Top 7 Elastic Observability Alternatives (2025): Features, Pricing & Comparison

Elastic Observability is a comprehensive observability suite from Elastic, the creators of the popular ELK stack (Elasticsearch, Logstash, Kibana). It comes with cost-effective and powerful search capabilities, distributed tracing support, and scalable ingest pipelines. The tool enables engineering teams to ingest, analyze, and visualize metrics, logs, traces, and uptime data in real time. Elastic is again recognized as a Leader in the 2025 Gartner Magic Quadrant for Observability Platforms. 

However, Elastic Observability can be complex to set up, requires significant expertise, and has a steep learning curve. Managing the Elastic Stack even via Elastic Cloud requires constant oversight into indexing and optimizing shard strategies. For companies dealing with large telemetry volumes, Elastic’s cost can spike unpredictably, which affects data retention capacity. Plus, its lack of an on-premise option for Elastic Cloud makes it difficult to maintain compliance for regulated industries.

CubeAPM is a future-ready, developer-first solution. As the best Elastic Observability alternative, it offers native OTEL support and full MELT observability (Metrics, Events, Logs, Traces, RUM, Synthetics, and Error Tracking). It offers smart sampling and granular control over data pipelines, enabling up to 60–80% cost savings compared to traditional observability platforms. Unlike Elastic Cloud, CubeAPM is fully self-hosted, ensuring data residency and compliance with local regulations. Its Slack-based support is also prompt. 

In this article, we’re going to explore the top 7 Elastic Observability alternatives based on criteria like OpenTelemetry support, MELT coverage, cost predictability, deployment flexibility, online reviews, and support quality. 

Top 7 Elastic Observability Alternatives in 2025

1. CubeAPM
2. Datadog
3. Dynatrace
4. New Relic
5. Coralogix
6. Sumo Logic
7. Better Stack

Why Look for Elastic Observability Alternatives?

Despite Elastic Observability’s strong capabilities and flexible stack architecture, many engineering, DevOps, and platform teams are actively moving away from it. Here’s a breakdown of the major reasons driving this shift:

1. High Operational Complexity and Maintenance Overhead

Elastic Observability, built on the ELK stack (Elasticsearch, Logstash, and Kibana), is inherently modular and powerful. But that power comes at a cost. Setting up and maintaining an Elastic-based observability pipeline requires significant operational investment. You’re not just running a monitoring tool; you’re managing a distributed search engine, a logging pipeline, index lifecycles, shard allocations, tiered storage (hot/warm/cold/frozen), and Kibana visualizations.

This complexity is magnified in production environments. Tasks like shard optimization, reindexing, configuring ingest pipelines, and scaling clusters require Elastic-specific expertise. Even on Elastic Cloud, many teams report spending a significant amount of engineering bandwidth just to keep the stack stable and performant.

For lean teams or those without dedicated infrastructure engineers, this becomes unsustainable, especially when compared to newer tools that offer plug-and-play observability with minimal setup.

2. Unpredictable and Escalating Costs at Scale

While Elastic’s open-source roots suggest cost efficiency, the reality is that large-scale observability with Elastic often becomes expensive and difficult to forecast. 

Elastic Cloud’s entry-level pricing starts at $99–$184/month, but production workloads are billed based on actual resource consumption—RAM, CPU, storage tier, and indexing throughput—which can increase quickly with scale

Coming to another pricing tier – Elastic Observability Serverless – its cost is based on pay-per-use. It starts from:

• Ingest: Starts from $0.15/GB of ingested data 
• Retention: Starts from $0.02/GB of data retained per month
• Egress: 50 GB free, then $0.05/GB of data transferred per month

Cost Estimation for Elastic Observability Serverless

For example, a mid-sized team ingesting 10TB/month, with moderate synthetic and RUM usage, can expect to spend $5,000–$7,000/month, and significantly more with higher support tiers or increased telemetry volume. This unpredictability makes Elastic harder to budget for compared to flat-rate or usage-capped observability tools.

But platforms like CubeAPM, which use smart sampling, flat ingestion rates, and full MELT coverage with predictable pricing – 10TB * $0.15 = $1536/month. Even if you take data transfer cost into account (10TB * $0.01 = $102), the cost becomes $1,638/month. $Here, RUM and synthetics are included, plus prompt support of less than 1 min TAT. 

3. Tail Sampling Tradeoffs: High Overhead and Delayed Signal Prioritization

Elastic Observability supports tail-based sampling in its APM solution, where the complete trace is ingested first, and then a decision is made on whether to retain it. While this approach allows for high-fidelity trace selection with full context, it introduces several practical challenges:

• High Resource Overhead: Tail sampling requires all traces to be ingested and buffered before sampling decisions can be made. This inflates compute, memory, and storage usage—particularly in high-throughput environments with microservices and distributed workloads.
  
• Delayed Alerting and Detection: Because sampling decisions happen post-ingestion, real-time responsiveness for alerting, anomaly detection, or SLO breaches is reduced—slowing down incident triage.
  
• Lack of Adaptive Signal-Aware Filtering: Elastic’s sampling is not inherently adaptive. It doesn’t prioritize traces based on runtime anomalies like error spikes, high latency, or unusual traffic patterns before ingestion. This can result in valuable signals being dropped when limits are reached.
  
• Vendor-Locked Instrumentation and Policy Logic: Elastic APM’s sampling and trace logic are tied to proprietary agents and ingestion pipelines. This creates friction when teams want to migrate to OpenTelemetry-native stacks or need fine-grained control over sampling policies.

In contrast, context-aware smart sampling—like that used by CubeAPM—evaluates traces at the edge using runtime signals such as latency spikes, cold starts, or error presence to retain only high-value traces. This reduces ingestion volume by up to 80% while preserving critical observability signals for faster debugging and lower cost.

