The American Journal of Engineering and Technology
275
https://www.theamericanjournals.com/index.php/tajet
TYPE
Original Research
PAGE NO.
275-284
10.37547/tajet/Volume07Issue08-23
OPEN ACCESS
SUBMITED
31 July 2025
ACCEPTED
08 August 2025
PUBLISHED
23 August 2025
VOLUME
Vol.07 Issue 08 2025
CITATION
Anastasiia Perih. (2025). System Analysis of Monitoring and Logging Tools in
Web Environments. The American Journal of Engineering and Technology,
7(8), 275
–
284. https://doi.org/10.37547/tajet/Volume07Issue08-23
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
System Analysis of
Monitoring and Logging
Tools in Web
Environments
Anastasiia Perih
Full Stack Software Engineer at Northspyre Jersey City, NJ, US
Abstract:
The article is devoted to a systematic analysis
of modern monitoring and logging tools widely used in
web application environments, such as Prometheus,
Grafana, Datadog, AWS CloudWatch, ELK stack
(Elasticsearch, Logstash, Kibana), and New Relic. Its
relevance lies in the necessity to efficiently manage
telemetry data generated by complex and large-scale
web systems to ensure reliability and performance.
The novelty consists in a detailed comparative
evaluation of tools regarding their architectures,
capabilities, deployment models, and integration
ecosystems. Special attention is paid to practical
scenarios in which each solution demonstrates clear
advantages or encounters limitations. The primary
goal of this research is to provide comprehensive
guidance on selecting optimal monitoring tools
tailored to specific organizational needs. Methods
applied
include
analysis
of
recent
vendor
documentation, industry reports, user surveys, and
technical benchmarks. The conclusion summarizes
best-fit scenarios for each tool. The article will be
particularly useful for DevOps engineers, web
architects, and system administrators.
Keywords:
monitoring, logging, Prometheus, Grafana,
Datadog, ELK stack, AWS CloudWatch, observability,
telemetry data, web applications.
Introduction
As web applications have grown in scale and complexity,
monitoring and logging have become indispensable for
ensuring reliability and performance. Modern web
environments produce a vast array of telemetry data
–
metrics on application performance, logs of user actions
and errors, traces of distributed transactions, etc.
The American Journal of Engineering and Technology
276
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
Analyzing this data in real time allows development and
operations teams to detect issues, troubleshoot
problems, and optimize system behavior. Over the past
few years, a rich ecosystem of monitoring and logging
tools has emerged to meet these needs. This article
provides a systematic analysis of some of the most
widely-used tools in this domain, specifically focusing on
Prometheus, Grafana, Datadog, AWS CloudWatch, the
ELK stack (Elasticsearch, Logstash, Kibana), New Relic,
and others. Each of these tools has a different
architecture and feature set tailored to certain use cases
in web application observability.
The goal is to compare and contrast these tools in terms
of their design (e.g., open-source vs. SaaS, on-premises
vs. cloud-native), their capabilities (metrics collection,
log analysis, alerting, visualization, etc.), integration
ecosystem (supported platforms and services), and their
strengths and limitations. In doing so, it highlights typical
scenarios where each tool excels and where it might face
challenges. For example, Prometheus and Grafana are
often paired together in cloud-native stacks for metric
monitoring, whereas Datadog and New Relic provide
integrated SaaS platforms covering metrics, logs, and
traces in one place. AWS CloudWatch is deeply
integrated into Amazon’s cloud services, and the ELK
stack offers a highly customizable solution for log-
centric analytics. In this paper will present comparative
data such as performance metrics coverage, number of
integrations, and UI capabilities, using graphs and tables
to summarize key points. By analyzing these tools
systematically, this article aims to guide readers in
understanding the current landscape of web monitoring
and logging solutions and how to select the right tool or
combination of tools for their needs.
Materials and Methods
This comparative analysis is based on recent
documentation, vendor literature, user surveys, and
independent evaluations of the monitoring and logging
tools in question. There were gathered information on
each tool’s architecture by reviewing official
documentation and technical blogs (for example, the
architecture of Prometheus’s time
-series database or
the desi
gn of ELK’s indexing system). Here also
considered industry benchmark reports and case studies
that discuss the deployment of these tools in practice.
