Top 8 Sentry Alternatives for Error Tracking in 2026

Error tracking ensures that software failures do not degrade the user experience or lead to customer churn. While Sentry remains a powerful tool for catching code-level bugs, it often lacks the context required for debugging complex, distributed environments. As applications move toward microservices and serverless architectures, the line between error tracking and full-stack observability has blurred. Teams are finding that Sentry's focus on stack traces isn't enough, they need to correlate exceptions with logs, metrics, and traces to understand the root cause of failures across their entire infrastructure.

Why Teams are Moving Away from Sentry

Sentry has evolved from a lightweight error tracker into a complex observability platform. While this expansion added features like Session Replay and Cron Monitoring, it also introduced operational overhead and pricing friction that many teams find difficult to manage.

Data Sovereignty and AI Training

In 2024, Sentry updated its Terms of Service to include rights for using "Service Data" for product development, which includes AI model training. Although the company later clarified that identifying data requires explicit authorization, the initial lack of transparency caused concern in regulated sectors like healthcare or finance, where any ambiguity in data usage rights is a significant risk.

Pricing Volatility and Budget Complexity

Sentry uses a volume-based pricing model where you pay for every captured event. During a cascading failure, an application might generate millions of exceptions in minutes. This can result in massive billing spikes unless you've configured quotas/rate limits, Spike Protection, or aggressive inbound filters.

The Operational Burden of Self-Hosting

While Sentry is "Fair Source," self-hosting the modern stack is no longer a simple task. A standard installation now runs dozens of services/containers (Kafka, Redis, PostgreSQL, ClickHouse/Snuba, Relay, Symbolicator, web/worker/cron, etc.), creating a distributed architecture that becomes difficult to maintain at scale.

Top 8 Sentry Alternatives for 2026

Now that you have an overview of the top Sentry alternatives, let's dive deeper into each platform. We'll explore their key features, pricing models, and how they compare to Sentry to help you make an informed decision for your specific use case.

1. SigNoz

SigNoz is a unified observability platform. Unlike Sentry, which often feels like several disparate tools stitched together, SigNoz uses a single, unified data model for logs, metrics, and traces. It is built natively on OpenTelemetry (OTel), the industry standard for vendor-neutral instrumentation.

Technical Architecture

SigNoz uses ClickHouse as its primary storage engine. ClickHouse is a columnar database designed for OLAP workloads, which makes it far more efficient than PostgreSQL for high-cardinality telemetry (data with many unique attribute combinations, like user IDs or trace IDs). This allows SigNoz to perform complex queries across billions of rows without the latency issues that plague Sentry's Snuba layer in self-hosted environments. Because it follows the OTel standard, you do not need proprietary SDKs to send data.

Core Capabilities

• Native OTel Integration: You instrument once with OpenTelemetry and can send data to SigNoz or any other OTel-compliant backend without changing your code.
• Correlated Telemetry: When an exception is logged, SigNoz automatically links it to the specific trace span. This allows you to see the database query, the downstream API call, and the host metrics at the exact millisecond of the failure.
• Custom Dashboards: Use the advanced query builder to create visualizations for business-critical metrics, such as error rates segmented by specific customer subscription tiers or geographical regions.

Comparison to Sentry

Sentry and SigNoz overlap in functionality, but they are optimized for different engineering objectives. Sentry is popularly used for developer-first error monitoring, offering deep breadcrumbs, stack traces, and session replays that simplify frontend and mobile debugging. It is the better choice if your primary pain point is triaging exceptions and managing release health.

In contrast, SigNoz is built for full-stack observability using an OpenTelemetry-native foundation. It is designed for teams that need to analyze traces, metrics, and logs together to debug system-wide performance and distributed microservices. SigNoz offers lower and more predictable costs compared to Sentry by moving away from per-event pricing and focusing on transparent per-GB data ingestion pricing.

In a modern production environment, many teams use both tools together to gain complete coverage. Sentry often handles frontend error triage and user experience monitoring, while SigNoz provides the end-to-end observability required for backend service correlation and infrastructure debugging.