4. Lack of Comprehensive MELT Capabilities

Elastic Observability provides a robust platform for log and metric ingestion, thanks to its ELK Stack. It offers capabilities in APM, RUM, synthetic monitoring, and error tracking (via integration), some of which rely on external integrations or manual setup. However, the maturity, correlation, and seamlessness of these features often lag behind specialized observability platforms.

This limits Elastic’s effectiveness as a truly unified MELT solution. For modern DevOps and SRE teams focused on reducing MTTR, minimizing alert fatigue, and avoiding tool sprawl, Elastic’s modular architecture can demand significant setup and maintenance, often leading to more operational overhead than actionable insight.

4. Limited OpenTelemetry (OTEL) Support

According to Elastic’s own docs under Limitations, a number of OpenTelemetry features are either unsupported or not fully implemented:

• Partial OTLP Support: Elastic supports OTLP over gRPC (protobuf) but lacks support for JSON/HTTP encoding—limiting compatibility with certain OpenTelemetry exporters.
• Span Misclassification: Some span types may be misrepresented—for example, applications using messaging semantics might appear as generic transactions in Kibana visualizations.
• Missing Granular Features: Span-level breakdowns like “Time Spent” across services are not natively available in Elastic APM, limiting root-cause analysis.
• Ingestion Schema Constraints: The app_logs pipeline restricts dynamic field mapping, reducing the semantic fidelity of logs and making it harder to perform flexible field-based queries.
• Manual Remapping of OTel Attributes: Resource and span-level attributes must often be manually remapped, and unmapped fields fall under generic labels.*—hindering rich, contextual correlation between traces, metrics, and logs.

As OpenTelemetry becomes the standard for vendor-neutral instrumentation, Elastic’s limited and partially implemented support introduces friction for teams looking to unify telemetry across stacks and reduce vendor lock-in.

5. No On-Premise Option on Elastic Cloud

Elastic Cloud, Elastic’s managed SaaS offering, does not offer a true on-prem or bring-your-own-cloud (BYOC) deployment option. This is a critical blocker for companies operating in regulated industries like healthcare, fintech, or government sectors that need to:

• Ensure data residency within specific regions (e.g., India, EU).
• Avoid public egress costs associated with SaaS vendors.
• Maintain observability with air-gapped or isolated VPC environments.

Although self-managing the ELK stack is an option, it brings back the operational overhead and eliminates the benefits of Elastic Cloud altogether. There is no middle ground.

6. Inconsistent Customer Support and Documentation Gaps

Several users on G2 and Reddit note that Elastic’s support — especially for non-Enterprise customers — can be slow, unhelpful, or overly reliant on community forums. You get limited support with the standard tier, while higher tiers – Gold, Premium, and Enterprise cost an extra 5-15% of the total cost. 

Documentation, while extensive, is often outdated or focused on old versions. For example:

• Setting up alerting with threshold logic in Kibana often lacks updated guides.
• Dashboards for APM data don’t auto-generate based on OTEL standards.

Criteria for Suggesting Elastic Observability Alternatives

To shortlist meaningful Elastic Observability alternatives, we used the following criteria — each focused on addressing the specific challenges teams face with Elastic:

1. Ease of Setup and Operational Simplicity

The alternative should offer a low-effort onboarding experience with intuitive configuration, minimal infrastructure management, and fast time to value. Ideally, teams should be able to go from zero to full observability in a matter of hours, not weeks.

2. Comprehensive MELT Coverage (Metrics, Events, Logs, Traces)

A true observability platform must support all pillars of telemetry — including Real User Monitoring (RUM), synthetic monitoring, and error tracking — in one unified experience, to minimize tool sprawl and enable full-stack visibility.

3. Native OpenTelemetry Support

The platform should be built with OpenTelemetry as a first-class citizen — not just as an ingest source — offering native dashboards, schema compatibility, and seamless correlation of traces, logs, and metrics. This ensures vendor neutrality and future-proof instrumentation.

4. Predictable, Cost-Efficient Pricing at Scale

The tool must scale without cost shocks. Features like smart sampling, tiered retention, or usage-based pricing models help ensure large telemetry volumes (10–50 TB/month) don’t lead to unmanageable bills. Cost transparency is critical.

5. Deployment Flexibility (SaaS + Self-Hosted Options)

Whether it’s data residency requirements, compliance mandates, or cost control, the best platforms offer the flexibility to deploy in the cloud, self-host in your own VPC, or operate in hybrid models, giving teams control over where data lives.

6. High-Quality Customer Support

Fast and effective support (via Slack, live chat, or email) is essential, especially during incidents. The best alternatives provide responsive, engineer-led support channels — not just community forums — with SLAs that teams can rely on.

7. Cross-Telemetry Correlation and Unified UI

A strong observability platform offers one pane of glass to correlate metrics, logs, and traces with context preserved across services and time windows. This dramatically reduces mean time to resolution (MTTR) and supports proactive debugging.

8. Integration Ecosystem and Migration Compatibility

Support for existing tools like Prometheus exporters, FluentBit, Beats, or Elastic agents reduces switching costs. Tools that offer drop-in compatibility with existing Elastic or OTEL pipelines make migration smoother and less disruptive.

Elastic Observability Overview

Known For

Elastic Observability is known for its powerful log analytics and centralized monitoring capabilities, built on top of the popular ELK Stack (Elasticsearch, Logstash, Kibana). It’s widely used by teams managing large volumes of machine data, especially logs and metrics, across distributed systems.

Standout Features

• Sampling: Elastic Observability offers tail-based sampling that allows you to make better sampling decisions after all trace spans are complete. This offers powerful and informed sampling rules. 
• Kibana Dashboards: Rich visualizations for logs, metrics, and traces with custom-built charts and drilldowns.
• Elasticsearch Query Language (KQL): Enables complex, full-text search and filtering across massive telemetry datasets.
• Elastic Common Schema (ECS): A flexible schema for normalizing data across sources.
• Index Lifecycle Management (ILM): Controls for tiering data into hot/warm/cold phases based on retention and cost.

