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Why AWS is poised to lead the Gartner Magic Quadrant for APM and Observability in 2024

Application Performance Monitoring (APM) and observability tools play a crucial role in cloud operations, and indeed, in any operational work. They both help us keep tabs on application performance and provide a world-class end-user experience to our users. There are many APM and observability tools in the market, and of course, there is fierce competition among them.

Gartner released its latest Magic Quadrant for Application Performance Monitoring and Observability last year. The industry widely relies on Gartner as a trusted indicator when clients plan their future technology landscape. It's the one place where every vendor wants to be.

Gartner Magic Quadrant for APM and Observability

Before going through the Gartner findings, let's examine and understand Gartner's rationale for ranking the tools.

What is Gartner looking for when it comes to APM and Observability?

  • Automated Infrastructure Discovery: Automatically find and map application components, including those in cloud services.
  • Versatile Application Monitoring: Monitor applications delivered through browsers, mobile apps, and APIs.
  • Performance Problem Identification: Identify and analyze performance issues and understand their impact on business outcomes.
  • Complete Transactional Observation: Understand how an application behaves throughout its entire transactional process.
  • Seamless Cloud Provider Integration: Integrate with automation tools, service management, and major cloud providers like Amazon CloudWatch, Azure Monitoring, and Google Cloud Operations.
  • Business Activity Monitoring: Analyze KPIs and user journeys comprehensively, from login to check-out.
  • Telemetry Exploration and Analysis: Interactively explore various telemetry types to detect and explain unexpected events, exceptions, and anomalies.
  • Application Security Features: Identify known vulnerabilities and block exploit attempts to enhance application security.
  • Endpoint Monitoring for User Experience: Monitor endpoints to understand user experiences and their impact on business outcomes.
  • Telemetry Ingestion from Various Sources: Ingest telemetry data from hosted or SaaS-based applications.
  • Health and Performance Anomaly Recommendations: Receive recommendations for resolving health and performance anomalies using advanced analytics, often referred to as "AIOps features."
  • DevOps Toolchain Integration: Seamlessly integrate with DevOps toolchains for continuous or progressive application delivery.

Above are some key capabilities or features Gartner is looking out for. Naturally, it's more of a wish list for anyone. While it's a comprehensive capability check Gartner is performing, it's important to be mindful that some of the vendor's tools have different objectives, such as more focus on business-centricity, security, or observability.

As per Gartner's recent evaluation, the leaders in the Application Performance Monitoring (APM) and Observability space include well-established solutions like Dynatrace, Datadog, NewRelic, Splunk, and Honeycomb. These companies are recognized for their innovative features, comprehensive capabilities, and proven track records in providing effective APM and observability solutions. Their market presence and customer satisfaction contribute to their leadership status, making them go-to choices for organizations seeking robust monitoring and observability tools.

On the other hand, the challengers in this competitive landscape consist of notable players such as Amazon Web Services (AWS), Cisco, Microsoft, and IBM. While these companies have a strong market presence and offer competitive solutions, they are positioned as challengers, indicating their potential to advance and challenge the leaders in the near future. Gartner's assessment provides valuable insights for organizations looking to navigate the APM and observability landscape, considering both established leaders and promising challengers in their decision-making processes.

So, what does Gartner think about AWS?

For a start, AWS is in the challenger category, and Gartner has flagged quite a few positives or strengths in its report. AWS is commended for providing analytics and machine learning-based insights, empowering users with advanced data-driven decision-making tools. According to Gartner, AWS stands out for its robust support for modern architecture, enabling businesses to embrace cutting-edge technologies and scalable solutions. Gartner has extensively talked about AWS's extensive geographic footprint and efficient distribution, ensuring reliable service delivery on a global scale.

To be honest, I would say this is a very fair analysis of AWS when it comes to APM and Observability in my mind.

In Gartner's report, they go through areas of concern or things to be cautious about when someone is selecting a particular tool. It's not a completely negative way of putting things but more of a word of being vigilant.

So, what are the areas Gartner has reservations about regarding AWS? (probably the reason why it's not in the leader category)

Early on, Gartner has picked up on the AWS product complexity aspects. It's discussing AWS having more of a set of services that wrap around and provide APM and Observability instead of a single-product approach. Gartner thinks this complex product approach is not that user-friendly and is looking for more of a simplifying product structure. Gartner also goes into detail about why AWS should refine its product strategy, aligning it more closely with market demands to ensure continued relevance and innovation. Finally, it's about cost. Optimizing cost management practices is highlighted as an area for improvement, encouraging AWS to enhance transparency and efficiency in pricing models.

Well, if you go through the so-called warnings of Gartner on AWS, the first thing you realize is they are not such super critical factors in the grand scale of things. Of course, AWS is a cloud provider, and their services have complex product roadmaps since they are not just competing in the space of APM and Observability. On the other hand, someone can argue that AWS provides more services that go beyond and address the entire needs of customers, so the product complexity, on one hand, should be an advantage for them. Pricing is always a debatable topic when compared to other products; let's agree to disagree on this.

Now that you have a clear understanding of Gartner's Magic Quadrant for APM and Observability, where AWS is placed and the rationale behind the decision, let's dive in to find out the key topics of this blog post.

