Top 8 Honeycomb Alternatives in 2025: Features, Pricing & Reviews

Honeycomb has established itself as a robust observability platform with a powerful query engine, high-cardinality tracing, and signature BubbleUp debugger.. Built for developers, it shines in debugging workflows and dynamic querying.

As application performance monitoring (APM) continues its rapid growth—projected to reach USD 12.27 billion in 2025 and as observability needs evolve across platform, DevOps, and SRE teams, many users are running into serious limitations. Common concerns of Honeycomb include a steep learning curve, mounting telemetry costs which scales with high cardinality data, smaller-third party integrations ecosystem, and lack of sampling strategy.

CubeAPM stands out as the most capable alternative. It offers full MELT coverage—including traces, logs, metrics, RUM, synthetics, infra monitoring, and error tracking—alongside smart sampling that captures the most meaningful data while reducing ingestion costs by up to 80%. It supports on-prem and BYOC deployments, offers native OpenTelemetry support, and charges $0.15/GB with zero per-user fees. With fast Slack-based support, enterprise SLAs, and unified dashboards, CubeAPM delivers what Honeycomb users wish they had—without the complexity or surprise bills.

In this article, we break down the top 8 alternatives to Honeycomb in 2025, comparing tools based on observability coverage, OpenTelemetry support, cost transparency, deployment flexibility, and real-world usability.

Top 8 Honeycomb Alternatives

1. CubeAPM
2. Signoz
3. Datadog
4. Dynatrace
5. Splunk Appdynamics
6. New Relic
7. Better Stack
8. Uptrace

Why Look for Honeycomb Alternatives?

While Honeycomb excels at high-cardinality event tracing and fast querying, several operational and architectural limitations are prompting teams to seek more comprehensive observability platforms—especially as telemetry scales and compliance requirements grow more stringent.

Here are the key reasons why modern DevOps, SRE, and platform teams are looking beyond Honeycomb in 2025:

1. Unpredictable Costs

Pricing is based on event volume, which can escalate with high-cardinality data. Honeycomb’s pricing model is based on the volume of events ingested, which may seem straightforward—but in practice, it can be highly volatile. As systems scale, event volume doesn’t just grow linearly; it spikes with high-cardinality dimensions like user IDs, session tokens, regions, or request paths. This means even a single query across those fields can generate a massive spike in cost.

Honeycomb Cost Complexity and Escalation at Scale with example

Honeycomb uses an event-based pricing model, starting at $130/month for 100 million events. For a mid-sized company ingesting 5 billion events per month (roughly 2,000 spans/sec), the estimated cost would be around $6,500/month, solely for trace ingestion. Similarly, a startup emitting 3 billion events monthly (about 1,200 spans/sec) could expect a bill of approximately $3,900/month. These estimates assume linear scaling, though actual billing may vary based on tier thresholds, included features, and overage protections. Additional costs may apply for extended data retention, SLOs, or enterprise features.

CubeAPM, by contrast, offers flat, usage-based pricing at $0.15/GB, with no per-user charges or feature-based upsells. Ingesting 10TB/month—typical for companies emitting 3–5 billion spans—would cost $1,536/month, covering APM, logs, infra monitoring, RUM, synthetics, and unlimited users. Smart sampling helps cut volume without losing critical signal.

So while Honeycomb’s event pricing may appear simple, it becomes expensive at scale and hard to predict. CubeAPM’s all-in-one model offers 60–75% cost savings, predictable billing, and complete observability without the fine print.

2. Advanced Sampling via Refinery, Without Built-In Adaptive Intelligence

Honeycomb’s native tail-based sampling, powered by its Refinery agent, allows the system to evaluate complete traces before deciding to retain them—ensuring critical outliers like errors and latency spikes aren’t missed. Teams can define highly granular sampling rules using span attributes such as path, latency, or status codes. However, this approach requires manual configuration and lacks out-of-the-box adaptive automation found in platforms like CubeAPM

Honeycomb provides burst protection: if you spike >2× daily target, excess events aren’t counted for EPM—but this only applies within limits and isn’t a substitute for proper sampling or planning

4. SaaS-Only Model Limits Deployment Flexibility

Honeycomb does not offer on-prem or BYOC (Bring Your Own Cloud) deployment. This is a dealbreaker for teams in regulated sectors (e.g., finance, healthcare, government) where data residency, latency control, and internal compliance audits require full control over data pipelines. In contrast, CubeAPM supports on-premise deployments where all telemetry stays within the customer’s cloud, with no egress or external storage concerns.