Key Features

• Centralized Log Management: Efficient ingestion, indexing, and querying of logs from various sources.
• Metrics Monitoring: Collects and monitors time-series metrics for infrastructure, services, and containers.
• Elastic APM: Distributed tracing support for backend services with service maps and latency breakdowns.
• Uptime Monitoring: Basic synthetic checks to monitor service availability.
• Basic RUM Support: JavaScript agent support for browser monitoring (limited depth).
• Alerting and Anomaly Detection: Threshold-based and ML-driven alerting on metrics and logs.

Pros

• Highly scalable architecture, suitable for ingesting massive volumes of logs and metrics.
• Core components of the ELK stack are open source and widely adopted.
• Powerful Search & Query Capabilities enable lightning-fast queries, even on large datasets.
• Kibana for custom dashboards and visual tools for in-depth monitoring.

Cons

• Steep learning curve; requires strong familiarity with Elasticsearch, KQL, and Kibana to build effective observability workflows.
• Requires frequent maintenance and tuning (shards, indices, retention, storage tiers).
• Limited support for OpenTelemetry, requires manual setup, and lacks auto-instrumentation.
• Partial MELT coverage; lacks strong support for RUM, synthetic monitoring, and unified error tracking.
• Resource-based pricing in Elastic Cloud can lead to unpredictable bills. 
• No on-premise support in Elastic Cloud

Best For

Large enterprises with deep Elasticsearch expertise or teams prioritizing log analytics and centralized logging over full-stack observability. Suitable for organizations already invested in the Elastic ecosystem that can dedicate engineers to manage and tune it.

Pricing & Customer Reviews

• Pricing: Elastic Cloud does not offer fixed pricing by telemetry volume. Instead, it charges based on the underlying infrastructure: compute units, storage, ingestion rate, and retention. 
• G2 Rating: 4.2/5 
• Praised for: Strong logging and analytics, powerful dashboards, and rich query engine.
• Criticized for: Complexity of use, pricing unpredictability, and limited support for modern observability needs.

Top 7 Elastic Observability Alternatives

1. CubeAPM

Known For

CubeAPM is a modern, OpenTelemetry-native observability platform designed to give DevOps, SRE, and platform teams full-stack visibility, without the lock-in, steep learning curves, or unpredictable pricing common with legacy tools like Elastic Observability.

Whether deployed in the cloud or on-prem, CubeAPM offers deep insights across infrastructure, logs, traces, real user sessions, and synthetic tests—all in one unified interface. Built with compliance in mind, CubeAPM is especially favored by teams working in regulated industries or operating under data localization mandates like GDPR or India’s DPDP Act.

Standout Features

• Smart Sampling Engine: Unlike Elastic, which captures every trace unless manually downsampled, CubeAPM uses contextual smart sampling to automatically prioritize traces based on errors, latency spikes, and anomaly patterns—cutting data volume by up to 80% while preserving signal quality.
• Privacy-First, Self-Hosted Option: CubeAPM allows teams to run entirely within their infrastructure—no telemetry leaves your network. This enables compliance with strict data laws and avoids vendor-controlled storage fees or cloud egress costs Elastic Cloud often incurs.
• Agent Compatibility for Fast Migration: CubeAPM supports ingestion from Elastic Beats, Fluent Bit, OpenTelemetry, and Prometheus exporters—enabling drop-in migration without re-instrumenting your services. Most teams can migrate within an hour using existing pipelines.
• Slack-Native Support from Engineers: Rather than routing you through ticket portals or forums, CubeAPM offers instant, Slack- or WhatsApp-based support from the core engineering team—ideal for fast-moving incident response teams who can’t wait days for help.

Key Features

• Full MELT Stack Observability: Unified view across Metrics, Events, Logs, and Traces, along with built-in Real User Monitoring (RUM), Synthetics, and Error Tracking—eliminating the need to bolt together multiple tools.
• Native OpenTelemetry & Prometheus Ingestion: Accepts telemetry natively in OTEL and Prometheus formats, with automatic correlation across traces, metrics, and logs—no translation layers or custom mapping required.
• Ingestion-based Pricing: CubeAPM uses transparent pricing—$0.15/GB of data ingested and $0.01/GB for data transfer—making forecasting and budgeting easier than Elastic’s resource-based pricing tied to indexing performance tiers.
• Synthetic Monitoring and RUM Included: Built-in tools allow teams to simulate user journeys and monitor frontend performance in real time, unlike Elastic, where RUM is basic and synthetics must be handled externally.
• Prebuilt Dashboards and No-Code Alerts: Comes with pre-configured dashboards for Kubernetes, PostgreSQL, Redis, and web services, along with simple, no-code threshold-based alerts to Slack or Webhooks.
• Kubernetes-Native Infra Monitoring: Deep insights into pods, nodes, namespaces, and container health with zero manual configuration—streamlining SRE workflows.
• Security and Compliance Controls: Role-based access control (RBAC), audit logs, MFA, SSO, and full support for compliance frameworks including SOC 2, HIPAA, and GDPR.
• Flexible Deployment: SaaS, hybrid, or fully on-prem hosting options allow for low-latency access and full sovereignty over your telemetry data.

Pros

• Smart sampling reduces volume while improving signal-to-noise ratio
• Predictable and transparent pricing at all volumes
• Supports full MELT + synthetics + RUM in one product
• Easy agent migration from Elastic, Prometheus, and OTEL
• No cloud egress costs if self-hosted
• Direct Slack support from core engineers—resolves issues in minutes
• Flexible hosting (SaaS or private cloud) supports compliance and low-latency needs

Cons

• Not suited for teams looking for an off-prem solution
• Strictly an observability platform, and does not support cloud security management

Best For

CubeAPM is ideal for mid-sized to large DevOps and platform teams migrating from ELK/Elastic Cloud. It’s also suitable for fintech, healthcare, or government organizations with strict compliance needs. Startups scaling telemetry ingestion but constrained by Elastic’s rising cost and limited OpenTelemetry coverage can use CubeAPM. Plus, it’s great for teams using Kubernetes and microservices and are looking for unified visibility without maintaining separate stacks for metrics, logs, and traces

Pricing & Customer Reviews

• Pricing: $0.15/GB of data ingested; error tracking & synthetics are included at no additional cost
• Score: 4.7/5 based on private feedback and mid-market deployments
• Praised for: Smart sampling, fast support, clear pricing, full OTEL support

CubeAPM vs Elastic Observability

Elastic Observability is powerful for search-heavy log use cases, but it lacks cohesive MELT coverage, native OpenTelemetry handling, and cost predictability. It also requires constant engineering effort to tune shards, manage index storage, and deal with pricing spikes based on backend infrastructure usage.

