Best 10 Observability Tools For IT Teams (2026)
We reviewed 10 observability platforms on data ingestion depth, correlation capabilities, and the quality of the insights they surface. The best ones reduce mean time to resolution significantly.
Written by
Craig MacAlpine
Technical Review by
Laura Iannini
Quick Summary
Observability tools collect and correlate metrics, logs, and traces from across IT infrastructure — giving teams a unified view of system health that basic monitoring cannot provide. Monitoring tells you something broke; observability helps you understand why, which is what determines mean time to resolution. We reviewed 10 platforms and found ManageEngine OpManager Plus, Cisco AppDynamics, and Datadog Observability Pipelines to be the strongest on correlation depth and actionable insights.
Observability is now infrastructure shorthand for monitoring that actually reveals what’s happening when systems fail. The problem is building observability usually means stitching together five or six point solutions. You end up with a network monitoring tool, a separate APM platform, log management somewhere else, container monitoring layered on top, and then a SIEM for security. Each tool requires different expertise. Context switching kills productivity. And nobody can see the full picture when it matters most.
Real observability requires three pillars: metrics that show what’s happening, logs that explain why, and traces that map dependencies. Most platforms excel at one or two. Finding a solution that handles all three without requiring three different admin teams is harder than it should be. Add cloud-native complexity and multi-vendor infrastructure, plus hybrid environments to the equation, and you’re building custom integrations just to see your own infrastructure.
We evaluated eight observability platforms across consolidation capabilities, automatic discovery, alert accuracy, query performance, integration range, and ease of deployment. We evaluated how each handles hybrid infrastructure, cloud-native workloads, and the operational overhead once deployed. We reviewed customer feedback to identify where vendor promises diverge from real-world performance.
This guide gives you the framework to choose observability that actually provides visibility without creating another tool management nightmare.
Our Recommendations
We found these platforms fit different priorities for observability at scale. Choose based on your consolidation needs, cloud readiness, and monitoring depth requirements.
- Best For Unified Operations Without Tool Sprawl: ManageEngine OpManager Plus consolidates network, server, app, and traffic monitoring in one console across 10,000+ devices.
- Best For Cloud-Native Unified Observability: Sumo Logic Observability combines log management, metrics, and tracing with auto-generated topology and query-based alerting for hybrid deployments.
- Best For Automatic Discovery With AI-Driven Analysis: Dynatrace’s OneAgent discovers components automatically with zero config and delivers causation-based root cause analysis through Davis AI.
- Best For Real-Time Granularity Without Sampling: IBM Instana delivers one-second monitoring granularity without sampling and deploys with fast initial setup and automatic agent-based discovery.
- Best For Hybrid Environments With Clean Dashboards: SolarWinds Observability unifies applications, infrastructure, logs, databases, and networks with clean dashboards that surface problems fast.
ManageEngine OpManager Plus is an IT operations management solution that supports network observability across network performance, traffic analysis, configuration management, firewall management and analysis, app performance monitoring, IP address management, and storage monitoring. Reporting and metrics are available in a single, well-designed admin console.
ManageEngine OpManager Plus Key Features
OpManager Plus provides granular in-depth operations monitoring with over 2,000 metrics available across up to 10,000 devices. Teams can monitor and manage physical and virtual server infrastructure to identify and quickly remediate any issues or risks. Configuration and compliance management supports devices from over 200 vendors, and granular bandwidth performance visibility includes over 200 pre-built performance reports.
The platform enables IT teams to manage security controls including firewall rules and policies, access controls, and compliance enforcement. OpManager Plus supports 24/7 monitoring, bandwidth optimization, and integrations with service desk tools within a unified admin console.
Our Take
ManageEngine OpManager Plus is a strong option for IT teams that need to consolidate network monitoring, configuration management, and security controls in a single platform rather than using multiple tools. The depth of metrics across 10,000 devices is good to see.