5. Steep Learning Curve for New Teams

Honeycomb is built for developers who love to interact with their data. It has a steep learning curve as  mastering the query builder, columnar data model, and service graphs takes significant time and training. For fast-moving teams or those unfamiliar with Honeycomb’s paradigms, this can be a major adoption barrier.

6. Limited Integrations and Ecosystem

Compared to tools like CubeAPM, which support native integration with Prometheus, OpenTelemetry, Datadog, and Elastic, Honeycomb’s integration catalog is more limited. This creates friction for teams with diverse infrastructure stacks that expect out-of-the-box compatibility.

7. Data retention and role for business metrics

• A reviewer observed that Honeycomb “only does timeseries,” lacking longer-term roll-ups or visualization optimized for business metrics reporting — meaning it often requires pairing with another BI tool G2.
• Others also mention that sometimes data retention window (e.g. 60 days) may be insufficient for compliance or long-term historical analysis G2.
• By default, all plans include 60‑day fixed retention. Longer retention is available but may incur additional fees

 8. Less extensive integration ecosystem

• According to The CTO Club’s recent review, Honeycomb still lacks the breadth of integrations offered by more established APM/observability platforms

Criteria for Suggesting Honeycomb Alternatives

When evaluating alternatives to Honeycomb, we focused on tools that address its biggest limitations: cost unpredictability, partial observability, lack of deployment flexibility, and limited ecosystem maturity. Based on market trends, real user reviews, and platform comparisons, here are the six criteria we used to shortlist stronger, production-grade alternatives:

1. Smart Sampling & Signal Optimization

With growing telemetry volumes, static sampling rules aren’t enough. We looked for platforms that support context-aware, dynamic sampling—based on latency spikes, error codes, or traffic anomalies—to help teams retain relevant data without overspending.

2. Deployment Flexibility & Compliance

Tools that offer SaaS, on-premise, options scored higher. Honeycomb is SaaS-only, which limits its adoption in regulated industries or environments where data localization is non-negotiable.

3. Transparent & Predictable Pricing

We favored tools with flat, usage-based pricing models over per-event or cardinality-based billing.  CubeAPM offers 60–75% cost savings with a simpler, all-inclusive billing model.

4. Ease of Use & Enterprise Readiness

Tools with easy onboarding, intuitive UI, built-in alerting, and fast support response times stood out. Enterprise-readiness was measured via SLAs, incident support, and platform maturity. While Honeycomb is powerful, users frequently report a steep learning curve and fragmented workflows when combined with other tools.

5. Rich Ecosystem & Native Integrations

Modern teams operate across a diverse stack—from Kubernetes and AWS to CI/CD pipelines and third-party APIs. Platforms with a large, actively maintained integration ecosystem scored higher. Compared to Datadog’s 900+ integrations or CubeAPM’s broad OTEL/Prometheus/Elastic compatibility, Honeycomb’s ecosystem is narrower, requiring more custom setup and third-party dependencies for full coverage.

Honeycomb Overview

Known for:

Honeycomb is best known for its high-cardinality trace analysis and interactive debugging capabilities tailored for complex, distributed systems. Designed for engineering teams navigating microservices and dynamic environments, Honeycomb provides powerful query-driven visualizations that help uncover patterns, anomalies, and outliers across millions of trace events—making it a go-to tool for root cause analysis and understanding service-level behavior at scale.

Standout Features:

1. Native OTEL Support:

   Honeycomb offers native integration with OpenTelemetry, allowing teams to ingest standardized telemetry data without vendor lock-in. This ensures flexibility and compatibility across diverse environments while enabling easy instrumentation across services.

2. BubbleUp Debugger:

   A powerful visual tool that automatically surfaces anomalies in high-cardinality fields, helping engineers quickly pinpoint the root cause of issues without writing complex queries.

3. Columnar Query Engine:

   Honeycomb’s custom-built columnar engine is optimized for structured, event-based telemetry, enabling high-speed, flexible querying at scale—even in environments with millions of spans.

4. Tail-Based Sampling:

   Supports capturing traces after completion, preserving rare but critical anomalies such as latency outliers. This leads to better signal retention and more accurate debugging, especially in distributed systems.