CubeAPM solves these challenges with its OpenTelemetry-first model, built-in support for RUM, synthetics, and smart sampling, along with full data control via self-hosting. By offering up to 80% cost savings, zero egress fees, and much faster support, CubeAPM emerges as a modern, transparent, and compliant alternative for engineering teams who have outgrown Elastic’s complexity and unpredictability.

2. Datadog

Known For

Datadog is a widely adopted SaaS-based observability platform that combines infrastructure monitoring, APM, log analytics, RUM, synthetics, and security tooling in a unified interface. Especially popular in cloud-native environments, it offers extensive out-of-the-box integrations and dashboarding flexibility. Datadog is often the first choice for teams running large Kubernetes clusters or managing dynamic microservices environments across AWS, Azure, or GCP.

Standout Features

• Massive Integration Ecosystem: Datadog supports over 900+ integrations across cloud providers, CI/CD tools, container orchestration platforms, databases, and messaging queues. This makes it incredibly easy to collect telemetry from virtually any source without custom exporters or additional agents.
• Unified Dashboards and Collaborative Notebooks: Datadog’s dashboards allow real-time correlation of logs, metrics, and traces in drag-and-drop interfaces. The Notebooks feature enables teams to compile and share root-cause investigations by combining graphs, traces, logs, and annotations in a single collaborative document.
• DevSecOps in One Platform: Datadog merges observability and security with features like Cloud Security Posture Management (CSPM), workload protection, runtime threat detection, and audit trail monitoring—positioning itself as an all-in-one DevSecOps solution.

Key Features

• All-in-One MELT Coverage: Datadog offers deep observability into Metrics, Events, Logs, and Traces (MELT) with additional support for RUM and synthetics. This allows engineering teams to monitor every layer from infrastructure to frontend UX.
• Security Monitoring: Modules like CSPM, Cloud Workload Security (CWS), and runtime threat detection let DevOps and SecOps work from the same interface, improving collaboration between teams responsible for uptime and risk.
• Frontend Visibility with RUM & Synthetics: Supports real user session replays, performance monitoring, and synthetic testing for APIs and web apps. These features are built into the platform, reducing reliance on third-party tools.
• Auto-Instrumentation Across Languages: Built-in support for Java, Python, Go, Node.js, .NET, Ruby, and PHP. Offers auto-instrumentation and distributed tracing with little to no code changes in many environments.
• Cloud-Native Awareness: Deep, native integrations with AWS Lambda, ECS, Fargate, Azure Functions, and GCP workloads ensure real-time monitoring of modern serverless and container-based applications.
• CI/CD and Deployment Tracking: Built-in pipeline visibility helps track deployments and correlate them with system behavior, latency changes, or error spikes.

Pros

• Seamless integration with major cloud platforms and 900+ third-party services
• Combines observability with security monitoring in a single tool
• Excellent visualization and dashboard UX for live data exploration
• Advanced support for Kubernetes, microservices, and serverless
• Features like Notebooks and monitors simplify collaboration and alerting

Cons

• Many users report monthly bills escalating quickly beyond expectations.
• Lack of self-hosting is not suitable for organizations operating in heavily regulated industries to meet compliance needs.
• Proprietary agents and formats make migrating to OpenTelemetry or other tools complex and time-consuming.
• Datadog uses probabilistic sampling, which can miss rare but important traces unless manually tuned. This is a problem, especially in high-latency environments.
• Many users complain of delays and a lack of context in support interactions; enterprise SLAs are available, but smaller teams may struggle to get timely help.

Best For

Datadog is best for cloud-native DevOps and platform teams using AWS/GCP/Azure who want tight integration and real-time observability out of the box. It’s also ideal for enterprises seeking a combined DevSecOps platform with both telemetry and security analytics, and Kubernetes-heavy teams looking for scalable dashboards and rich performance insights

Pricing & Customer Reviews

• Infrastructure Monitoring: $15–$34/host/month
• APM (traces): $31–$40/host/month (annual), or $36/month on-demand
• Log Ingestion: $0.10/GB + $1.70/M events (with ~15 days retention)
• RUM & Synthetics: Charged separately per session or check volume
• Serverless Monitoring: $10 per million invocations
• Security Monitoring: $15–$40/user/month
• G2 Rating: 4.4/5 (630+ reviews)
• Praised for: Rich integrations, real-time dashboards, and platform completeness
• Criticized for: Complex billing, lack of self-hosted option, and costly scale-ups

Datadog vs Elastic Observability

Both Datadog and Elastic offer broad observability capabilities, but their architecture and cost models differ significantly. Elastic emphasizes flexibility, search, and open-source extensibility—but struggles with MELT integration, OpenTelemetry maturity, and self-managed complexity. Datadog, on the other hand, provides a cohesive SaaS platform that’s easier to onboard but comes with high cost unpredictability and limited deployment flexibility.

While Datadog is more feature-complete and better suited for dynamic cloud-native teams, Elastic can be more cost-effective for log-heavy setups, provided the organization can manage its operational complexity. Still, neither tool addresses the growing demand for OpenTelemetry-native, self-hosted, and cost-transparent observability, which is where tools like CubeAPM offer a superior balance.

