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October 12, 2022 By Manny Lopez

Datadog alternatives for cloud security and application monitoring

What is Datadog header

What is Datadog?

If you work in IT or DevOps, unless you’ve been living on a remote island without Internet access, you’ve likely heard of Datadog, a popular platform for monitoring cloud applications. Datadog collects and interprets data from various IT resources. The resulting insights assist in managing performance and reliability challenges to deliver a better end-user experience.

What is Datadog used for?

Datadog is a popular cloud-based tool known for application performance monitoring (APM) and observability. It is compatible with various IT environments, including on-premises servers and cloud infrastructure (public clouds, hybrid clouds, private clouds, etc.), making it helpful to companies that need to uncover performance issues and improve customer experience.

What is Datadog used for

Datadog performs both data collection and analysis. Its analytics layer applies artificial intelligence (AI) and machine learning (ML) to data points, helping to address troubleshooting with alerts and root cause analysis. Its real user monitoring (RUM) identifies end-user challenges. This combination of features eliminates the need for IT teams and site reliability engineers (SREs) to set up separate data collection and analysis or visualization tools.

Why choose a Datadog alternative

Although companies use Datadog widely, it’s not perfect. Some of Datadog’s main disadvantages include:

  • Advanced troubleshooting across all telemetry. Datadog’s heritage is in metrics, which is helpful for understanding if a problem occurs. But metrics are simple indicators of something going wrong like the available CPU resources are at capacity. They are insufficient to pinpoint performance issues deep in applications and infrastructure. Whereas logs will indicate exactly what’s happening. A strong log foundation is the pillar of any APM and observability solution and is critical to anomaly detection and uncovering the root cause of a problem.

  • Log search. Technical teams and developers need search power and speed to find and address issues. Datadog lacks a query language making exploratory analysis difficult. Instead, Datadog correlates sampled sets of data using breadcrumbs, which limits data accuracy and end-to-end visibility of digital service performance. Log analysis in Datadog can take hours to process.

  • Usability — log parsing. While Datadog can monitor virtually any resource, it works better out-of-the-box with some resources than others. For example, Datadog parses everything. Parsing data makes unstructured, unreadable data more understandable. Depending on how log files are formatted, users may need to define manual parsers for Datadog to perform collection.

    For instance, if your logs are JSON-formatted, Datadog can parse them automatically. But, for logs formatted in other ways, you’ll have to create custom parsers. Or you could modify how your logs are structured, but that’s often even more work than defining a parser.

  • Limited security and certifications. Datadog security functionality consists of a collection of out-of-the-box detection rules that apply to ingested logs. Its basic search queries only support simple logic/count functions in its detection rules. Datadog has none of the features you’d find in a SIEM or SOAR platform.

Limited security and certifications
  • Licensing model, hidden costs. Each Datadog product is licensed and billed separately but low initial prices may mushroom with Datadog add-ons. Datadog customers also complain of nickel and diming with hidden fees for retention and custom metrics when pulling in data outside its collection agent.

  • Deployment model. Datadog’s agent-based architecture means you must deploy agents on the resources you want to monitor before beginning the monitoring process. Agent deployment can take time, especially if you have a large-scale environment or in cases where it isn’t easy to automate the installation of agent software. Datadog recommends making monthly updates to the agent and redeploying them to resources to account for patches and minor upgrades, which they note “can be challenging.”

Datadog alternatives


While Datadog is a strong player in the monitoring space, there are reasons to consider alternative providers for application performance management with extended functionality. If you need more robust troubleshooting, full-proof security and cost-efficient pricing, Datadog may not be the ideal solution or market leader in those features.

Never fear; alternatives to Datadog do exist!

Download Datadog alternatives comparison chart

Choose Sumo Logic for full-stack observability and comprehensive security

Sumo Logic is a cloud-native SaaS analytics platform built to scale. Powered by AI and machine learning, its full-stack APM and security solution is easy to deploy and cost-effective. Customers appreciate its real-time insights, ready-to-use dashboards and powerful query language for fast searches and troubleshooting. Embracing OpenTelemetry and supporting agent-based and agentless monitoring, Sumo Logic is relatively simple to deploy and scale.