Get Started with SigNoz

You can choose between various deployment options in SigNoz. The easiest way to get started with SigNoz is SigNoz cloud. We offer a 30-day free trial account with access to all features.

Those who have data privacy concerns and can't send their data outside their infrastructure can sign up for either enterprise self-hosted or BYOC offering.

Those who have the expertise to manage SigNoz themselves or just want to start with a free self-hosted option can use our community edition.

2. Better Stack

Better Stack is an observability tool that prioritizes developer experience and log management. It is designed to replace the fragmented stack of separate tools for errors, alerts, and logs.

How It Works

• SQL for Everything: Better Stack allows you to use standard SQL to query your error logs. This is particularly useful for SREs who want to perform complex aggregations across millions of log lines without learning a new domain-specific language.
• Unified Workflow: Alerting and incident management are built directly into the log explorer. If an error rate exceeds a threshold, Better Stack handles the on-call escalation and Slack notification in a single, cohesive workflow.

Main Features

• Real-time log aggregation and analysis: Better Stack collects and processes logs in real-time, enabling quick identification and resolution of issues.
• Custom alerting and incident management: It includes built-in tools to set up custom alerts and manage incidents efficiently.
• Collaborative troubleshooting tools: Features like shared dashboards and comments foster team collaboration during debugging.
• Integrations with popular DevOps tools: Better Stack seamlessly integrates with tools like Slack, PagerDuty, and GitHub to enhance workflows.

Comparison to Sentry

Better Stack is often chosen by teams that find Sentry's interface too cluttered and want a more "SQL-first" approach to debugging and system health. It provides significant cost savings for high-volume log ingestion.

Its ecosystem, including Better Stack Logs and Better Stack Uptime, provides a comprehensive observability solution with built-in incident management. One-click integration with Better Uptime allows for easy notifications to on-call team members about application anomalies, whether from log errors or usage patterns.

3. LogRocket

LogRocket is a frontend monitoring tool that bridges the gap between error tracking and product analytics. It is most famous for its session replay capability, which acts like a video recording of your web application for developers.

Technical Highlights

• DOM Reconstruction: Instead of recording a literal video file, LogRocket records the state changes of the DOM. This results in very low bandwidth usage while allowing for perfect fidelity during playback.
• Network Interception: It captures all XHR and Fetch requests, including headers and bodies, so you can see if an error was caused by a malformed API response or a timeout.

Key Features

• Session replay and user interaction recording: LogRocket captures detailed recordings of user sessions, enabling developers to replay and analyze user interactions for better debugging and UX optimization.
• Front-end performance monitoring: It tracks key frontend metrics like page load time, JavaScript errors, and slow network requests to ensure optimal user experience. However, LogRocket's backend monitoring capabilities are limited, primarily focusing on the frontend. To complement this, integrating tools like Datadog or New Relic for backend monitoring can provide a more comprehensive view of the application's performance, helping teams track server-side issues, database queries, and API performance alongside frontend metrics.
• Error correlation with user actions: LogRocket correlates errors with specific user actions, providing detailed context that reduces debugging time by showing the exact user actions leading to each error.
• Redux and React integration: It offers seamless integration with popular frontend frameworks like React and Redux for enhanced state management and debugging.

Comparison to Sentry

Sentry has added session replay, but it lacks the advanced frustration tracking and the deep product analytics found in LogRocket. LogRocket's Galileo AI analyzes sessions for struggle indicators and severity, providing more actionable context for UX designers and developers working on frontend issues. LogRocket's Mobile SDK ensures minimal performance impact with bandwidth usage around 10KB per second. Both platforms offer strong security with TLS encryption and compliance certifications (LogRocket: SOC II, GDPR, CCPA; Sentry: SOC2, HIPAA, ISO 27001), though LogRocket's focus on user session data makes it particularly valuable for teams prioritizing frontend UX and customer experience insights.

4. Dynatrace

Dynatrace is an enterprise platform that focuses on automation. It is designed for environments with thousands of microservices where manual configuration of error tracking is no longer feasible.