Why AWS is poised to lead the Gartner Magic Quadrant for APM and Observability in 2024

1.AWS is already ticking all the capability boxes that Gartner is looking for in an APM and Observability tool.

Gartner has gone in-depth to provide clarity around what a perfect APM and Observability tool looks like. What this means is we have a relatively easy job of just figuring out whether each capability is met by any tool. So when it comes to AWS, AWS is ticking almost all the capabilities Gartner is looking for. If you want to be in the leader quadrant, the bare minimum is to ensure you have the full capability. That is what AWS has, and the sheer reason that AWS has all the capabilities is a compelling reason to add AWS into the leader category during this year.

Here's a quick capability vs. AWS service mapping, which will provide you with a little detailed view of how AWS capabilities are holistic in nature.

Capability AWS Services
Holistic Transactional Observation AWS X-Ray, Amazon CloudWatch
Automated Infrastructure Discovery AWS Config, Amazon EC2 Auto Scaling
Versatile Application Monitoring Amazon CloudWatch, AWS X-Ray
Performance Problem Identification Amazon CloudWatch, AWS X-Ray, Amazon DevOps Guru
Seamless Cloud Provider Integration Amazon CloudWatch, AWS X-Ray, AWS Config
Business Activity Monitoring Amazon CloudWatch, Amazon QuickSight
Telemetry Exploration and Analysis Amazon CloudWatch, AWS X-Ray, Amazon OpenSearch Service
Application Security Features Amazon Inspector, AWS Shield, AWS WAF
Endpoint Monitoring for User Experience Amazon CloudWatch Synthetics
Telemetry Ingestion from Various Sources Amazon CloudWatch, AWS X-Ray, Amazon Kinesis
Health and Performance Anomaly Recommendations Amazon DevOps Guru, Amazon Lookout for Metrics
DevOps Toolchain Integration AWS CodePipeline, AWS CodeBuild, AWS CodeDeploy
Performance Testing and Load Testing Integration AWS X-Ray, Amazon CloudWatch Synthetics

Some call this year the "Gen AI" year, and if you're into Operations, it's the year of AIOps.

2. AWS offers superior AIOps capabilities that go beyond what Gartner is looking for.

Over the years, AWS has been leveraging AI and ML to power its services and provide greater benefits to its customers. This is an integral part of most AWS service offerings, including those related to APM and Observability. AWS brings a lot of AIOps capabilities to the table, and it's challenging to ignore the sheer presence of them or the value they are adding to AWS. If you want to be at the forefront of the AIOps journey, then AWS's offering is already catering to that. This provides AWS with a greater edge when it comes to their competitors. This is one major factor I would think Gartner will consider during this year's assessment. This will naturally lead to more points for AWS, and just focusing on this area would be enough to propel them into the leader group.

Just to provide a quick recap of the AIOps capabilities AWS is offering, I have listed them below. I'm sure this is a list you want to save and keep, especially if you're into AIOps. This is what you're looking for in APM and Observability, leveraging AI and ML.

  • Anomaly Detection: Automatically detects anomalies in metrics and generates alarms when anomalies are found. Uses statistical and machine learning algorithms to determine normal baseline behavior.
  • Metric Math: Lets you search, filter, or transform metric data to generate new time series metrics. Useful for creating derived metrics and KPIs to better understand performance.
  • Contributor Insights: Analyzes log data to identify root causes of problems and outliers contributing to performance changes. Uses ML to group and analyze log messages.
  • Logs Insights: Automatically discovers log patterns, extracts metrics and entities, and identifies common query patterns from log data to provide insights.
  • CloudWatch Container Insights: Collects metrics, logs, and events from containerized applications and microservices. Includes anomaly detection, metric correlation, and AI-powered troubleshooting.
  • CloudWatch RUM (Real User Monitoring): Uses ML to provide visualizations into client-side application performance.
  • CloudWatch Synthetics: Canary tests that simulate user traffic and workflows to monitor availability and performance. Uses ML to detect anomalies.
  • Forecasting: Provides time-series forecasts for metrics to help with capacity planning and resource optimization.
  • Dashboards: Auto-generation of dashboards based on metrics, resources, and alarms. Helps visualize monitored resources.

In summary, AWS leverages ML extensively for anomaly detection, log analysis, forecasting, and providing automated insights into monitored resources and applications.

So far, I have provided you with two compelling reasons why I think AWS will get into the leader board this year. I'm sure what I've covered up to now is not news for you. It's probably something you're already aware of. But wait, what else do we have to support the AWS case?

3. You remember the re:Invent 2023?

Well, it feels like yesterday we had the re:Invent, and of course, it's what we all were waiting for. AWS didn't disappoint us when it comes to APM and Observability. There were top releases that improve AWS's position. Key releases which I was tracking are as follows:

  • Amazon CloudWatch Logs now offers automated pattern analytics and anomaly detection
  • Amazon CloudWatch Application Signals for automatic instrumentation of your applications (preview)
  • Use Amazon CloudWatch to consolidate hybrid, multicloud, and on-premises metrics
  • Use natural language to query Amazon CloudWatch logs and metrics (preview)
  • New Amazon CloudWatch log class for infrequent access logs at a reduced price

The points mentioned above add a lot of brownie points to AWS's case. Now that it's adding more and more to its AIOps capabilities, it's hard to imagine why it can't make it to the leader group.