Recognized in both the Gartner 2022 Magic Quadrant for APM and Observability and Magic Quadrant for SIEM, Sumo Logic is an AWS ISV partner of the year with broad out-of-the-box certifications and attestations. By focusing on reliability and security working together Sumo Logic offers frontend to backend visibility from a single pane of glass.

Strengths

  • Integrated observability and security: Sumo Logic consolidates full-stack observability and security, including APM, SIEM and SOAR functionality onto one powerful platform.

  • Log management. Highly scalable log analytics powers its deep expertise in AWS monitoring, Kubernetes monitoring and multi-cloud web services for fast troubleshooting.

  • Cloud-native distributed architecture allows for dynamic scale and flexibility. Proprietary machine learning ingests and analyzes any telemetry.

  • Flexible, cost-effective licensing: Sumo Logic’s unique data tiering model and credit licensing help customers meet expanding data ingestion with reasonable pricing.

Flexible, cost-effective licensing

Users of Sumo Logic praise the platform for being user-friendly, offering an easy setup process, and providing thorough documentation, free training and certification and partnership throughout the relationship.

Flexible, cost-effective licensing - 2

Weaknesses

Sumo Logic is a cloud-based platform. While it can monitor systems on-premises, it is best suited for multi-cloud and hybrid-cloud monitoring.

Its query language is SQL based, so if you know SQL. . . you will have a lot of power at your fingertips! If not, it may take a bit longer to learn, but free training and certifications help customers get up and running quickly.

See a side-by-side comparison of Sumo Logic vs Datadog.

Choose Cisco AppDynamics for network and server monitoring

AppDynamics, acquired by Cisco in 2017, is the foundation of Cisco’s observability strategy. More recently, the company integrated the synthetic and network monitoring capabilities of ThousandEyes to unite different data sources and help customers more easily perform root-cause analysis. End users note its advanced analytics features and easy-to-interpret flow map visualizations.

Strengths

  • IT infrastructure monitoring — from network to server monitoring — is the company’s sweet spot.

  • Global sales, support and partner networks are extensive via Cisco.

  • Via Cisco, AppDynamics has a widespread global data center presence.

Weaknesses

  • AppDynamics’ on-premises backend architecture does not mesh well with modern cloud-native apps and cloud-hosted workloads. Word on the street is there are plans to launch a new cloud-native observability tool in late 2022.

  • End users report slow performance when using the product at scale. It also provides limited support for custom log parsing.

Choose Dynatrace as an AI-operated APM alternative to Datadog

Dynatrace is a cloud monitoring tool celebrated for its focus on automation. The platform also emphasizes AIOps, meaning machine learning helps drive observability and remediation. With a focus on the enterprise, Dynatrace is a leader in application monitoring, infrastructure monitoring, and network monitoring to improve uptime and digital experiences.

Strengths

  • An established enterprise-focused brand with a broad product portfolio and a sizable customer/installed base.

  • The platform provides observability with modularized capabilities for APM, IT infrastructure monitoring, digital experience monitoring (DEM), business analytics, AIOps, cloud automation, and application security

  • Dynatrace’s application security can determine whether a vulnerability has public internet exposure and access to sensitive data; it also identifies other services or apps that are dependent or affected.

Weaknesses

  • Dynatrace’s host unit pricing is based on the host’s memory size, making pricing complex for modern architecture environments

  • Along with host unit pricing Dynatrace also charges for ingestion in Davis data units (DDU), a custom currency for the Dynatrace platform, which users have said makes pricing opaque and hard to predict.

Choose Elastic for its open-source, free Datadog alternative tools

An open-source data search and analytics engine, Elastic is part of the ELK Stack, which refers to Elasticsearch, Kibana, Beats, and Logstash. By combining these various open-source tools, users can build a data analytics aggregation solution that allows them to collect and analyze large bodies of observability data. The Elastic baseline solution is available as a cloud-hosted managed service deployed on the major public cloud platforms or as a self-hosted version.

Strengths

  • One of the most significant advantages of using the ELK Stack is that it is open source and free to use for public, private, or hybrid cloud deployments. The company charges for its cloud-hosted managed services.

  • Ability to solve observability and security use cases via its SIEM product.