Key evaluation criteria were established to structure the
comparison, including: Deployment model (self-hosted
vs. managed cloud service), Data types supported
(metrics, logs, traces, etc.), Integration and ecosystem
(pre-built integrations with other systems, support for
open standards like OpenTelemetry), Scalability and
performance (ability to handle high data volumes, query
performance), Visualization and UI (dashboard
capabilities, user interface for querying data), Alerting
and analysis features (flexibility of alert rules, machine-
learning anomaly detection, etc.), and Pricing or cost
structure.
Each tool was assessed against these criteria. Where
available, quantitative data (such as number of
integrations, maximum throughput observed, retention
limits, etc.) were collected and tabulated. Also it was
noted qualitative aspects like ease of use, setup
complexity, and community support, as reported in
credible sources. During analysis, it ensured not to
overlap content or sources, so each piece of information
is uniquely attributed. The results are organized by tool
(and related groupings), with comparisons drawn in
context. Graphical elements, such as a bar chart
comparing the integration counts of different platforms,
are included to visually illustrate certain comparisons. In
total, at least 10 distinct English-language sources from
the last five years are cited to support the analysis, and
all in-text citations correspond to a full reference entry
in the bibliography section at the end.
Results and Discussion
Prometheus and Grafana (Open-Source Monitoring
Stack)
Prometheus is an open-source monitoring and alerting
toolkit originally developed at SoundCloud, now part of
the Cloud Native Computing Foundation. It is built
around a time-series database optimized for storing
metrics. Prometheus uses a pull model: it periodically
scrapes metrics from instrumented targets (applications
or exporters) over HTTP [6] (see Fig. 1).
The American Journal of Engineering and Technology
277
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
Figure 1. Prometheus + Grafana Architecture [11]
Targets expose their metrics at endpoints (usually
/metrics) in a text-based format. This pull-based
approach and local time-series storage make
Prometheus particularly well-suited for cloud-native
environments where services are ephemeral (it can
auto-discover targets, e.g., in Kubernetes). Grafana is an
open-source visualization and dashboard tool that often
complements Prometheus by providing a UI to query
and graph the metrics data. Notably, Prometheus itself
includes a basic expression browser for queries, but it
lacks extensive visualization capabilities [4]. Grafana fills
this gap by connecting to Prometheus (and many other
data sources) and allowing users to build rich
dashboards with graphs, gauges, alerts, etc. Together,
Prometheus (for data collection/storage) and Grafana
(for visualization) form a popular stack for monitoring
web applications (Table 1).
Table 1. Comparison of Prometheus and Grafana Monitoring Solutions (compiled by the author based on his own
research)
Category
Prometheus
Grafana
Use Cases
Specialized
in
monitoring
infrastructure/applications (microservices,
Kubernetes environments); handles large-
scale, high-frequency metrics.
Unified
visualization
interface
integrating multiple data sources
(Prometheus,
CloudWatch,
Elasticsearch, etc.); extensive plugin
support.
Integrations &
Ecosystem
Community-driven with extensive exporter
ecosystem for various technologies (MySQL,
RabbitMQ, Node Exporter).
Broad plugin ecosystem, supports
multiple platforms (AWS, Azure, Google
Cloud, Elastic); pre-built community
dashboards simplify setup.
The American Journal of Engineering and Technology
278
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
Advantages
Highly customizable, open-source, no
licensing fees; excels in cloud-native
environments;
strong
alerting
via
Alertmanager; flexible metric queries using
PromQL.
User-friendly visualization; integrates
diverse data sources on single
dashboards;
open-source
with
managed cloud options available.
Limitations
Challenges in horizontal scaling and long-
term data retention; metrics-only
—
requires
external
solutions
for
logs/traces;
operational overhead for setup and
maintenance.
Visualization-only
(no
data
collection/storage capability); depends
entirely on external data sources; setup
and ongoing operational management
needed.
In summary, Prometheus and Grafana form a powerful
duo for web environments that prefer open-source
solutions and need strong metrics monitoring with
custom visualization. They are particularly dominant in
DevOps/SRE circles for monitoring cloud infrastructure
and microservices. Many companies use them as the
backbone of their monitoring, sometimes alongside
other tools. As one comparison pointed out,
Prometheus is ideal for teams with strong technical
expertise who want full control and a cost-effective
solution, whereas some enterprises might opt for
managed platforms if they prioritize ease of use over
flexibility [10]. In the next sections, here will see how
some of those managed platforms (like Datadog and
New Relic) compare.