Key Features:

1. High-Cardinality Event Support

   Handles millions of unique field combinations—ideal for dynamic metadata like user IDs, sessions, or geographic segments.

2. Custom Query Builder

   Honeycomb uses a SQL-like interface to enable fine-grained slicing/dicing of telemetry data for interactive debugging.

3. Service Map & Trace Spans Explorer

   Visualizations for service-to-service call flows and deep inspection of individual spans and attributes.

4. Frontend SDKs (RUM)

   Recently added support for Real User Monitoring via JavaScript SDKs, though this requires manual setup and lacks out-of-the-box UX metrics.

5. Basic Alerting & SLOs

   Provides alerting based on derived columns or thresholds. More suited for debugging than real-time operational monitoring.

Pros:

• Excellent for debugging high-cardinality production issues
• Fast query performance even across billions of events
• Ideal for event-driven architectures and async systems
• Integrates well with OpenTelemetry pipelines

Cons:

• No native infrastructure monitoring or metrics dashboards
• Event-based pricing gets expensive as span count grows
• Limited log support and no built-in log-to-trace correlation
• Steep learning curve for new users unfamiliar with Honeycomb’s data model
• Honeycomb provides burst protection: if you spike >2× daily target, excess events aren’t counted for EPM
• Lacks longer-term roll-ups or visualization optimized for business metrics reporting
• Data retention window (e.g. 60 days) may be insufficient for compliance or long-term historical analysis G2.

Best For:

• DevOps and SRE teams focused on trace-level debugging
• Use cases involving microservices, high-cardinality data, and asynchronous systems
• Organizations that already manage logging/infra separately and want a tracing-first workflow

Pricing & Customer Reviews:

• Free Tier: 20 million events/month
• Pro Tier: $130/month per 100 million events
• Tail-based sampling is included, but additional charges apply for longer retention or advanced derived columns
• Pricing becomes unpredictable with scale due to high-cardinality data and unbounded event generation
• G2 Rating: 4.7/5
  Most users praise its power and speed for debugging. Common complaints include the lack of full-stack support, steep learning curve, and difficulty forecasting costs.

Top 8 Honeycomb Alternatives

1. CubeAPM

Known for:

CubeAPM is a modern observability platform purpose-built for teams seeking end-to-end visibility, efficient cost control, and data residency compliance—all through a native OpenTelemetry pipeline. Unlike cloud-only incumbents, it offers flexible hosting , high-volume performance, and transparent pricing without per-user limitations.

CubeAPM is purpose-built to gather telemetry from both applications and infrastructure, processing the data locally to avoid dependency on third-party cloud services. This edge-based approach enables page loads that are 2–4 times faster than traditional cloud-hosted APM tools. One of its defining strengths lies in its intelligent sampling engine, which uses contextual signals to retain critical data—ensuring high fidelity while minimizing overhead, even under heavy traffic loads. CubeAPM leverages distributed traces and system metrics to surface meaningful insights and enable early issue detection.

Additionally, it offers native support for monitoring a wide range of infrastructure technologies right out of the box, including Kubernetes, Redis, Kafka, MySQL, and MS SQL. Its automatic integration with AWS services—such as EC2, EBS, RDS, DynamoDB, and Lambda—further enhances its observability reach, making it a standout solution among modern telemetry platforms.

Key Features:

1. Adaptive Smart Sampling

   Prioritizes critical traces like error spikes and latency outliers, while reducing noise—allowing teams to retain meaningful data without ballooning ingestion costs.

2. Unified MELT Stack

   Provides a fully integrated experience across metrics, events, logs, and traces—alongside native support for RUM and synthetic testing.

3. Instant Infrastructure Observability

   Built-in metrics collection for Kubernetes, cloud infrastructure, and Linux systems with no third-party plugins required.

4. Flexible Hosting Models

   Deploy CubeAPM in your own cloud or on-prem to maintain data control and meet compliance requirements like GDPR or HIPAA.

5. Wide Integration Support

   Seamlessly compatible with OpenTelemetry, Prometheus, Datadog agents, and Elastic exporters.