  • Support for ingesting OpenTelemetry metrics, logs, and traces, provides easy integration with competitive/complementary tools, and reduces vendor lock-in

Weaknesses

  • Pricing for their cloud offering is resource-based, forging the industry norm of ingestion per-seat or -host licensing models. While this approach can lead to more predictable monthly statements, initial sizing of your environment can be challenging.

  • The relative effort required to roll your Elastic stack (in a public, private or hybrid cloud deployment) versus paying the company to access it via the cloud is considerable.

Honeycomb is an alternative if you need stronger OpenTelemetry

Founded in 2016 by former Facebook engineers Christine Yen and Charity Majors, Honeycomb, a SaaS-delivered observability platform, combines application performance monitoring, infrastructure monitoring and log management.

Strengths

  • Honeycomb has quickly established itself as a capable monitoring platform and observability provider with a long list of reference clients.

  • Recent updates have strengthened support for OpenTelemetry, added an API for data exchange, and introduced metrics.

  • The company’s BubbleUp feature, which makes heatmaps interactive, is a crucial selling point. When a range is selected, BubbleUp generates a series of histograms that explain how a subset of data differs from other data, surfacing the most likely attribute to explain anomalous behavior.

Weaknesses

  • While Honeycomb supports all three dimensions of telemetry data, it is not traditional monitoring software. It has minimal alerting functionality: no service maps and does not support infrastructure inventory and traversing it.

  • Features like BubbleUp attempt to reduce the burden of troubleshooting, but Honeycomb requires a sufficiently capable troubleshooter to utilize it effectively.

  • Honeycomb delivers its service from one US region, limiting it from a latency and security perspective.

New Relic is a Datadog alternative for mid-sized companies

The New Relic One platform is a popular monitoring and observability platform covering APM, digital experience monitoring (DEM), and infrastructure monitoring. It emphasizes customizability; users say they also like the product for its ease of deployment. The platform is used widely by mid- and large-sized companies.

Strengths

  • Despite some instability in recent years, New Relic is still a significant player in the APM space appealing to developers and DevOps audiences.

  • A new pricing model, investment in a Kubernetes debugging tool (via the acquisition of Pixie Labs), and a new CEO are focusing New Relic to more effectively target larger enterprises and seize more of the market opportunity around cloud-native applications.

Weaknesses

  • New Relic is primarily an observability offering. While it announced a Vulnerability Management tool in May 2022, it is not considered a security platform. It will need to prove the breadth and strength of its capabilities beyond APM.

Choose Splunk for a modular approach to observability and security

While it supports extensive observability and security use cases, Splunk takes a modular approach to deliver broad-based functionality consisting of multiple (five+) products that are not integrated or unified. For example, Splunk Observability Cloud combines metrics, traces, end-user experience and incident response capabilities. It does not include the Splunk Platform (Splunk Cloud or Splunk Enterprise) – the company’s core log analytics platform — they are entirely different products.

Strengths

  • Ability to solve for both observability and security use cases, although in a highly modular format

  • Support for OpenTelemetry

  • Enterprise customer base

Weaknesses

  • Complex licensing structure: Splunk introduced simplified single SKU pricing for Observability Cloud. However, pricing can be complicated and relatively high when purchased alongside Splunk Enterprise, especially for low volumes of hosts.

  • Lacking full integration: Splunk is still working to integrate its entire suite of APM and observability offerings with Splunk Synthetics (Rigor) and Cloud Network monitoring (Flowmill) not yet integrated. Some clients have expressed confusion about why two separate log solutions (Log Observer and Splunk Cloud) are needed.

When choosing an APM and observability solution, choose alternatives for today and tomorrow

Datadog may be a widespread cloud monitoring and observability solution, but it’s not always ideal. Depending on your priorities, a platform, such as Sumo Logic may make more sense to address advanced troubleshooting and broad-based visibility and security requirements. Datadog’s relatively inflexible pricing model makes it challenging to obtain the features you need cost-effectively.

Learn more. Download the complete comparative chart of Datadog alternatives.

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Manny Lopez

Manny Lopez

Competitive Intelligence Principal

More posts by Manny Lopez.

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