• Average mid-market ARR: $170,000 — up 55% year-over-year
• Customers with ARR ≥ $100,000: 858 — up 89% year-over-year
• Customers with ARR ≥ $1,000,000: 50 — up 72% year-over-year (compared to just 12 customers two years prior)

The dollar-based net retention rate remained above 130% for the tenth consecutive quarter as of Q4, driven by both expanded usage of existing products and successful cross-selling of newer offerings.

Multi-Product Adoption Accelerating

One of the most telling signals of Datadog’s platform momentum is the rapid shift toward multi-product adoption among its customer base:

• 60% of customers were using two or more products as of Q4 2019, up from 50% the prior quarter and just 25% a year earlier.
• ~25% of customers deployed across all three core observability pillars (infrastructure monitoring, APM, and log management), compared to only 5% a year prior.
• The majority of new logo deals already included two or more products at the time of signing.

This expansion reflects Datadog’s deliberate strategy of growing its platform incrementally, adding APM and log management capabilities over the preceding three years.

The Three Pillars of Datadog’s Observability Platform

Founded in 2010, Datadog serves a diversified user base that includes developers, IT operations teams, and business users. The core platform is built around three foundational observability capabilities:

• Infrastructure Monitoring: Provides visibility into hosts, containers, and cloud services across hybrid and multi-cloud environments.
• Application Performance Monitoring (APM): Traces requests across distributed services, helping teams identify latency bottlenecks and errors.
• Log Management: Centralizes and indexes log data, enabling correlation with metrics and traces for faster root-cause analysis.

Together, these three pillars address the growing complexity of modern IT stacks as organizations move workloads from static on-premises infrastructure to public and private clouds, while also deploying containers, microservices, and serverless architectures at scale.

New Products Expanding the Platform’s Scope

Datadog has consistently broadened its product portfolio beyond the three core pillars. Key additions launched around this period include:

Synthetics (Q3 2019)

Synthetics allows customers to simulate end-user interactions with web applications, providing proactive visibility into performance problems before real users are affected. This is particularly useful for establishing performance baselines and detecting regressions during deployments.

Network Performance Monitoring (Q4 2019)

An extension of Datadog’s core Infrastructure product, Network Performance Monitoring provides granular visibility into network flows across hybrid cloud and on-premises environments. It delivers real-time insights into performance metrics and service dependencies — critical for diagnosing issues that span multiple network segments.

Real User Monitoring (Q4 2019)

Real User Monitoring (RUM) extends Datadog’s user experience monitoring suite to capture real-time data about individual end-user sessions — including page load times, JavaScript errors, and user actions — enabling teams to prioritize frontend performance issues by actual business impact.

Security Monitoring (Beta, November 2019)

Launched in beta in November 2019, Security Monitoring is designed to break down silos between security teams, developers, and IT operations. It provides security teams with unified visibility into infrastructure metrics, distributed traces, and logs — enabling real-time threat detection and streamlined alert investigation. This product represented Datadog’s first significant move into the security observability space.

Why Datadog Is Winning in Cloud Environments

Several structural trends are driving Datadog’s growth:

• Cloud migration: As organizations move workloads to AWS, Azure, and Google Cloud, they require monitoring tools that can handle ephemeral, dynamic infrastructure — a poor fit for legacy on-premises monitoring tools.
• Microservices and containers: Distributed architectures dramatically increase the number of observable components, making unified platforms that correlate metrics, traces, and logs more valuable than point solutions.
• Serverless computing: Functions-as-a-service environments introduce new observability challenges around cold starts, execution duration, and invocation errors that traditional APM tools were not designed to address.
• Developer-led purchasing: Datadog’s developer-friendly instrumentation and SaaS delivery model align well with modern engineering teams who influence or own tooling decisions.

Financial Outlook for 2020

For Q1 2020, Datadog guided for revenue of $117–$119 million (approximately 68% growth at the midpoint), ahead of the consensus estimate of $108.8 million at the time of reporting.

Full-year 2020 revenue guidance of $535–$545 million represented approximately 49% growth at the midpoint, coming in roughly 7% above the then-consensus estimate of $504.3 million.

Datadog went public in September 2019 at $27 per share, opening at $40.35. At the time of the Forbes report (February 2020), the stock was trading around $47 per share, having recently set a post-IPO high of $50.12.

Key Takeaways for DevOps and Platform Engineering Teams

For engineers evaluating observability platforms, Datadog’s Q4 2019 trajectory highlights several important considerations:

• The consolidation of metrics, traces, and logs into a single platform reduces context-switching and accelerates root-cause analysis during incidents.
• Native support for containers, serverless, and hybrid cloud environments is increasingly table stakes for modern observability tooling.
• Expanding into security observability signals an industry trend toward converging DevOps and SecOps tooling under unified data pipelines.
• High net retention rates (above 130%) suggest that teams consistently find enough value to expand their usage — a useful proxy for real-world platform stickiness.

Note: Financial figures and product details in this article reflect Datadog’s reported results and announcements as of early 2020. Platform capabilities, pricing, and market positioning have continued to evolve since this period.