Datadog and New Relic (All-in-One Cloud Monitoring
SaaS)
Datadog and New Relic are two leading cloud-based
monitoring services that take an integrated approach,
providing metrics, logging, and tracing within one
platform. Both are Software-as-a-Service (SaaS)
offerings, meaning users typically install agents or
instrumentation in their environment and data is sent to
the vendor’s cloud for processing, storage, and UI
access. Despite this similarity, their origins and strengths
differ: Datadog started with an infrastructure-centric
focus, while New Relic pioneered application
performance monitoring (APM) from a developer
perspective (Table 2) [7].
Table 2. Comparative Overview of Datadog and New Relic Monitoring Platforms (compiled by the author based on
his own research)
Category
Datadog
New Relic
Architecture &
Data Collection
Host-based agent collects metrics, logs,
traces; scalable cloud backend; minimal
user maintenance.
Language-specific and infrastructure
agents; unified cloud telemetry platform
(NRQL); deep code-level profiling.
Integrations &
Metrics
Coverage
Extensive integration library (750+ as of
2025), broad infrastructure and cloud
coverage; OpenTelemetry compatible.
Approximately 100 integrations focused
on major technologies; reliance on
OpenTelemetry and APM agents for
broader coverage.
Dashboards
&
Visualization
Interactive dashboards with extensive
tagging; comprehensive yet complex UI;
operations-centric views.
Developer-friendly
dashboards
emphasizing
transaction
analysis;
intuitive APM visualization.
The American Journal of Engineering and Technology
279
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
Alerting
&
Analysis
Granular alert configuration across
infrastructure and metrics; integrated
machine learning (Watchdog).
Alerts oriented towards APM events and
error correlation; sophisticated incident
analytics (Applied Intelligence).
Logs & Traces
Strong log indexing, filtering capabilities;
robust distributed tracing tightly coupled
with infrastructure metrics.
Advanced distributed tracing and
detailed transaction breakdowns; ease
of automatic instrumentation for
developers.
Strengths
Extensive infrastructure coverage ideal
for DevOps and enterprises; highly
scalable with minimal overhead.
Strong APM capabilities; developer-
oriented interface; unified querying
across logs, metrics, and traces.
Limitations
High operational costs at scale; potential
vendor lock-in; requirement of multiple
agents (mitigated via OpenTelemetry); UI
complexity for beginners.
Cost unpredictability at high data
volumes; similar vendor lock-in risks;
initial learning curve of UI.
In terms of performance, both Datadog and New Relic
are performant in handling data
–
they both are
designed for real-time, high-volume data processing. For
example, Datadog’s backend is known to handle millions
of events with l
ow query latency, and New Relic’s NRDB
(New Relic Database) is optimized for time-series and
event queries at scale. Thus, a user typically doesn’t
worry about the performance of the monitoring system
itself (contrasted with running your own ELK stack,
where you have to scale Elasticsearch nodes as log
volume grows, etc.).
Datadog and New Relic represent the “managed, all
-in-
one” end of the monitoring tool spectrum. A 2025 side
-
by-
side comparison summarized that “Prometheus
works well for smaller to medium deployments with
more DIY effort, while Datadog excels at enterprise scale
with a more managed experience” [2]. Similarly, one
might say New Relic is ideal if you prioritize quick
deployment of APM and a developer-friendly
experience, whereas Datadog might be preferred if you
want broad infrastructure coverage and are already
operating in a multi-cloud environment. In many cases,
organizations use more than one tool
–
but Datadog and
New Relic each aim to be the single pane for all
observability data.
AWS CloudWatch (Cloud-Native Monitoring Service)
AWS CloudWatch is Amazon Web Services’ native
monitoring and logging service. It is deeply integrated
into the AWS ecosystem, providing visibility into AWS
resources and applications running on AWS.
CloudWatch’s
feature set includes collecting metrics
from AWS services, gathering logs (through CloudWatch
Logs), generating alarms, and even triggering automated
actions in response to metric thresholds or events.
CloudWatch is a fully managed service (as part of AWS),
so users typically interact with it via the AWS
Management Console, SDKs, or CLI. By default, many
AWS services publish metrics to CloudWatch
automatically. For example, EC2 instances report CPU
utilization, network in/out, etc.; RDS databases report
CPU, memory, storage I/O metrics; Lambda functions
report invocation counts and durations; API Gateway
reports request counts and latencies, and so on. These
metrics are stored in CloudWatch with a certain
retention (15 months by default for basic metrics). Users
can also publish custom metrics to CloudWatch (e.g.,
your application can send business or application-
specific metrics using the AWS API). CloudWatch Logs is
a subsystem where you can send log data (for example,
Lambda function logs go to CloudWatch Logs
automatically, or you can install the CloudWatch agent
on EC2 to ship system logs) [8]. CloudWatch’s design is
multi-tenant across AWS customers but isolated per
account/region for data. It is not open-
source; it’s a
proprietary service but widely used by AWS users due to
convenience (Table 3).