Strengths
- Over 2,000 metrics available across up to 10,000 devices
- Configuration and compliance management for devices from over 200 vendors
- Unified console covers network, server, firewall, and application monitoring
- Over 200 pre-built performance reports for bandwidth analysis
- Integrations with service desk tools for streamlined workflows
Cautions
- Pricing not publicly available; requires contacting sales for a quote
Cisco AppDynamics
Cisco AppDynamics is an enterprise APM platform for large, complex environments. What sets it apart is the ability to link application performance directly to business outcomes, so your ops and engineering teams get evidence to prove impact, not just flag incidents. We think it’s best suited for enterprises with the resources to invest in deep application telemetry.
Cisco AppDynamics Key Features
AppDynamics provides full stack visibility across public, private, and multi-cloud deployments with low overhead monitoring agents. We found the machine learning driven root cause analysis cuts MTTR quickly. The correlation between backend database queries and front end latency pinpoints ownership fast, which removes a lot of the internal blame cycles that slow incident response. Synthetic monitoring runs scheduled user flow simulations, so your team spots degradation before real users do.
What Customers Say
Users say transaction tracing and dashboard visibility are strong once teams get up to speed. Mapping front end latency to backend queries saves real engineering time. There is one limitation to be aware of: the feature set is deep, and onboarding takes longer than expected, especially for less experienced teams. Some customer reviews note that since the ThousandEyes integration and rebranding, knowing who to contact for licensing and account issues has become harder.
Our Take
If your team has the capacity to onboard properly, the return is clear. The KPI correlation helps performance teams speak the same language as business stakeholders, which is a meaningful advantage. Leaner teams without dedicated APM engineering support may find the cost outpaces the value.
Strengths
- ML-driven root cause analysis reduces MTTR across multi-cloud environments
- Full stack visibility links front end latency to backend queries
- Low overhead agents support scale without degrading performance
- Synthetic monitoring catches degradation before real users do
Cautions
- Users report steep onboarding curve without dedicated APM expertise
- Reviews note support contacts have become unclear since the ThousandEyes integration
Datadog Observability Pipelines
Datadog Observability Pipelines gives platform teams direct control over data movement, transformation, and routing at petabyte scale. We think it’s a strong option for large enterprises dealing with complex data flows, compliance requirements, and real observability cost pressure. If controlling data volume and routing directly impacts your budget, this is well worth considering.
Datadog Observability Pipelines Key Features
Observability Pipelines lets you collect data from any source and send it anywhere, including on-premises storage, which removes vendor lock-in from your stack. We found the configurable sampling and aggregation effective at cutting data volume while keeping KPI trends intact. Sensitive data redaction happens before data leaves your infrastructure, which matters when navigating data residency laws. Automatic parsing, enrichment, and schema enforcement keep data quality consistent across the pipeline without manual intervention at scale.
What Customers Say
Users say Datadog’s real strength is consolidating infrastructure metrics, APM, and log management into one place. During incidents, teams pivot from a CPU spike to the relevant trace and logs in seconds. Alerting across multiple metrics is a meaningful step up from single threshold monitoring. That said, some customer reviews note that the UI has a steep learning curve, especially for new team members. Log indexing costs are a recurring concern, and the gap between ingesting logs and actually searching them forces hard decisions about retention.
Our Take
We think Observability Pipelines suits large enterprises where controlling data volume and routing directly impacts cost. The pipeline control is serious. Your team needs operational maturity and dedicated platform engineering to get full value from it. For smaller teams, the cost structure may not fit.
Strengths
- Configurable sampling and aggregation cuts data volume without losing KPIs
- Sensitive data redaction happens before data leaves your infrastructure
- Routes data anywhere, including on-premises, removing vendor lock-in
- Automatic parsing and schema enforcement at petabyte scale
Cautions
- Users report the UI has a steep learning curve for new teams
- Customers note log indexing costs force hard decisions on data retention
Sumo Logic Observability
Sumo Logic Observability is a cloud-native platform that unifies log management, metrics, and tracing for teams running hybrid or multi-cloud environments. It also doubles as a SIEM, which makes it a strong option for organizations wanting to consolidate observability and security into one tool. We think it’s one of the more accessible platforms on the market for teams without deep observability experience.