3. Dynatrace

Known For

Dynatrace is an enterprise-grade observability platform known for its automated root cause analysis, intelligent dependency mapping, and embedded application security. Purpose-built for complex IT environments, it’s a favorite among large organizations running mission-critical applications across hybrid, multicloud, and on-prem infrastructures. With its tightly integrated AI engine and automation-first approach, Dynatrace helps platform and SRE teams reduce operational overhead while delivering real-time insights across the entire software stack.

Standout Features

• AI-Driven Root Cause Analysis with Davis®: Dynatrace’s built-in Davis AI engine processes millions of telemetry signals in real time, correlating them across infrastructure, services, and applications to identify precise root causes. This eliminates alert fatigue and guesswork, and drastically reduces mean time to resolution (MTTR).
• Topology Mapping with Smartscape: Instead of isolated dashboards, Dynatrace auto-generates a living topology map of your system, including services, APIs, databases, containers, and VMs. This visual map evolves in real time and is used by Davis AI to understand causality and detect anomalies.
• Runtime Application Protection (RASP): Beyond observability, Dynatrace introduces security into the observability workflow with runtime threat detection and behavioral analysis, allowing teams to block exploits in production without additional agents.
• Full Digital Experience Monitoring (DEM): Combines synthetic tests and RUM to monitor frontend performance and user behavior. The data can be correlated back to the backend infrastructure and traces for holistic troubleshooting.

Key Features

• End-to-End MELT Observability: Covers metrics, logs, traces, user sessions, and events within a single interface—removing silos and enabling unified analysis across your stack.
• Auto-Instrumentation & Language Support: Automatically instruments environments like Kubernetes, serverless, and VMs, with support for Java, .NET, Node.js, PHP, Python, and more—saving setup time.
• Code-Level Transaction Tracing: Dynatrace traces requests down to specific functions and methods, providing developers with granular performance insights for debugging bottlenecks.
• Real-Time Threat Detection: Uses behavior analytics to monitor for code vulnerabilities, third-party exploits, or anomalies in live traffic—all surfaced in the observability interface.
• Intelligent Alert Correlation: Reduces noise by aggregating related incidents and prioritizing alerts that represent the root cause. This correlation is informed by system topology and historical baselines.
• Native Cloud & Container Support: Offers native integrations for AWS, Azure, GCP, Kubernetes, OpenShift, and more, with resource-level tagging and context propagation.

Pros

• Embedded AI for proactive root cause identification and anomaly detection
• Auto-discovery and mapping of system architecture through Smartscape
• Combining observability with real-time application security in one platform
• Eliminates manual alert tuning with AI-powered correlation
• Full-stack visibility with minimal manual instrumentation

Cons

• DDU-based pricing is difficult to estimate and predict; usage-based charges can grow quickly without clear visibility.
• Does not natively align with OTEL data structures, making migration difficult.
• Full functionality still depends on Dynatrace-managed services, making it challenging for compliance-heavy environments.
• The AI-first approach, while powerful, can be complex for new users unfamiliar with Smartscape or Dynatrace’s data model.

Best For

Elastic Observability is ideal for large enterprises managing sprawling infrastructure across cloud and on-prem, or organizations with strict uptime SLAs that require real-time root cause detection.

Pricing & Customer Reviews

Dynatrace pricing is based on Davis Data Units (DDUs)—a usage-based credit system. 

• Full-Stack Monitoring: $0.08 per 8 GiB host 
• Infrastructure Monitoring: $0.04/hour per host.
• Container (K8s) Monitoring: $0.002/hour per pod.
• Application Security (RASP): $0.018/hour per 8 GiB host.
• Real User Monitoring (RUM): $0.00225 per session.
• Synthetic Monitoring: $0.001 per HTTP check or plugin test.
• G2 Rating: 4.5/5 (1,300+ reviews)
• Praised for: AI automation, smart dependency mapping, depth of observability
• Criticized for: Pricing opacity, limited OTEL alignment, and steep onboarding curve

Dynatrace vs Elastic Observability

While Elastic gives teams flexibility in building their own observability pipelines using open-source tools, it demands high operational effort and lacks full MELT integration and automation. Dynatrace takes the opposite approach—it offers a closed but powerful ecosystem driven by AI, with built-in RUM, security, and topology intelligence.

Elastic’s strengths lie in search and log analytics, especially for log-heavy use cases, but its tooling for tracing, user monitoring, and proactive detection is underdeveloped. Dynatrace, meanwhile, shines in automation, visualization, and operational intelligence—but comes with a premium price tag and proprietary architecture.

For large, complex systems where speed, automation, and incident prevention matter more than open-source flexibility, Dynatrace is a superior—but costlier option.

4. New Relic

Known For

New Relic is a cloud-based observability platform built for developers and SREs who want fine-grained control over telemetry and real-time visibility across every layer of their stack. Known for its powerful query interface and customizable dashboards, New Relic brings together APM, infrastructure monitoring, logs, traces, synthetics, and real user insights into a unified experience. It’s popular with teams that value flexible telemetry analysis and deep integration with modern CI/CD and cloud-native environments.

Standout Features

• NRQL: Developer-Centric Query Language: New Relic’s proprietary query language, NRQL (New Relic Query Language), allows users to perform advanced analytics across logs, metrics, traces, and custom events—enabling real-time dashboards, SLO tracking, and anomaly detection from a single DSL interface.
• Explorer View for Entity-Centric Debugging: The platform auto-generates a visual map of monitored entities—hosts, services, containers, and databases—allowing teams to understand interdependencies and system health at a glance. Explorer View helps streamline investigations in complex, distributed environments.
• Lookout Anomaly Detection: Using statistical baselines and ML models, New Relic’s Lookout feature automatically surfaces performance anomalies, regressions, or traffic spikes. It reduces manual alert tuning and helps prioritize issues across noisy telemetry.
• Custom Dashboarding & Visualization: Teams can build tailored dashboards using prebuilt widgets, NRQL queries, and advanced layout controls, making it easier to create role-specific views for SREs, developers, or product teams.