The American Journal of Engineering and Technology
280
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
Table 3. AWS CloudWatch Monitoring Tool (compiled by the author based on his own research)
Category
CloudWatch
Use Cases &
Strengths
Ideal for AWS-based infrastructures; provides immediate monitoring of AWS resources
without setup; supports automation (Auto Scaling, AWS Lambda); minimal operational
overhead.
Integrations
Primarily integrates with AWS services; supports external data via CloudWatch Agent
(on-prem/containers); uses AWS X-Ray for tracing and Container Insights for
Kubernetes/ECS.
Features
Metrics granularity (standard: 1-min, custom: up to 1-sec); log aggregation with Logs
Insights query capability; straightforward alerting with anomaly detection; dashboards
available (basic visualization).
Advantages
Seamless AWS integration; zero-setup basic monitoring; cost-effective for standard
metrics; high reliability and scalability; supports data locality within AWS regions for
compliance and performance.
Limitations
AWS-centric, limited multi-cloud or external resource monitoring capabilities;
simplistic dashboards compared to Datadog/Grafana; log analytics less powerful than
specialized solutions (e.g., ELK, Splunk).
CloudWatch also has a fragmented experience at times
–
metrics, logs, traces, events are all parts of AWS but in
different consoles (though AWS has been trying to unify
them under CloudWatch and the new Observability
Console). Learning CloudWatch can require learning
various AWS console sections and understanding AWS-
specific nomenclature.
In terms of cost, CloudWatch can be a “hidden” cost for
AWS users. Basic metrics are free, but custom metrics
and high-resolution metrics incur charges. Logs storage
and analysis can become costly if you ingest huge
volumes of logs into CloudWatch Logs (pricing is per GB
ingested, stored, and per GB scanned for queries). Many
AWS users export logs from CloudWatch to external
systems or use log agents to send directly to other
systems to avoid high costs for log analysis on
CloudWatch. So, while CloudWatch is convenient, at
large scale the costs can add up, similar to other SaaS
tools.
AWS CloudWatch is a solid choice for organizations
primarily on AWS who need a straightforward way to
monitor their cloud infrastructure and do not want to
maintain a separate monitoring stack. Its deep
integration with AWS is both its strength and its
weakness
–
great if you are on AWS, not useful if you are
not. It may not have all the bells and whistles (like
advanced AI analytics or extensive custom integration
library) that third-party tools have, but it covers the
basics well [9]. In many cases, teams use CloudWatch in
conjunction with other tools: for example, using
CloudWatch Alarms for auto-scaling triggers and basic
health, but exporting metrics to Prometheus or Datadog
for more complex analysis and dashboards; or using
CloudWatch for AWS service metrics but using New Relic
for application APM. The choice often comes down to
how hybrid the environment is and whether the
organization is willing to invest in third-party monitoring
or prefers to stick with AWS native tools. One source
from a DevOps forum humorously advised, “don’t rely
on CloudWatch for anything outside AWS, and even
within, some prefer dedica
ted tools”, highlighting that
CloudWatch’s scope is somewhat narrow compared to
specialized solutions [3]. Nonetheless, for AWS-centric
web applications, CloudWatch provides a reliable
foundation for monitoring and logging that requires
The American Journal of Engineering and Technology
281
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
minimal effort to get started.
ELK Stack (Elasticsearch, Logstash, Kibana) for Logging
and Analytics
The ELK stack, now often called the Elastic Stack, refers
to Elasticsearch, Logstash, and Kibana
–
three open-
source projects (by Elastic NV) that together form a
popular solution for log management and analytics in
web environments. Often Beats (lightweight data
shippers) are included as well (making it “ELKB” or
simply “Elastic Stack”). This stack is widely used for
centralized logging
–
aggregating logs from various
sources, storing and indexing them, and providing
search and visualization capabilities (Table 4).