Standout Features:

• Smart sampling reduces costs by intelligently filtering low-value traces.
• Self-hosting avoids public cloud egress fees and enables full data residency.
• Transparent pricing includes unlimited users—no licensing surprises.
• Easy migration tools for Datadog, New Relic, and Uptrace—up and running in under an hour.
• Responsive Slack-based support channel with sub-5-minute TAT.

Pros:

• Up to 80% lower TCO compared to Datadog and New Relic.
• Full observability: traces, metrics, logs, RUM, synthetics, and infra coverage.
• Pricing is usage-based with no charge per seat or team member.
• Enterprise-ready support and fast troubleshooting from core engineering.

Cons:

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

Best For:

• DevOps and platform teams looking to cut observability costs without losing visibility.
• Engineering teams needing OpenTelemetry-native observability with data localization.

Pricing & Customer Reviews:

• Pricing: Starts at $0.15 per GB of ingestion
• Rating:  4.7/5 (based on demo feedback, early adopter reviews, and pilot programs)

CubeAPM vs Honeycomb:

While Honeycomb is tailored for deep trace debugging, CubeAPM provides a complete observability platform—spanning logs, metrics, frontend performance, synthetic testing, and infra monitoring. CubeAPM also includes smart sampling (absent in Honeycomb) and supports self-hosting, making it a stronger fit for cost-sensitive or compliance-heavy teams that need full-stack visibility out of the box.

2. SigNoz

Known for:

SigNoz is a developer-friendly, OpenTelemetry-native observability platform designed to offer full MELT (Metrics, Events, Logs, Traces) coverage with the flexibility of open-source deployment. It’s widely favored for its modern UX, ClickHouse-backed analytics engine, and seamless self-hosted or cloud deployment options.

Key Features:

1. Native OpenTelemetry Integration

   SigNoz is built ground-up to work with OpenTelemetry SDKs and supports Prometheus metrics, enabling vendor-agnostic telemetry collection across distributed systems.

    

2. Complete MELT Stack Support

   The platform covers:

    

   • Distributed Tracing with service maps and latency insights 
   • Log ingestion with filter and search 
   • Infrastructure metrics via Prometheus exporters 
   • Event-driven alerting via integrated rule engine 

3. Deployment Flexibility

   Teams can deploy SigNoz using Docker or Helm on Kubernetes. For those looking to reduce infrastructure management, SigNoz also offers a hosted cloud version.

    

4. Custom Dashboards and Querying

   Comes with preconfigured dashboards and allows advanced querying through ClickHouse’s SQL-like interface, blending performance and customizability.
   

Standout Features:

• Transparent, open-source roadmap via GitHub
• ClickHouse-based backend for high-speed analytics
• Unified view of logs, metrics, and traces
• Community Slack and open documentation for troubleshooting
• Support for tail-based sampling in OTEL pipelines

Pros:

• Fully OTEL-compliant and free of vendor lock-in
• Active open-source community (20,000+ GitHub stars)
• Smooth self-hosting with Docker and Helm options
• Single-pane visibility for MELT telemetry
• Managed cloud plan available for faster go-live

Cons:

• No native support for intelligent or dynamic sampling
• High operational overhead due to Kafka, Alertmanager, and ClickHouse stack
• Slower query response under large-scale/high-cardinality telemetry
• Community-tier support with slower response times (1–2 days)

Best For:

• DevOps and SRE teams exploring open-source observability stacks
• Startups and mid-sized companies looking to avoid vendor lock-in
• Engineering teams already familiar with ClickHouse or self-managed telemetry stacks

Pricing & Customer Reviews:

• Self-hosted: Free
• Cloud version: Starts at $49/month + $0.30/GB for logs, $0.30/GB for traces
• Infra costs (self-hosted): Customer pays for compute, storage, and networking
• G2 Rating:  4.5/5
  Users applaud its UI and open-source ethos, but note performance issues under scale and limited smart sampling capabilities as key challenges.

SigNoz vs Honeycomb:

Honeycomb is optimized for high-cardinality trace debugging in SaaS environments, with strong tail-based sampling and fast querying—but at the cost of event-based pricing and no self-hosting. SigNoz, while lacking some of Honeycomb’s advanced debugging features, offers full MELT support, OpenTelemetry-native ingest, and flexible self-hosting. For teams prioritizing cost control and open-source transparency, SigNoz is a strong Honeycomb alternative.