Sumo Logic Observability Key Features
The platform automatically builds application topologies by correlating traces, logs, and metrics in real time. We found this particularly useful for understanding service dependencies without manual mapping. Compliance coverage is strong, with PCI, HIPAA, SOC 2 Type II, GDPR, and FedRAMP certifications. Flexible licensing and data tiering help manage costs as log volumes grow.
What Customers Say
Users praise the query-based alerting, especially with PagerDuty integrations. Teams trigger alerts directly from log queries, catching issues as they happen rather than after the fact. There is one limitation to be aware of: some customer reviews note that query performance slows significantly with large datasets and long retention periods. Anomaly detection capabilities also lag behind more specialized competitors.
Our Take
We think Sumo Logic fits best if you need observability and SIEM capabilities in one platform without managing separate tools. The setup process is easy and the compliance certifications make it accessible. If your priority is fast queries over massive log volumes, evaluate alternatives alongside it.
Strengths
- Auto-generates application topologies from correlated logs, metrics, and traces
- Query-based alerting with PagerDuty integration for proactive detection
- FedRAMP, HIPAA, PCI, and SOC 2 Type II certified
- Flexible data tiering controls costs as log volumes scale
Cautions
- Customers note query performance slows with large datasets and long retention
- Reviews note anomaly detection lags behind specialized competitors
Dynatrace
Dynatrace is an AI-driven observability platform built for enterprises managing complex, dynamic environments. We were impressed by the automatic discovery and causal AI analysis, which significantly reduce manual troubleshooting overhead. We think it’s best suited for cloud-native environments where infrastructure changes constantly and manual configuration creates drift.
Dynatrace Key Features
The OneAgent automatically detects applications, containers, and services at startup with no manual configuration or code changes. Davis, the built-in AI engine, performs causation-based root cause analysis automatically. Instead of correlating alerts and hunting through logs, you get direct answers about what broke and why. The platform also learns normal performance patterns dynamically, establishing baselines without manual threshold tuning. Real-time entity topology maps dependencies across your stack, showing how issues propagate through interconnected services.
What Customers Say
Users appreciate the quick installation and intuitive interface. Davis insights get consistent praise for surfacing problems fast. The ability to connect applications and maintain data sources across the platform works well. That said, some users report that network, database, and infrastructure monitoring capabilities trail specialized competitors. The Dynatrace Query Language has a learning curve, though newer AI features that generate queries from natural language help.
Our Take
We think Dynatrace is a very strong option for enterprises prioritizing automatic discovery and AI-driven troubleshooting. The zero-touch approach delivers real value in dynamic environments. If you need deep network or database monitoring, evaluate whether Dynatrace covers your specific requirements before committing.
Strengths
- OneAgent auto-discovers with no manual configuration or code changes
- Davis AI delivers causation-based root cause analysis automatically
- Real-time topology maps entity dependencies and impact propagation
- Dynamic baselining learns normal performance patterns on its own
Cautions
- Users report network and database monitoring trails specialized competitors
- Customers note some features feel underdeveloped next to the core APM
Grafana Cloud Frontend Observability
Grafana Cloud Frontend Observability is a hosted RUM service for web applications. It captures page load times, user interactions, and layout shifts to surface frontend issues before users notice. We think it’s a strong option for engineering and platform teams already invested in the Grafana ecosystem, where it adds frontend visibility without introducing yet another tool.
Grafana Cloud Frontend Observability Key Features
The platform reconstructs user behavior leading up to a specific issue and correlates that data with backend requests. We found this front to back connection cuts debugging time considerably. Your engineers stop guessing whether slowness lives in the frontend or the API. Error triage includes contextual metadata and severity scoring based on volume and frequency. Performance metrics segment by user group, so your product and engineering teams see which audience segments experience the most friction.
What Customers Say
Users say dashboarding is the standout feature. Combining frontend performance data with logs, metrics, and traces in one Grafana view accelerates incident response. Managed deployment keeps signals and alerts online even during your own infrastructure outages. There are trade-offs. Some customer reviews note that advanced features carry a steep learning curve, and billing controls on the managed offering have caused concern for teams watching observability spend closely.