Under the Hood

• OneAgent Technology: A single binary that automatically discovers and instruments every process running on a host without requiring manual SDK integration for every service.

  

• Davis AI: A deterministic AI engine that performs root cause analysis by looking at the entire topological map of your system to find the "source" of a problem.

What You Get

• OpenTelemetry integration: Dynatrace offers OpenTelemetry integration, allowing users to seamlessly collect, correlate, and analyze telemetry data across various services. This integration enables a more unified observability experience and helps streamline the troubleshooting process in complex, distributed systems.
• Distributed tracing and code-level insights:
  Provides in-depth tracing and diagnostics for distributed systems, offering actionable insights at the code level.
• Infrastructure monitoring:
  Monitors the health and performance of cloud, on-premises, and hybrid infrastructures in real time.

Comparison to Sentry

Dynatrace is an enterprise solution with pricing that is usually prohibitive for smaller startups. While Sentry focuses on lightweight error tracking with minimal overhead, Dynatrace's comprehensive monitoring may introduce higher resource consumption but delivers unmatched depth for enterprise-scale observability. Dynatrace's proprietary architecture handles large-scale data ingestion with advanced RBAC across account, environment, and management zones, compared to Sentry's basic role system. Both platforms maintain strong security (Dynatrace: end-to-end encryption, SOC 2, GDPR, HIPAA; Sentry: AES-256, SOC2, ISO 27001), but Dynatrace is purpose-built for organizations requiring sophisticated dependency mapping, infrastructure monitoring, and AI-powered insights rather than focused application error tracking.

5. BugSnag

BugSnag remains a strong contender for mobile error tracking. While Sentry has expanded its mobile SDKs, BugSnag provides more granular data on mobile-specific failures like Application Not Responding (ANR) events and Out of Memory (OOM) crashes.

Technical Highlights

• Stability Index: This feature provides a high-level score that helps product managers decide whether to focus on new feature development or technical debt repayment.
• Precision Search: You can filter errors by mobile-specific dimensions such as OS version, device model, or even WiFi strength at the time of the crash.

Feature Highlights

• Automatic error grouping and prioritization: BugSnag automatically groups related errors and prioritizes them based on severity, making it easier to manage and resolve issues.
• Release tracking and stability scores: BugSnag provides insights into the stability of your app by assigning a stability score based on the error rates during a specific release, helping teams track the health of their app over time.
• Breadcrumbs for error reproduction: BugSnag collects breadcrumbs, which are events or logs that provide context before an error occurred, enabling developers to reproduce and diagnose issues faster.
• Integration with issue trackers and communication tools: BugSnag integrates seamlessly with tools like Jira, Slack, GitHub, and others, allowing teams to streamline their workflows and respond to issues more efficiently.

Comparison to Sentry

BugSnag’s "Release Health" dashboards are more specialized for mobile release cycles than Sentry’s version. For teams managing complex mobile deployments, the ability to correlate crashes with specific A/B test variants is a significant advantage.

Both platforms maintain strong security (BugSnag/SmartBear advertise SOC 2/ISO 27001 programs and GDPR/CCPA support, with specifics varying by product and plan; Sentry: AES-256, SOC2, HIPAA, ISO 27001), though BugSnag's RBAC is more limited and available only in higher tiers. BugSnag excels for teams prioritizing mobile stability management, transparent invoicing, and low-latency performance without complexity, making it ideal for mobile-first development teams seeking efficient error tracking over full-stack monitoring capabilities.

6. New Relic

New Relic is a veteran in the APM space. It offers a comprehensive observability platform that includes everything from infrastructure monitoring to browser performance and log management.

Technical Highlights

• NRQL Query Language: A powerful SQL-like language that allows for complex data aggregation. You can alert if the error rate for a specific API version is 20% higher than the baseline over a rolling window.
• Global Synthetics: You can schedule "synthetic" users to walk through your checkout flow every minute from dozens of public locations worldwide (plus private locations behind your firewall) to ensure uptime.
• Advanced NRQL Features: New Relic's NRQL lets you build precise alerts, for example, alerting when a specific API version's error rate exceeds baseline over a rolling window using TIMESERIES and FACET for complex data aggregation.