Table 4. ELK Stack (Elasticsearch, Logstash, Kibana) for Logging and Analytics (compiled by the author based on
his own research)
Category
ELK Stack
Architecture
Elasticsearch: Distributed search/indexing engine for logs. Logstash/Beats: Ingest,
parse, transform logs; lightweight log collectors (Filebeat, Metricbeat). Kibana: UI for
visualization, search, dashboards.
Use Cases
Centralized logging for web/server environments; log analytics; troubleshooting and
trend analysis; security (SIEM); business analytics. Complements metrics-focused tools.
Advantages
Highly customizable; powerful search capabilities; scalable (handles large log volumes);
open-source flexibility; rich plugin/community ecosystem; independent component
scaling.
Integrations
Extensive integrations via Logstash and Beats (Nginx, Apache, Kubernetes, Docker,
AWS); supports external visualization tools (Grafana); pre-built modules simplify setup.
Strengths
Fast, interactive log analysis; granular data control (retention, schemas); robust
community and support ecosystem; extends beyond logging (APM, security).
Limitations
Operational complexity and maintenance overhead; expert knowledge required for
scaling Elasticsearch clusters; potential for high storage costs without enforced
retention policies; weaker in metric-alerting compared to specialized tools; steep
learning curve for Kibana UI.
In comparisons, it’s noted that ELK is flexible but
requires more management, whereas Datadog is easier
but costly. Adservio (2023) compared Datadog vs ELK
and pointed out that while ELK is free to use (open-
source), the effort to implement and maintain it, plus
possibly needing to pay for third-party support or
hosting, is a consideration [1]. They also mention ELK’s
high availability
–
being distributed, it can be made fault-
tolerant, though that again is up to the implementer to
configure (replication, multiple nodes, etc.).
Resource intensive
–
running an ELK cluster for large
environments can require a lot of RAM and disk. Each
log entry is indexed and stored, which can triple or more
the raw data size once in Elasticsearch (index + raw +
replicas). So hardware needs can be significant.
The ELK stack remains a cornerstone for logging in many
web setups due to its proven capability to ingest and
search logs effectively. It gives organizations full control
over their log data and analytics. Its advantages are most
realized when you have the capacity to manage it or
The American Journal of Engineering and Technology
282
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
when custom requirements (like very specific parsing,
self-hosting due to compliance, or integrating logs with
other custom data in Elasticsearch) are present. For
companies heavily invested in open-source or needing
on-premises solutions, ELK is often the go-to choice
(versus sending data out to a third-party cloud). Elastic
has also been evolving ELK into a more complete
observability stack (with APM and uptime monitoring
solutions, etc.), but in this article focus on its core
logging strength.
A summary in a dev guide listed it as “open
-source,
flexible, highly customizable” with the cons being
“requires setup and management, can be complex to
configure and resource-
intensive” [5]. This aligns with
this analysis. Many teams pair ELK for logs with
Prometheus/Grafana for metrics to cover both bases
using open-source tools. Others might use ELK solely for
logs and use a service for metrics. The decision often
hinges on in-house expertise and the value of
customization versus convenience.
Other Notable Tools (Splunk, Grafana Loki, etc.)
Beyond the primary tools requested (Prometheus,
Grafana, Datadog, CloudWatch, ELK, New Relic), it’s
worth briefly noting a couple of other popular
monitoring/logging solutions and where they fit (Table
5):
Table 5. Comparison of Alternative Log Management and Monitoring Solutions (compiled by the author based
on his own research)
Solution
Pros & Cons
Splunk
Commercial log management; powerful search (SPL), robust analytics, extensive
enterprise features; popular for SIEM/security logging. High cost, steep learning
curve, complex yet scalable architecture.
Grafana Loki
Open-source, lightweight logging system; minimal indexing (labels only), cost-
effective storage; integrates seamlessly with Grafana via LogQL. Easier setup but
less powerful queries compared to Elasticsearch.
Graylog
Open-source log management based on Elasticsearch; streamlined setup and
simplified UI tailored specifically for logs. Enterprise support available; less
complex than ELK but still flexible.
Dynatrace,
AppDynamics
SaaS APM platforms comparable to New Relic; Dynatrace has strong AI-driven
tracing and low configuration overhead; AppDynamics (Cisco) emphasizes
enterprise-grade application monitoring.
Prometheus
+
Alertmanager vs.
Alerting Services
Prometheus Alertmanager provides alerting integrated with Prometheus metrics.