Our Take
We think Frontend Observability fits engineering teams already invested in the Grafana ecosystem. The RUM to backend correlation shortens MTTR meaningfully, and managed hosting removes operational overhead. If you’re not already on Grafana Cloud, the integration overhead may outweigh the RUM benefit.
Strengths
- Session reconstruction correlates user behavior to backend requests
- Automatic error grouping with severity scoring prioritizes by impact
- Managed hosting keeps alerts online even during your own outages
- Native Grafana Cloud integration avoids adding another tool
Cautions
- Users report advanced features carry a steep learning curve for new teams
- Reviews note billing controls on the managed offering need improvement
IBM Instana
IBM Instana is a real-time observability platform built for DevOps, SRE, and ITOps teams managing microservices and containerized environments. We were impressed by the one-second monitoring granularity, which captures 100% of all requests without sampling. We think it’s a good fit for fast-moving teams focused on MTTR reduction in dynamic environments.
IBM Instana Key Features
A single lightweight agent per host discovers components automatically and deploys sensors without manual configuration. Instana now supports over 300 platforms, and the automatic dependency mapping eliminates the constant tagging and manual configuration other enterprise tools demand. The dynamic graph visualization makes performance issues accessible even to project managers without deep technical backgrounds. Integration with IBM Turbonomic provides a broader view across infrastructure.
What Customers Say
Users consistently praise the easy initial setup and automatic discovery. The high-speed log and trace searches that link IT data directly to business context help keep development cycles fast. There is one limitation to be aware of: some users note that historical data retention and long-term trend analysis feel limited since the platform optimizes for real-time visibility over long lookbacks. Transaction visibility for third-party integrations can also be opaque across environments.
Our Take
We think Instana is best suited for teams prioritizing real-time troubleshooting over historical trend analysis. The automatic discovery and one-second granularity excel in dynamic microservices environments. If you need months of historical data for pattern detection or highly customizable dashboards for non-technical stakeholders, you may find limitations.
Strengths
- One-second granularity without sampling for precise issue isolation
- Single agent auto-discovers and maps dependencies across 300+ platforms
- Easy setup with no manual configuration or instrumentation
- Dynamic graph visualization accessible to non-technical users
Cautions
- Customers note historical data retention and trend analysis feel limited
- Users report third-party integration visibility can be opaque
New Relic Observability Platform
New Relic is a unified observability platform that covers networks, infrastructure, applications, and end user telemetry. We think it’s a strong option for SRE and engineering teams looking to consolidate observability into a single platform. New Relic ingests all telemetry without sampling, which means your teams stop compromising on which signals to retain.
New Relic Observability Platform Key Features
The platform instruments all telemetry into one cloud platform without requiring sampling. We found the unified application and infrastructure correlation cuts diagnosis time for SRE teams. AI assistance, including the newer SRE Agent for automated incident response and Intelligent Root Cause Analysis, reduces manual effort when navigating signals across a complex stack. Coverage spans the full software lifecycle, from development through production monitoring.
What Customers Say
Users say alert quality stands out. Graphs and error details appear alongside notifications, so engineers arrive at incidents with context, not just a trigger. APM is particularly valuable for SRE teams linking application behavior to infrastructure events. That said, according to customer feedback, separate agent requirements across different features have caused friction. In regulated environments, introducing each new agent requires additional security review, slowing adoption.
Our Take
We think New Relic suits engineering and SRE teams consolidating observability into a single platform. Ingesting all telemetry without sampling matters more as your stack grows across cloud providers and services. If your organization has strict agent controls or compliance gating, factor in the extra onboarding overhead.