Datadog (NASDAQ: DDOG), a cloud-based monitoring and analytics platform aimed at DevOps and infrastructure teams, made a high-profile market debut in late September 2019. Shares surged nearly 39% on the first day of trading, and the company closed its opening session with a valuation of approximately $10.9 billion. The IPO positioned Datadog as one of the more successful software market entries of that year, drawing comparisons to other high-growth SaaS companies that had gone public in the same period.

A Strong First Day: From $27 to $40.50

Datadog priced its IPO at $27 per share. When trading opened, investor demand pushed the price to a peak of approximately $40.50 before the stock settled at $37.55 by the end of the first session. That closing price implied a market capitalisation of around $10.9 billion — a significant milestone for a company that had not yet reached profitability.

The strong debut reflected confidence in Datadog’s core business model: a subscription-based platform that helps engineering and operations teams monitor infrastructure, applications, and log data across cloud environments. As organisations increasingly adopt multi-cloud architectures, the demand for unified observability tooling has grown substantially, giving Datadog a well-timed platform on which to go public.

Revenue Growth and the Path to Profitability

According to the company’s S-1 filing with the SEC, Datadog reported revenue of $153.4 million in the first half of 2019, representing a 79% year-over-year increase for that period. The company also reported year-over-year revenue growth of approximately 97% between full-year 2017 and full-year 2018 figures.

Despite this trajectory, Datadog was not yet profitable at the time of its IPO. The company posted net losses of roughly $13.4 million in the first half of 2019. This is a common profile for high-growth SaaS businesses: heavy investment in sales, marketing, and engineering ahead of the revenue scaling needed to achieve positive operating margins. Investors who backed Datadog were effectively betting that the company’s growth rate and customer retention metrics justified a premium valuation even in the absence of near-term profitability.

Customer Retention: The Key Metric Winning Over Investors

In the SaaS industry, revenue growth alone is rarely sufficient to sustain investor confidence — customer retention and expansion metrics matter as much as top-line numbers. Datadog’s dollar-based net retention rate of 146% as of June 2019 was a headline figure from the IPO roadshow. A net retention rate above 100% means that existing customers are, on average, spending more over time than when they first subscribed, even after accounting for any churn. A rate of 146% indicates meaningful expansion revenue from the existing customer base — a strong signal that users are adopting additional Datadog products and increasing usage as their infrastructure grows.

This metric directly addressed one of the central concerns investors have with high-growth software companies: that a large or fast-growing user base can mask underlying churn or stagnant per-customer spending. Datadog’s retention figures suggested the opposite dynamic was at play.

The Cisco Acquisition Attempt: A Valuation Floor

One factor that likely boosted investor confidence ahead of the IPO was widespread reporting that Cisco had explored acquiring Datadog in the weeks leading up to the offering, with figures in excess of $7 billion cited in contemporaneous news coverage. Datadog’s leadership chose to pursue the public market instead. For investors, this reported acquisition interest served an important psychological function: it established a credible floor valuation from a strategic acquirer with deep knowledge of the infrastructure software market. If Cisco — a major networking and software company with its own monitoring portfolio — valued Datadog at over $7 billion as a private acquisition target, public investors could reasonably conclude that the business had tangible strategic worth independent of speculative growth narratives.

By opting for an IPO and successfully pricing above that implied floor, Datadog’s executive team signalled confidence in the company’s standalone prospects and gave early investors a reference point against which to anchor their own valuation thinking.

Why Multi-Cloud Monitoring Is a Durable Market

Datadog’s core product addresses a structural challenge in modern infrastructure: as organisations distribute workloads across multiple cloud providers — AWS, Azure, Google Cloud, and others — they need a single pane of glass to monitor performance, detect anomalies, and correlate logs and metrics across all environments. Purpose-built multi-cloud and hybrid monitoring is not a transient trend; it is a natural consequence of how enterprise IT architecture has evolved over the past decade.

Competing in this space means going up against established players including Splunk, New Relic, Dynatrace, and cloud-native monitoring tools offered by AWS and Azure themselves. Datadog differentiated itself by offering tight integrations across a wide range of technologies and by providing infrastructure, application performance monitoring (APM), and log management within a single unified platform — reducing the need for teams to stitch together disparate tools.

What the IPO Meant for the DevOps Tooling Market

Datadog’s successful IPO was notable not just as a financial event but as a signal about the broader observability and DevOps tooling market. A $10.9 billion public debut for a monitoring and analytics platform validated the idea that infrastructure visibility tooling could command enterprise-grade valuations — a category that had historically been undervalued relative to application-layer software.

For engineering and platform teams evaluating observability vendors, the IPO brought both benefits and considerations: Datadog was now a well-capitalised public company with resources to invest in product development and integrations, but also one with quarterly earnings pressure that could influence product roadmap priorities over time.

As of its 2019 IPO, Datadog had not yet reached operating profitability. Its long-term viability would depend on continuing to grow revenue faster than costs while maintaining the high customer retention rates that made the IPO compelling in the first place.