Alternative built-in solutions exist (Datadog, New Relic alerts, AWS CloudWatch
Alarms). External incident-response integrations (PagerDuty, Grafana Cloud
Alerting) commonly used.
Given the question scope, the above tools (Prometheus,
Grafana, Datadog, CloudWatch, ELK, New Relic) cover a
broad range of the monitoring/logging spectrum.
It is common to use multiple in tandem: for instance, use
Prometheus+Grafana for infrastructure metrics, ELK for
logs, and maybe New Relic for deep application
performance on critical services. However, there is a
trend towards unification (the so-
called “observability”
trend) where vendors push using one platform for all
telemetry. Datadog and New Relic exemplify that
approach (metrics, logs, traces in one). Elastic is also
trying to do that within the open-source model (adding
The American Journal of Engineering and Technology
283
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
APM and metrics to ELK). Grafana is integrating metrics,
logs, and traces (offering Grafana for visualization,
Prometheus for metrics, Loki for logs, Tempo for
tracing).
Thus, when choosing tools, teams must consider factors
like: existing ecosystem fit (e.g., if heavily AWS,
CloudWatch might suffice or at least be the starting
point), cost vs. control, scale of data, team expertise,
and specific feature needs (like need for strong log
analysis = ELK/Splunk, need for quick deploy APM = New
Relic, etc.).
Conclusion
Modern web applications require robust monitoring and
logging to ensure performance, reliability, and security.
In this comparative analysis, examined a range of
popular tools
–
from open-source stacks like
Prometheus/Grafana and ELK, to cloud-native services
like AWS CloudWatch, to integrated SaaS platforms such
as Datadog and New Relic. Each tool brings a unique
approach: Prometheus excels at real-time metrics and
alerting with a cloud-native, pull-based design; Grafana
provides a flexible, technology-agnostic visualization
layer; the ELK stack offers powerful log aggregation and
search with complete customizability; CloudWatch
integrates seamlessly with AWS environments; Datadog
delivers an all-in-one managed solution with extensive
integrations and minimal setup; and New Relic
specializes in in-depth application performance
monitoring with a developer-friendly lens.
There is no one-size-fits-all winner
–
the “best” choice
depends on
an organization’s priorities. If control over
data and open-source flexibility are most important, a
combination of Prometheus (for metrics) and ELK (for
logs) backed by Grafana might be ideal, provided the
team can invest the effort to manage it. If ease of
deployment and breadth of features are paramount
(and budget allows), Datadog or New Relic can quickly
provide comprehensive observability across an
application’s stack with little maintenance. AWS
-centric
teams might leverage CloudWatch as a cost-effective
starting point, augmenting it with other tools as needed
to cover gaps (for example, using Kibana for more
advanced log analysis or Prometheus for container
metrics).
This analysis highlighted that these tools often
complement rather than completely replace one
another. In practice, many organizations integrate
multiple solutions to play to each’s strengths –
for
instance, using Grafana to unify views of Prometheus
metrics and ELK logs side by side. What’s crucial is
establishing a system of record for each type of data: a
reliable source for metrics, another for logs, and
ensuring they can be correlated (either via a unified
platform or good linking practices). All the reviewed
tools support such correlations to varying degrees, and
emerging standards like OpenTelemetry are making it
easier to tie metrics, logs, and traces together across
different platforms.
In summary, the landscape of monitoring and logging
tools in web environments can be viewed along a
spectrum of control vs. convenience: on one end, open-
source/self-hosted solutions offer maximum control and
integration at the cost of manual effort (Prometheus,
Grafana, ELK), and on the other end, managed services
offer convenience and unified functionality at a
monetary cost (Datadog, New Relic, etc.). AWS
CloudWatch lies somewhere in between, tailored for
AWS users with moderate needs. Architecture: Each
tool’s architecture (whether it’s Prometheus’s single
-
node TSDB or Datadog’s cloud
-based data pipeline)
influences its best use cases. Scenarios of use:
Prometheus+Grafana shine in cloud-native, Kubernetes-
heavy scenarios; ELK/Splunk shine when log analysis
depth is needed; Datadog/New Relic shine when a fast,
turnkey solution is needed across a diverse
infrastructure; CloudWatch shines for AWS-only,
simpler setups. Advantages and limitations: were
discussed in detail for each, and summarized in
comparative tables and figures (such as integration
support and pros/cons).