Strengths
- Ingests all telemetry without sampling for full signal fidelity
- Unified app and infrastructure correlation cuts SRE diagnosis time
- AI assistance and SRE Agent reduce manual effort across the stack
- Alerts include contextual graphs and error details for instant context
Cautions
- Users report per-capability agents add security review overhead in regulated orgs
- Customers note full value requires broad instrumentation and significant setup
SolarWinds Observability
SolarWinds Observability is a SaaS platform designed for DevOps, IT, and Cloud Ops teams managing hybrid environments. It covers applications, infrastructure, logs, databases, digital experience, and network monitoring in one unified solution. We think it’s a good option for teams wanting consolidated visibility without managing multiple point solutions, especially organizations already invested in the SolarWinds ecosystem.
SolarWinds Observability Key Features
The platform presents database and infrastructure issues without overwhelming you with noise. We found the dashboards practical and readable even for team members who aren’t database specialists. Slow queries, high CPU, and memory problems get highlighted clearly without requiring manual log diving. Multi-database support allows consolidated monitoring from a single interface. SolarWinds recently launched SW1, an agentic AI teammate designed to move IT teams from reactive problem-solving to autonomous operational resilience.
What Customers Say
Users appreciate the clean interface and straightforward monitoring once configured. The ability to track metrics like CPU, memory, and network latency with customizable dashboards gets consistent praise. Integration with Hybrid Cloud Observability provides a unified view across environments. There are trade-offs. Based on customer reviews, initial deployment requires significant technical expertise and configuration time. Only ServiceNow ITSM integrates out of the box, and other platforms need custom work.
Our Take
We think SolarWinds Observability works best for teams already invested in the SolarWinds ecosystem or those needing broad coverage across cloud and on-premises systems. The clean dashboards surface problems fast without requiring deep database expertise. If you’re a smaller team with budget constraints or need deep ITSM integrations beyond ServiceNow, evaluate the total cost and integration requirements carefully.
Strengths
- Clean dashboards surface issues without deep database expertise
- Unified monitoring across apps, infrastructure, logs, databases, and networks
- AIOps correlation and forecasting for complex distributed environments
- Multi-database monitoring from a single interface
Cautions
- Users report initial deployment requires significant expertise and configuration
- Reviews note only ServiceNow ITSM integrates out of the box
Splunk Enterprise
Splunk Enterprise is a data platform for IT monitoring, analytics, and security across cloud-native and on-premises environments. Now fully owned by Cisco following the $28B acquisition completed in March 2024, Splunk combines application performance monitoring, infrastructure visibility, and incident management into a unified suite with deep search and analytics capabilities. We think it’s best suited for larger organizations with dedicated platform teams and substantial data budgets.
Splunk Enterprise Key Features
The APM component provides NoSample distributed tracing, capturing every transaction rather than statistical samples. We found this valuable for tracking down intermittent issues that sampling would miss. Code-level visibility helps pinpoint exactly where performance degrades. Infrastructure monitoring delivers real-time alerts with instant visibility across hybrid cloud environments. Splunk On-Call automates incident response to reduce on-call burden.
What Customers Say
Users praise the platform’s versatility and intuitive dashboards for viewing observability and security events together. Native integrations, including Microsoft Purview DLP, implement easily and work reliably. The dynamic dashboards prove particularly useful for incident management workflows. There is one limitation to be aware of: costs scale aggressively with data volume. Some customer reviews note that initial setup and query writing require skilled resources. Some users also note that since the Cisco acquisition, platform innovation has slowed relative to market expectations.
Our Take
We think Splunk fits best for enterprises with dedicated platform teams that need unified observability and security analytics with mature tooling. The depth of analytics and flexibility justify the investment when you need that power. If you’re a smaller team or cost-sensitive on data ingestion, evaluate whether the full Splunk capability matches your actual requirements.
Strengths
- NoSample tracing captures every transaction for complete visibility
- Dashboards unify observability and security event monitoring
- Native integrations like Microsoft Purview DLP implement easily
- Splunk On-Call automates incident response to reduce on-call burden
Cautions
- Users report costs scale aggressively with data volume
- Customers note setup and query writing require skilled resources
What To Look For: Observability Solutions Checklist
When evaluating observability platforms, we’ve identified seven criteria that determine whether you get real visibility or just another monitoring tool.