Key Features

• Full-Stack MELT Observability: New Relic unifies metrics, events, logs, and traces into one interface, offering comprehensive coverage from backend services to user interactions.
• Language Agent Support: Robust instrumentation for Java, .NET, Python, Go, Node.js, PHP, and Ruby, with support for profiling, distributed tracing, and exception tracking across all major frameworks.
• RUM and Synthetic Monitoring: Provides browser-based real user monitoring (RUM) alongside synthetic checks for uptime, API health, and site performance—all fully integrated with backend observability.
• Anomaly Detection and Alerting: Includes dynamic threshold detection, multi-condition alert policies, and third-party integrations (Slack, PagerDuty, Jira) for automated incident response.
• Cloud & Kubernetes Support: Deep integrations with AWS, GCP, Azure, and Kubernetes environments, including support for serverless functions, containers, and workload-specific telemetry.
• Partial OpenTelemetry & Prometheus Support: New Relic supports OpenTelemetry and Prometheus data ingestion, but not natively—teams often manage dual agents or face ingestion overhead to maintain compatibility.

Pros

• Advanced telemetry exploration using NRQL
• Real-time, customizable dashboards and prebuilt templates
• Strong integration with cloud-native environments and CI/CD pipelines
• ML-powered anomaly detection reduces manual alerting noise
• Explorer view simplifies dependency mapping and infrastructure insights
• Fast to deploy for SaaS-first organizations

Cons

• No on-prem or BYOC options, limiting use in data-sensitive or regulated environments
• Pricing includes ingest fees, user licenses, and retention charges, making it difficult to forecast at scale
• Head-based sampling can miss critical traces under high load unless explicitly tuned
• Limited native OTEL Support, requires dual-agent setups or proxy layers to fully ingest OTEL data without losing context
• Primarily ticket-based, which can delay time-sensitive troubleshooting for smaller teams

Best For

New Relic is ideal for engineering and DevOps teams who want real-time, self-serve observability with flexible dashboards and deep query power. It’s a great fit for teams comfortable working with NRQL and managing telemetry pipelines via SaaS. However, it’s less suited for compliance-heavy organizations or those looking for cost-stable observability at high data volumes.

Pricing & Customer Reviews

• Free Tier: Includes 100 GB/month ingest and 1 core user
• Ingest Charges: $0.35–$0.55/GB, depending on data retention
• Core Users: $0 to $49/user/month
• Full Platform Users: $99 to $418/user/month for advanced features
• Extra Charges: Synthetic checks, long-term retention, and external integrations incur additional fees
• G2 Rating: 4.4/5 (500+ reviews)
• Praised for: Rich dashboard customization, NRQL flexibility, and wide cloud integration
• Criticized for: Unpredictable billing, limited OTEL alignment, and lack of on-prem hosting

New Relic vs Elastic Observability

Both New Relic and Elastic offer robust observability capabilities, but they cater to different teams. Elastic focuses on search-heavy use cases with open-source flexibility but struggles with ease of use, MELT integration, and hosted UX. New Relic offers a polished SaaS experience with built-in anomaly detection, real-time dashboards, and powerful querying via NRQL—but lacks deployment flexibility and incurs high costs due to its combined ingest, retention, and user licensing model.

Teams looking for developer-first control and real-time telemetry analytics may find New Relic a more modern option than Elastic, but at the cost of full OpenTelemetry support and data control. For those prioritizing predictability and compliance, it still falls short of tools like CubeAPM.

5. Coralogix

Known For

Coralogix is a log-centric observability solution built to help teams tame large-scale telemetry through flexible pipeline control and cost-efficient indexing. By emphasizing dynamic routing, real-time stream processing, and customer-owned archives, Coralogix empowers DevOps and security teams to tailor exactly which logs get indexed versus stored, minimizing waste and maximizing visibility.

Standout Features

• Dynamic Log Classification & Routing: Users can define rules that decide, on a per-log basis, whether data is fully indexed, sent to a low-cost archive, or dropped entirely, ensuring that only high-value events incur indexing fees.
• Streama™ Ingestion Engine: Coralogix processes logs and metrics during ingestion rather than post-indexing, enabling alerts and dashboards to update instantly, cutting alert latency to near-zero.
• Customer-Managed Archival: All telemetry can be offloaded to the customer’s own S3 or GCS buckets at no additional platform cost. This method shifts long-term storage fees to the customer’s cloud provider and preserves a complete audit trail.
• GitOps-Driven Configuration: Dashboards, alert rules, and pipeline logic live in version-controlled repositories. Teams can apply CI/CD best practices to observability configuration, improving auditability and collaboration.

Key Features

• Log-First Dashboards & Alerts: Purpose-built for log analytics, Coralogix’s UI centers on log search, pattern-based alerts, and live tail, with supplementary metric and trace overlays.
• ML-Based Anomaly Detection: Coralogix applies machine learning to detect unusual spikes, volume shifts, or value anomalies in log streams automatically, without manual threshold setup.
• Ingest-Time Alerting: Alerts trigger as soon as data arrives, before it is written to long-term storage, ensuring critical incidents are surfaced without delay.
• Cloud-Agnostic Deployment: Available as a fully managed SaaS or deployed within the customer’s VPC. The latter supports private networking and tighter compliance, though it requires extra operational setup.
• SIEM & Data Export Compatibility: Telemetry can be forwarded from Coralogix to SIEM platforms (e.g., Splunk, QRadar) or data lakes (Snowflake, S3) for advanced security analytics or archival compliance.

Pros

• Fine-grained routing rules drastically reduce index costs for low-value logs.
• Streama™ ensures dashboards and alerts reflect incoming data in real time.
• Long-term data resides in your AWS/GCP buckets, shifting storage costs away from Coralogix.
• Version-controlled pipelines improve collaboration and change tracking.

Cons

• Since data flows through Coralogix before archival, customers incur cloud egress charges.
• Temporary routing through Coralogix’s infrastructure can conflict with strict localization mandates.
• Tracing and time-series metrics support lag behind full-stack observability leaders.
• Self-hosted mode requires substantial networking and operational work.