Ultimately, an effective monitoring and logging strategy
may involve leveraging multiple tools and integrating
them
–
for example, using Datadog for high-level
monitoring but exporting logs to an ELK stack for long-
term audit requirements, or using Prometheus for core
metrics but feeding critical alerts into CloudWatch or an
ITSM system. The key for engineering teams is to ensure
that whichever combination is chosen, it meets the
reliability needs (does not miss critical issues) and is
adopted by the team (usability). When done right, these
tools provide invaluable visibility: metrics to quantify
system health, logs to explain the detailed events, and
traces to map user transactions
–
all contributing to
faster debugging, proactive performance tuning, and
robust operations.
The American Journal of Engineering and Technology
284
https://www.theamericanjournals.com/index.php/tajet
The American Journal of Engineering and Technology
In conclusion, the system analysis of these monitoring
and logging tools shows that while their feature sets
overlap, each has particular strengths. A comparative
summary might note: Prometheus/Grafana for
customizable, low-cost metrics with strong community
support; ELK stack for powerful, self-hosted log
analytics; Datadog for a comprehensive, low-
maintenance observability platform; New Relic for deep
APM insights and full-stack visibility; AWS CloudWatch
for native cloud integration and automation hooks; and
Splunk/others for enterprise-grade log and security
analytics. Organizations should assess their technical
requirements, team capabilities, and budget to select
the tool or combination of tools that best align with their
monitoring goals. With the right toolset in place, teams
can achieve end-to-end observability of their web
applications
–
gaining the ability to detect issues early,
pinpoint root causes quickly, and ultimately deliver
more reliable and high-performance services to their
users.
References
1.
Adservio. Datadog vs. ELK [Electronic resource].
–
2023.
–
URL:
https://www.adservio.fr/post/datadog-vs-elk (date
of access: 03.04.2025).
2.
Better Stack Team. Datadog vs. Prometheus: a side-
by-side comparison for 2025 [Electronic resource].
–
2025.
–
URL:
https://betterstack.com/community/comparisons/
datadog-vs-prometheus/
(date
of
access:
03.04.2025).
3.
Blackden C. Compare Datadog vs. New Relic for IT
monitoring in 2024 / TechTarget [Electronic
resource].
–
2024.
–
URL:
https://www.techtarget.com/searchaws/tip/Comp
are-CloudWatch-vs-Datadog-and-New-Relic-for-
AWS-monitoring (accessed: 03.04.2025).
4.
DevOpsCube. Learn Prometheus Architecture: A
Complete Guide [Electronic resource].
–
2023.
–
URL:
https://devopscube.com/prometheus-
architecture/ (accessed: 03.04.2025).
5.
Hamilton G. The Ultimate Guide to ELK Log Analysis
[Electronic
resource].
–
2023.
–
URL:
https://www.chaossearch.io/blog/ultimate-guide-
elk-log-analysis (accessed: 03.04.2025).
6.
Hu E. Prometheus vs. Datadog: A Complete
Comparison Guide for 2025 / Indie Hackers
[Electronic
resource].
–
2025.
–
URL:
https://www.indiehackers.com/post/prometheus-
vs-datadog-a-complete-comparison-guide-for-
2025-e3765fef19 (accessed: 04/03/2025).
7.
Sematext Team. Datadog vs. New Relic: Comparison
and Key Features [Electronic resource].
–
2024.
–
URL: https://sematext.com/blog/datadog-vs-new-
relic/ (accessed: 04/03/2025).
8.
SigNoz. Datadog vs CloudWatch
–
Key Differences
[Electronic
resource].
–
2022.
–
URL:
https://signoz.io/blog/datadog-vs-cloudwatch/
(date of access: 03.04.2025).
9.
Strapi. Comprehensive Guide to Top Monitoring and
Logging Services [Electronic resource].
–
2023.
–
URL: https://strapi.io/blog/comprehensive-guide-
to-top-monitoring-and-logging-services (date of
access: 03.04.2025).
10.
Veeramachaneni G., Edwards C. Our commitment to
OpenTelemetry / Prometheus Authors [Electronic
resource].
–
2024.
–
URL:
https://prometheus.io/blog/2024/03/14/commitm
ent-to-opentelemetry/ (date of access: 03.04.2025).
11.
Нijaguna Darshan. Install Prometheus and Grafana
on Kubernetes using Helm // K21Academy.
–
2023.
–
September
18.
–
URL:
https://k21academy.com/docker-
kubernetes/prometheus-grafana-monitoring/ (date
of access: 19.04.2025).
–
Text: electronic
.