• Automatic Discovery: Does the platform auto-discover applications, services, and dependencies? Or do you manually tag everything? Zero-config discovery matters when your infrastructure changes constantly. Manual instrumentation doesn’t scale in dynamic environments.
• Full Three Pillars: Does the platform handle metrics, logs, and traces in a unified way? Or does it excel at one while feeling half-baked at the others? True observability requires all three. Integration between pillars matters more than having all three.
• Query Performance at Scale: Test queries with your actual data volume. Does performance degrade significantly with large datasets? Can you query 90 days of logs in seconds or does it timeout after five minutes? Performance matters when you’re troubleshooting production incidents.
• Alert Accuracy and Tuning: Can you fine-tune alerts without drowning in noise? Does the platform learn baselines automatically or require constant manual threshold adjustment? Alert fatigue kills the value of observability.
• Integration range: How many third-party tools integrate natively? Do you need custom webhooks for everything outside the vendor’s ecosystem? Broad integration reduces operational overhead.
• Cost Model and Data Governance: How does pricing scale with your data volume? Can you reduce costs by sampling or data tiering? Some platforms charge per GB ingested, others per user or per resource. Understand the model before committing.
• Deployment Time and Expertise Required: How long does initial deployment take? Does it require deep platform expertise or can a general IT engineer handle it? Some tools are operational within days, others demand weeks of configuration and tuning before delivering value.
How We Compared The Best Observability Tools
Expert Insights is an independent editorial team that researches, tests, and reviews security and infrastructure solutions. No vendor can pay to influence our review of their products. Our Editor’s Scores reflect product quality only. We map the observability vendor market across cloud-native and traditional infrastructure before testing.
We evaluated eight observability platforms across automatic discovery, query performance, alert accuracy, integration range, multi-pillar capabilities (metrics, logs, traces), deployment time, and operational overhead. We evaluated each platform against real-world hybrid and multi-cloud scenarios. We assessed ease of setup, dashboard intuitiveness, customization flexibility, and skill requirements for ongoing management.
Beyond hands on evaluation, we conducted market research across the observability market and reviewed customer feedback to validate whether vendor marketing aligns with actual operations. We spoke with platform teams about architecture decisions, roadmap priorities, and scalability limitations. Our editorial and commercial teams operate independently. Vendor relationships never influence our testing or assessments before publication.
This guide is updated quarterly. For complete details on our methodology, visit our How We Test & Review Products.
The Bottom Line
The best observability solution depends on your architecture, team expertise, and whether you prioritize range or depth.
For IT teams tired of switching between network, server, and application monitoring tools, ManageEngine OpManager Plus consolidates monitoring into one console. The 200+ pre-built reports accelerate capacity planning.
For cloud-native environments where automatic discovery and AI-driven troubleshooting matter, Dynatrace delivers zero-touch discovery with Davis AI for causation-based root cause analysis. If you need unified observability and SIEM capabilities, Sumo Logic Observability combines both without separate tools.
For real-time microservices monitoring at one-second granularity, IBM Instana prioritizes instant visibility over historical analysis. For database and hybrid infrastructure clarity, SolarWinds Observability presents issues clearly without specialized database knowledge.
For enterprises with dedicated platform teams and large data budgets, Splunk Enterprise delivers the depth of analytics and flexibility that mature organizations demand. Read the detailed reviews above to evaluate the trade-offs between consolidation, automation, and analytical depth that matter for your specific infrastructure and team.
FAQs
Everything You Need To Know About Observability Tools (FAQs)
Observability tools allow you to monitor your system’s current state, providing reports and metrics on data and processes. This data includes metrics, traces, and logs. Observability uses data generated by endpoints and services in your multi-cloud computing environment, to grant extensive insight across your entire network. Each asset, be it a device, hardware, software, container, or open-source tool has a record of all activity. Observability helps teams understand events so they can get a wider understanding of their network. From here they can detect and remediate issues faster, ensuring that everything is working correctly and efficiently.
