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.

However, Elastic Observability can be complex to set up, requires significant expertise, and has a steep learning curve. Its pricing can also be a little confusing with different costs for cloud-managed, serverless, and self-managed. On top of that, their support package is tiered, ranging from standard (included) to premium (5-15% of the total bill). Plus, retention and egress charges are calculated extra. All these add to the overall pricing.

Fortunately, CubeAPM, as the best Elastic Observability alternative, is more cost-efficient with pricing $0.15/GB of ingested data. You won’t be charged anything extra for data retention, egress, support, RUM, or synthetics. CubeAPM also employs smart sampling to save up to 50% of the costs, and is fully self-hosted to ensure data residency and compliance.

In this article, we’re going to explore the top 7 Elastic Observability alternatives based on cost, deployment flexibility, online reviews, support quality, and more.

Top 7 Elastic Observability Alternatives in 2025

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

Comparison Table

Tool	* Pricing (Small, Mid, Large Teams)	OTEL Native	Support TAT	Self-hosting
CubeAPM	Small: $2,080; Mid: $7,200; Large: $15,200	Yes	Within minutes	Yes
Elastic Observability	Small: $4,550; Mid: $17,435; Large: $35,370	Yes	30 min to 3 days	Yes
Datadog	Small: $8,185; Mid: $27,475; Large: $59,050	Yes	<2-48 hrs	No
Dynatrace	Small: $7,740; Mid: $21,850; Large: $46,000	Yes	4 hrs to days	Yes
New Relic	Small: $7,896; Mid: $25,990; Large: $57,970	Yes	1 hr to 2 days	No
Coralogix	Small: $4,090; Mid: $13,200; Large: $29,000	Yes	Within minutes	No
Sumo Logic	Small: $4,641 Mid: $16,065; Large: $33,915	Yes	<1 hr to 2 days	No
Better Stack	Small: $5,723 Mid: $20,550; Large: $43,350	Yes	<4 hrs to 2 days	No

*All pricing comparisons are calculated using standardized Small/Medium/Large team profiles defined in our internal benchmarking sheet, based on fixed log, metrics, trace, and retention assumptions. Actual pricing may vary by usage, region, and plan structure. Please confirm current pricing with each vendor.

Why Look for Elastic Observability Alternatives?

Escalating Costs at Scale

Elastic Observability (via Elastic Cloud) offers fully managed and serverless options whose costs scale with heavier ingest volume, retention, and features. There is a self-managed (self-hosted) model where you can run Elasticsearch, Kibana and related components on your own infrastructure and pay only for the license/subscription plus infrastructure (hardware/VMs/ storage/network), rather than per-GB ingestion or usage-based billing.

This self-managed route can reduce the premium costs associated with cloud/managed offerings, but you are responsible for infrastructure maintenance, upgrades, backups, scaling, and operations overhead, which may significantly raise your total cost of ownership (TCO) in staff time and operational complexity.

Limited Self-Hosting

Self-hosting option is available as a separate service, not included in Elastic Cloud, Elastic’s managed SaaS offering. This is a concern for companies using Elastic Cloud and operating in regulated industries that need to ensure data residency within a specific region or avoid additional costs associated with cloud vendors.

Costly Customer Support Tier

You get limited support with Elastic Cloud’s Standard tier, while higher tiers – Gold, Platinum, and Enterprise cost an extra 5-15% of the total billing. Also, the response greatly varies from Standard to Enterprise tiers. The response time in the Standard tier is 3 business days, while the Enterprise tier’s response time is 30 minutes for urgent cases, 4 hours for high-priority cases, and 1 day for normal cases.

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.
• No on-premise deployment 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: Cloud-hosted starts from $99/month; serverless starts from $0.09/GB for full-stack observability (egress and retention extra); self-managed: custom pricing
• G2 Rating: 4.2/5 
• Praised for: Strong logging and analytics, powerful dashboards, and a rich query engine.
• Criticized for: Pricing complexity, limited self-hosting

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; 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 complnace with SOC 2 and ISO 27001.
• Flexible Deployment: 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 its cost can be confusing with different tiers. Its pricing also includes extra charges for egress and retention. The platform can introduce a learning curve and has a limited self-managed option.

CubeAPM solves these challenges with a full self-hosting option on the customer’s cloud or on-premise infrastructure. Its pricing is simple with zero egress fees. It doesn’t charge anything extra for support or data retention as well. CubeAPM emerges as a modern, transparent, and compliant alternative for engineering teams.

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

• Expensive for small teams
• No self-hosting can be an issue for organizations operating in heavily regulated industries

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 emphasises flexibility, search, and open-source extensibility, but adds extra cost for support, egress, and retention, and self-hosting. Datadog, on the other hand, provides a cohesive SaaS platform that’s easier to onboard but comes with high cost and SaaS-only deployment

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; usage-based charges can grow quickly with increasing data volumes
• Learning curve for smaller teams

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 offers limited self-hosting and is expensive. Dynatrace is a powerful ecosystem driven by AI, with built-in RUM, security, and topology intelligence. For large, complex systems where speed, automation, and incident prevention matter more, Dynatrace is a great 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 scale

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: $0.40/GB, depending on data retention
• Core Users: $0 to $49/user/month
• Full Platform Users: $349/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 and can be self-managed, but New Relic offers a polished SaaS experience with built-in anomaly detection, real-time dashboards, and powerful querying via NRQL—but lacks self-hosting.

5. Coralogix

Known For

Coralogix is a full-stack 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

• UI complexity for some users
• Learning curve

Best For

Coralogix is best suited 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: $0.42/GB
• Traces: $0.16/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 data through its infrastructure, so organizations should carefully validate it to meet compliance requirements.

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

• Learning curve
• UI can be improved

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: UI performance

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.

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
• Slightly abrupt cost spike from free to paid

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 requires careful setup. 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.

Conclusion: Choosing the Right Elastic Observability Alternative

Elastic Observability has powerful search and log analytics, but at the cost of extra charges for high-tier support, retention, and egress to a limited self-hosting option.

CubeAPM addresses these pain points with a cost-efficient, simple pricing model. full self-hosting, and no extra charges for customer support, e.g., or retention. Teams can move faster while saving 60–80% on observability costs. Book a free demo today and see why teams are switching to CubeAPM for scalable, future-ready observability.

Disclaimer: The information in this article reflects the latest details available at the time of publication and may change as technologies and products evolve.

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