Best For

Coralogix is suitable for organizations that generate massive log volumes and need precise control over indexing costs, especially when long-term archives can live in customer-owned buckets. It’s ideal for security, compliance, and SRE teams who prioritize real-time alerts and pipeline versioning, provided temporary data egress is acceptable.

Pricing & Customer Reviews

• Logs: ~$0.52/GB
• Traces: ~$0.44/GB
• Metrics: ~$0.05/GB
• AI/Anomaly Detection: $1.50 per 1M tokens
• Archival: Free if stored in customer-managed S3 or GCS
• G2 Rating: 4.6/5 (300+ reviews)
• Praised for: Flexibility in log routing, low alert latency, and cost control
• Criticized for: Hidden egress charges, incomplete MELT coverage, and weak data localization options

Coralogix vs Elastic Observability

Elastic and Coralogix both specialize in log analytics, but their architectures diverge significantly. Elastic relies on index-first ingestion and requires manual storage tuning, while Coralogix pushes observability left—processing data before storage and enabling smarter routing at the pipeline level.

Coralogix is better suited for teams who want real-time alerts, fine-grained control over storage costs, and Git-based observability governance. However, unlike Elastic’s self-managed flexibility, Coralogix routes all data through its infrastructure, making it less favorable for regulated industries or teams with strict data sovereignty needs. For cost-conscious, log-heavy teams, Coralogix is a powerful Elastic alternative—especially when full-stack APM isn’t a top priority.

6. Sumo Logic

Known For

Sumo Logic is a cloud-native observability and security platform designed for enterprises managing large volumes of log and event data across distributed systems. With a heritage in log analytics and SIEM, it combines machine data intelligence with infrastructure monitoring, APM, and security analytics—all accessible via a unified SaaS interface. Sumo Logic is particularly favored by teams in security-conscious sectors looking to unify operational and threat visibility under one roof.

Standout Features

• Cloud-Native Architecture with Multi-Tenant Scaling: Sumo Logic’s architecture is fully SaaS and built to scale across high-ingest environments, supporting real-time telemetry ingestion, processing, and retention for logs, metrics, and events without requiring on-prem infrastructure or manual tuning.
• Integrated SIEM & Security Analytics: In addition to observability, Sumo Logic offers built-in security modules like threat detection, compliance dashboards, and audit trail analytics. It’s one of the few platforms combining DevOps observability with modern SIEM features for full-stack DevSecOps.
• PowerQuery & LogReduce™: Sumo’s proprietary analytics engines—PowerQuery and LogReduce—help identify patterns in log data, surface anomalies, and reduce noise. This enhances root cause investigation and helps streamline alerting workflows, especially in log-heavy environments.
• Out-of-the-Box Dashboards & Sources: With hundreds of prebuilt apps and integrations, teams can monitor services like AWS, Kubernetes, NGINX, and MySQL with minimal setup. These packages come with predefined queries, dashboards, and alerts tailored for each source.

Key Features

• Unified MELT Observability: Combines monitoring for Metrics, Events, Logs, and Traces, with pre-integrated RUM, synthetics, and infrastructure monitoring tools, though some capabilities are limited compared to specialized APM vendors.
• Multi-Cloud & Kubernetes Support: Native integrations for AWS, Azure, GCP, and Kubernetes clusters allow real-time tracking of cloud workloads, container lifecycles, and scaling behavior.
• Security & Compliance Modules: Offers threat detection and alerting across infrastructure, with built-in support for PCI, HIPAA, and SOC 2 compliance dashboards—blending observability with security operations.
• LogReduce™ and Outlier Detection: Uses unsupervised ML to detect unusual log behavior or rare event patterns across massive data streams, helping reduce alert fatigue and improve detection.
• Flexible Data Sources: Supports ingestion from agents like FluentD, OpenTelemetry exporters, and AWS/GCP native telemetry streams, but lacks deep OpenTelemetry-native correlation across all layers.

Pros

• All-in-one observability + security analytics in a single SaaS platform
• Scales well for enterprises handling massive log volumes
• Offers powerful log analysis tools like LogReduce™ and PowerQuery
• Dozens of prebuilt dashboards accelerate time to value
• Built-in support for compliance and security standards

Cons

• Fully SaaS-based with no option for private cloud or self-managed deployments, which restricts data sovereignty.
• Pricing is based on daily ingest volume and query frequency—costs can rise unpredictably in high-cardinality environments.
• Ingests OTEL data but lacks native support for full trace-log-metric correlation workflows.
• APM and tracing features are not as mature or intuitive as competitors like CubeAPM or Dynatrace.

Best For

Sumo Logic is best suited for large enterprises with high log ingestion needs and overlapping observability and security requirements. It’s especially effective in environments where centralized DevSecOps visibility is critical and where SaaS-based deployment is preferred over infrastructure ownership. However, it may fall short for teams needing OpenTelemetry-native workflows, on-prem hosting, or precise control over ingestion and retention.

Pricing & Customer Reviews

Sumo Logic pricing is based on data ingest volume and scanning frequency:

• Free tier
• Paid: Pricing is based on data ingest and features enabled.
• Logs: $2.50/GB (standard ingest), metrics are available via add-ons, and traces are supported but priced as part of advanced observability plans
• G2 Rating: 4.3/5 (600+ reviews)
• Praised for: Log analytics strength, security integration, and scalability
• Criticized for: Pricing unpredictability, limited OpenTelemetry workflows, and sluggish UI in large dashboards

Sumo Logic vs Elastic Observability

Both platforms emphasize log analytics at their core, but Sumo Logic offers more out-of-the-box capabilities with bundled security tools and managed dashboards. Elastic gives teams more raw flexibility and control—especially with on-prem deployment—but requires significant tuning and internal expertise.