Observability tools act as a centralized platform for aggregating and visualizing this telemetric data. They monitor application behavior and infrastructure, before performing careful analysis on it, then delivering actionable insights. This aids organization’s in being able to spot and address problems before they have a chance to develop. Observability tools integrate a range of monitoring capabilities, ensuring that they can discover deep, meaningful insights to help find issues, optimize performance, and ensure continued availability.
Every aspect of your environment will generate a record of its activity, giving a wealth of data that, if utilized properly, provides an insight into your entire system. This data can be used to identify areas that need improving, performance levels, and any outstanding and developing issues. Observability tools measure and analyze system performance and health, using this telemetry that comes from endpoints and services in your multi-cloud environment.
Using this data, observability tools can detect, analyze, and help teams understand the significance of events that occur. This gives an insight into network operations, application security, software development life cycles, and end-user experiences. Going beyond monitoring, observability tools can also identify trends and anomalies, send alerts, and will use data optimization and correlation to produce actionable insights.
When looking for an observability tool for your organization, you should consider the following features:
- Compatibility: Ideally, your solution should be compatible with a range of environments, including public, private, and multi cloud settings.
- Visibility: Your solution should have complete, holistic visibility with no blind spots. It should offer a comprehensive view of your entire network in order to deliver the best possible service.
- Data Processing: Data processing should be efficient and fast. Some solutions are able to direct certain data to storage units and retrieve it when needed.
- Security And Compliance: Your chosen solution must have robust security features – for example, redacting sensitive information before it leaves your organization – and adhere to any compliance guidelines relevant to your business.
- Alerting: The solution should continuously monitor telemetry data and alert administrators when certain conditions are met and when they aren’t, helping them to identify and investigate critical events.
- Anomaly Detection: The solution should use artificial intelligence and machine learning to automate anomaly. AI can be used on datasets to understand “normal” behavior and create a baseline from which the solution can identify abnormal behavior.
- Data Optimization: Automated capabilities such as data optimization and storage allow for continuous control over mass volumes of data and any costs associated with it.
- Dashboards: Dashboards should be intuitive, clean, easy to navigate, and updated in real-time. Your solution will be collecting and processing mass volumes of telemetry data which needs to be presented in a digestible way. While customization allows for more flexibility, pre-built dashboards can reduce time spent on configurations.
- Customization: Your solution should be customizable to your specific business needs, including configurations, and dashboards.
- Distributed Tracing: Your solution should profile and monitor applications, honing in on exactly where failures and problems arise and identifying the cause.
- Data Correlation: Data correlation is an important feature that can aggregate and pool data, helping to identify trends across data sets and present it in a unified way.
Written By
Craig MacAlpine is CEO and Founder of Expert Insights. Before founding Expert Insights in August 2018, Craig spent 10 years as CEO of EPA Cloud, an email security provider that rebranded as VIPRE Email Security following its acquisition by Ziff Davies, formerly J2Global (NASQAQ: ZD) in 2013.
Craig is a passionate security innovator with over 20 years of experience helping organizations to stay secure with cutting-edge information security and cybersecurity solutions.
Using his extensive experience in the email security industry, he founded Expert Insights with the singular goal of helping IT professionals and CISOs to cut through the noise and find the right cybersecurity solutions they need to protect their organizations.
Technical Review
Laura Iannini is a Cybersecurity Analyst at Expert Insights. With deep cybersecurity knowledge and strong research skills, she leads Expert Insights’ product testing team, conducting thorough tests of product features and in-depth industry analysis to ensure that Expert Insights’ product reviews are definitive and insightful.
Laura also carries out wider analysis of vendor landscapes and industry trends to inform Expert Insights’ enterprise cybersecurity buyers’ guides, covering topics such as security awareness training, cloud backup and recovery, email security, and network monitoring. Prior to working at Expert Insights, Laura worked as a Senior Information Security Engineer at Constant Edge, where she tested cybersecurity solutions, carried out product demos, and provided high-quality ongoing technical support.
Laura holds a Bachelor’s degree in Cybersecurity from the University of West Florida.