Key Features

• Full-stack observability (errors, metrics, logs, traces): New Relic provides a unified platform to monitor every layer of your stack, integrating application performance, error tracking, infrastructure metrics, and logs in one place.
• AI-powered anomaly detection: Leverages machine learning to automatically detect anomalies and performance issues, helping teams proactively address potential problems.
• Distributed tracing and service maps: New Relic's distributed tracing enables users to track requests across services, and service maps provide clear visualizations of application dependencies.
• Custom dashboards and alerting: Create personalized dashboards for your team, set up custom alerts, and get notified when key metrics or performance indicators fall outside defined thresholds.

Comparison to Sentry

New Relic is an "all-in-one" tool that monitors the server, the database, and the network infrastructure simultaneously. Their "forever free" tier includes 100GB of data ingestion per month, which is often more generous than Sentry’s free tier for small projects.

While Sentry offers simpler setup, custom error grouping rules, and lower performance impact ideal for focused error management, New Relic excels with extensive cloud service integrations and advanced monitoring capabilities including synthetic testing and automatic error grouping by stack traces and HTTP requests. Learn more about new relic vs sentry.

7. Raygun

Raygun provides a suite of tools for error tracking, real user monitoring (RUM), and frontend performance. It is particularly strong in the .NET ecosystem but supports all major programming languages.

Technical Highlights

• Deployment Tracking: It directly correlates error spikes with specific code deployments, allowing for instant rollbacks if a new release introduces a regression.
• P99 Latency Visuals: Provides detailed breakdowns of how different users experience page load times across various browsers, devices, and global locations.

Key Features

• Real user monitoring and session tracking: Raygun captures and analyzes real user interactions, providing insights into the actual performance experience for users and how it affects their journey across applications.
• Error grouping and prioritization: Automatically groups errors and crashes to avoid overwhelming developers with duplicate issues and allows prioritization based on severity or user impact.
• Multi-language and platform support: Raygun supports a wide range of programming languages and platforms, including JavaScript, .NET, Java, iOS, and Android, ensuring comprehensive monitoring coverage across your stack.

Comparison to Sentry

Raygun's RUM capabilities are more integrated into the core product than Sentry's. It provides a more user-centric view of performance, which is often more useful for frontend-focused engineering teams.

While Sentry offers broader backend monitoring capabilities and self-hosting options for strict data governance, Raygun excels with deployment tracking for release correlation and deep RUM integration for frontend performance insights.

8. Datadog

Datadog is the dominant player in the enterprise observability market. It is often used by SRE teams that need to monitor thousands of Kubernetes pods and serverless functions in a single location.

Technical Highlights

• Unified Service Mapping: Automatically visualizes how your microservices communicate, helping you identify which service is the bottleneck during a cascading failure (a chain reaction of failures across multiple services).
• Watchdog AI: Uses machine learning to detect anomalies in your metrics that might not trigger a traditional threshold-based alert, such as a slow increase in memory usage.

Key Features

• Full-stack observability: Includes logs, metrics, and traces to offer a complete view of your application's performance and health.
• AI-powered alerting and anomaly detection: Automatically detects anomalies and triggers alerts based on AI-driven insights.
• Extensive integration ecosystem: Supports a wide range of integrations with cloud platforms, services, and tools.
• Custom dashboards and reporting: Allows users to create tailored dashboards for real-time monitoring and performance tracking.

Comparison to Sentry

Datadog is significantly more expensive and has a steeper learning curve than Sentry. However, it eliminates "tool sprawl" by consolidating infrastructure monitoring, error tracking, and security auditing into one platform.

While Sentry offers simpler click-based alert configuration, self-hosting options for greater control, and real-user monitoring capturing live errors with lower resource consumption, Datadog provides sophisticated customizable alerting with variables and rules, comprehensive incident management, and automatic scalability without backend configuration needs.

Hope we answered all your questions regarding Sentry alternatives. If you have more questions, feel free to use the SigNoz AI chatbot, or join our slack community.

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