4. If you're in AWS, why not use AWS APM and Observability?

I agree; this is one of my weakest arguments in this post. However, for me, AWS is the most popular cloud provider, and many forks are already onboard with AWS. So when they are looking for an APM and Observability solution, it makes sense to go with AWS. AWS provides a comprehensive suite of services that not only streamlines your operations but also offers an all-encompassing solution tailored to your needs. With AWS, you have everything at your fingertips, ensuring seamless integration and a holistic approach to application performance monitoring and observability. While it's a little weak argument, I would say it makes sense. Other vendors will continue to build integrations with AWS, and, well, you don't have to depend on that if you're leveraging what AWS is providing.

Just to solidify my argument here, a few pointers I have picked are as follows:

  • Seamless Integration: AWS APM and Observability Tools offer native harmony with AWS services, ensuring optimal performance and unified management.
  • Deep Service Ecosystem: Leveraging AWS tools provides an integrated experience within the extensive AWS service ecosystem, enhancing visibility and control over diverse cloud resources.
  • Tailored for AWS Infrastructure: AWS-native tools are purpose-built to maximize compatibility and efficiency within the AWS infrastructure, delivering targeted insights and actionable data.
  • Unified Management: By staying within the AWS ecosystem, users benefit from a unified management console, streamlining operations and reducing the complexity of integrating multiple third-party tools.
  • Native Collaboration: AWS APM tools seamlessly collaborate with other AWS services, promoting efficient workflows, automated responses, and deeper insights for applications deployed on AWS infrastructure.

So we have come to my final argument; it's kind of now or never.

5. Playing the maturity model card?

I'm sorry if I confused you with this one. The idea is, if you're into APM or observability, you definitely will have an end game. You will start from some place and then continue your journey in achieving the highest level. Generally, below are the processes we are following to achieve nirvana:

  • Monitoring: Utilizes basic traffic light monitoring to comprehend the availability of individual components constituting IT services.
  • Observability: Examines the behavior of IT environments by capturing metrics, logs, traces, events, and health status.
  • Casual Observability: Contextualizes telemetry data (metrics, traces, events, logs) through a unified topology. Correlates data over time to trace changes as they propagate throughout the system.
  • Proactive Observability with AIOps: Leverages AIOps to sift through vast amounts of data, identifying significant patterns and impactful events.

This approach allows teams to concentrate their efforts on crucial matters.

In case if you have any doubts, let me go in to little detail in to recommended high-level steps for achieving proactive observability with AIOps for a new application deployed on AWS:

Monitoring Maturity:

  • Set up CloudWatch dashboards and alarms for basic traffic light monitoring of resources such as EC2, ELB, and RDS.
  • Utilize service health checks and SDK metrics for application monitoring.
  • Stream logs to CloudWatch Logs.

Observability Maturity:

  • Instrument application code with the X-Ray SDK for distributed tracing.
  • Enable enhanced monitoring for services like EC2, ELB, and Lambda.
  • Leverage CloudWatch Container Insights for ECS/EKS metrics, logs, and events.
  • Stream application logs, events, and metrics to CloudWatch.
  • Aggregate and query telemetry data in OpenSearch.

Causal Observability:

  • Correlate metrics, traces, logs, and events using tools like OpenSearch.
  • Build service maps in X-Ray to visualize dependencies.
  • Query and filter telemetry data by trace IDs.
  • Set up CloudWatch dashboards linking related metrics and logs.

Proactive Observability with AIOps:

  • Employ CloudWatch anomaly detection to identify outliers.
  • Implement DevOps Guru for analyzing telemetry, providing insights, and offering remediation.
  • Develop custom ML models in SageMaker for predictive analytics.
  • Utilize Lookout for Metrics to detect anomalies in KPIs.
  • Automate response and scaling based on CloudWatch alarms.
  • Visualize metrics and traces with tools like OpenSearch Lens and X-Ray Service Graphs.

These steps encompass essential services and capabilities across monitoring, observability, and AIOps on AWS, positioning it hopefully as a leader in the Gartner APM and Observability Magic Quadrant for 2024!

I hope I have convinced you that AWS will be the leader in APM and observability. There are a lot of good things going with AWS, and I rest my case here.

So let me wrap this up. In 2024, AWS is poised to lead the Gartner Magic Quadrant for APM and Observability, driven by its existing capabilities, extensive service offerings, and continuous innovation showcased at re:Invent 2023. AWS's native integration, AIOps features, and comprehensive suite make it the optimal choice for users already in the AWS cloud. With Gartner's sought-after capabilities already covered and AWS offering a swift path to observability maturity, AWS stands as the go-to solution for APM and Observability in 2024.

Quick disclaimer, I'm using most of the tools mentioned in the Gartner report, and this is based on my experience. Of course, I'm sure you can have your favorite tool and rationale for why you think they should be in the leader group too.

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