Sumo Logic, while easier to onboard, is limited by its SaaS-only nature, unpredictable billing, and lack of native OTEL correlation. Elastic appeals to teams wanting open-source customization, whereas Sumo Logic is more aligned with teams seeking managed, integrated observability and SIEM in one platform. For organizations seeking balance, but still needing full MELT correlation and OTEL-first architecture, CubeAPM offers a more future-ready and compliance-flexible alternative.

7. Better Stack

Known For

Better Stack is a streamlined observability platform that combines log management, uptime monitoring, incident response, and infrastructure tracking into a single developer-focused interface. With fast setup, a generous free tier, and Slack-first workflows, it’s become a popular choice among early-stage startups and agile engineering teams that want consolidated observability without the complexity of traditional APM tools. While its polished UI and bundled feature set make it appealing for small to mid-sized teams, it may lack the depth and customization that larger or compliance-sensitive organizations require.

Standout Features

• eBPF-Powered Auto-Instrumentation: Better Stack enables zero-code tracing and system metrics collection via eBPF-based OpenTelemetry collectors. This approach allows for fast setup and deep observability without needing manual instrumentation or language-specific agents.
• Slack-Integrated Incident Management: Incident response is tightly integrated with Slack, phone, and SMS alerts—complete with on-call scheduling, escalation policies, AI-based alert deduplication, and even postmortem tools. This removes the need for separate platforms like PagerDuty or Opsgenie.
• 30-Second Uptime Monitoring with Diagnostics: Teams can set up granular uptime checks (including SSL expiry, cron job health, and traceroute diagnostics) and publish branded status pages. These checks are combined with synthetic and heartbeat monitoring to ensure robust external visibility.
• ClickHouse-Powered Telemetry Exploration: Better Stack uses ClickHouse under the hood for high-speed querying across logs, metrics, and traces. A SQL-like interface enables quick investigation and dashboarding—even on high-volume datasets.

Key Features

• Unified Observability Stack: Logs, traces, metrics, uptime, status pages, and incident management are all consolidated into one dashboard, reducing the need for multiple tools.
• Install-Free Tracing & Monitoring: eBPF-based collectors remove friction during instrumentation, though teams operating in hardened environments may encounter kernel limitations.
• Advanced Alert Routing: Includes unlimited Slack, phone, and SMS notifications with AI-based noise suppression to streamline response workflows.
• Configurable Uptime Checks: Includes real-world browser tests using Playwright, heartbeats for background jobs, and built-in support for SSL and domain expiry monitoring.
• Wide Integration Ecosystem: Supports integrations with tools like Prometheus, Datadog, Grafana, Zabbix, New Relic, AWS, Kubernetes, and more, offering easy connectivity for hybrid stacks.

Pros

• Extremely easy to set up—ideal for teams without dedicated observability engineers
• Free tier includes generous limits across uptime, logs, metrics, and alerting
• Eliminates the need for separate tools like Statuspage.io, Pingdom, or PagerDuty
• Clean, modern UI with fast dashboards and simplified workflows
• ClickHouse backend delivers fast queries on large datasets

Cons

• No support for self-hosting or BYOC deployments
• Limited scalability for high-ingestion or high-cardinality telemetry
• Alerting lacks multi-dimensional logic and advanced thresholding
• No native OpenTelemetry or smart sampling support
• Heavy reliance on eBPF, which can be incompatible with certain kernel versions or security policies

Best For

Better Stack is best suited for fast-growing SaaS companies and tech teams that want an all-in-one observability solution without assembling and managing multiple tools. It’s ideal for DevOps teams that value a generous free tier, fast deployment, and tight Slack integration for incident response. 

Pricing & Customer Reviews

• Free Plan: Includes 10 uptime monitors, 1 static status page, 3 GB logs (3-day retention), 2 billion metrics (30-day), and alerting via Slack, phone, or SMS
• Paid Plans: Start at $29/user/month with included incident response and extended telemetry limits. Additional telemetry and responder bundles available à la carte.
• G2 Rating: 4.8/5 (292 reviews)
• Praised for: Fast onboarding, intuitive interface, generous free features, and built-in alerting
• Criticized for: Lack of self-hosting, weak OTEL support, no sampling features, and shallow analytics for large-scale observability

Better Stack vs Elastic Observability

While Elastic provides powerful log indexing and search flexibility, it demands significant setup and operational overhead. Better Stack, by contrast, excels in simplicity and developer-friendliness. Its integrated uptime monitoring, incident response, and logs/traces dashboard make it a compelling out-of-the-box tool for fast-moving teams.

However, Elastic allows for far more customization, on-prem deployment, and advanced search logic, making it better suited for large enterprises or compliance-driven teams. Better Stack’s lack of OpenTelemetry-native support, self-hosting, and advanced alert logic limits its appeal beyond the mid-market. 

Conclusion: Choosing the Right Elastic Observability Alternative

As the demands of modern observability continue to grow, engineering teams are finding that Elastic Observability is falling short in key areas, despite its powerful search and log analytics. From high operational overhead and complex index tuning to unpredictable pricing and limited native OpenTelemetry support, Elastic introduces friction where agility is needed most. For teams working in regulated industries or managing large-scale telemetry, Elastic’s SaaS-only limitations, lack of unified MELT correlation, and resource-based pricing create real challenges.

CubeAPM directly addresses these pain points. With its OpenTelemetry-native architecture, smart sampling engine, and full MELT support (including logs, metrics, traces, RUM, synthetics, and error tracking), CubeAPM offers a complete observability experience—without the complexity, lock-in, or unpredictable billing. Unlike Elastic, CubeAPM provides self-hosted deployment options, ensuring data sovereignty and compliance with regional regulations like GDPR. And with transparent pricing (starting at $0.15/GB) and Slack-based support from engineers, teams can move faster while saving 60–80% on observability costs.

If you’re struggling with Elastic’s limitations—or simply want a more modern, cost-efficient, and compliant path forward—CubeAPM offers the visibility you need with none of the friction.

Book a free demo today and see why teams are switching to CubeAPM for scalable, future-ready observability.