3. Datadog

Known for:

Datadog is a well-established observability and monitoring platform widely used across cloud-native, enterprise, and DevOps environments. Known for its extensive out-of-the-box integrations, Datadog brings together logs, infrastructure metrics, application traces, and security insights into a single, SaaS-based interface.

Key Features:

1. Infrastructure Monitoring at Scale

   Real-time insights into host, container, and cloud infrastructure using Datadog agents and prebuilt dashboards.

2. Distributed APM Tracing

   Captures service-to-service interactions and latency across APIs, databases, and external systems.

3. Centralized Log Aggregation

   Enables log ingestion, full-text search, and correlation through features like Live Tail and archive scanning.

4. Cloud Security & Compliance Monitoring

   Monitors for vulnerabilities, misconfigurations, and access violations across your cloud workloads.

5. Synthetic and Real User Monitoring (RUM)

   Includes browser-based session tracking and synthetic tests for simulating user flows and performance validation.

Standout Features:

• Over +900 native integrations, including Kubernetes, AWS, Azure, GitHub, and Jenkins.
• AI-based “Watchdog” engine detects anomalies, latency spikes, and traffic shifts.
• All observability domains—logs, infra, APM, RUM, security—accessible from a single control plane.
• Scales effectively for global deployments and enterprise workloads.

Pros:

• Broad feature set spanning observability and security.
• Prebuilt dashboards, anomaly detection, and alerting for quick setup.
• Ideal for multi-cloud environments and large-scale infrastructure.
• Rich ecosystem and community support.

Cons:

• Costs escalate quickly with scale due to separate charges per GB, per host, and per feature module.
• Does not support smart, context-aware sampling—relies on fixed-rate probabilistic methods.
• Known for opaque billing and frequent complaints about overages and pricing complexity.
• SaaS-only deployment—no support for on-prem or BYOC, which limits compliance control.

Best For:

• Enterprises with large budgets seeking a comprehensive, all-in-one monitoring solution.
• Teams needing deep integrations and centralized dashboards across all layers of infrastructure and applications.

Pricing & Customer Reviews:

• Pricing:
  • APM: From $31/host/month
  • Logs: Approx. $0.10/GB + $1.70 per million events (15-day retention)
  • Infrastructure Monitoring: $15/host/month
  • Synthetic Monitoring, RUM, and other features billed separately
• Rating:  4.4/5 on G2
  Users consistently praise Datadog for its breadth and polish, but often flag concerns about unpredictable pricing, billing complexity, and vendor lock-in.

Datadog vs Honeycomb:

While Honeycomb focuses on trace-first observability, Datadog offers a more complete observability and security suite—with logs, metrics, RUM, and cloud security tools. However, Datadog lacks smart sampling and cost transparency. Many teams using Honeycomb for traces eventually hit similar cost ceilings in Datadog when trying to scale to full-stack coverage. CubeAPM, in contrast, delivers the same breadth as Datadog with smart sampling, flat pricing, and OpenTelemetry-first design—making it a more sustainable long-term option for modern engineering teams.

4. Dynatrace

Known for:

Dynatrace is an enterprise observability and automation platform built for large-scale, high-complexity environments. It’s particularly known for its AI-powered root cause detection and zero-config auto-discovery—making it a preferred choice for enterprises running massive hybrid cloud stacks.

Key Features:

1. Davis® AI Engine

   Anomaly detection, issue correlation, and root cause analysis powered by machine learning—automatically surfaces incidents without manual alerting logic.

2. End-to-End Monitoring

   Unified visibility across infrastructure, backend services, frontend performance, and third-party APIs—including RUM and synthetics.

3. Automatic Topology Mapping

   Auto-detects service relationships, dependencies, and traffic flows—eliminating the need for manual instrumentation.

4. Log Management

   Ingests logs from across environments, enabling context-rich troubleshooting alongside traces and metrics.

5. Application-Level Insights

   Provides deep diagnostics into runtime performance across JVM, .NET, Node.js, and other environments.

Standout Features:

• Complete observability coverage with AI-driven correlation of logs, metrics, traces, and user experience.
• No manual setup needed—services, containers, and code paths are auto-discovered in real time.
• Ties business KPIs directly to technical metrics for real impact measurement.
• Also offers application security analysis to flag vulnerabilities and compliance risks.

Pros:

• Intelligent automation and predictive alerting significantly reduce manual configuration.
• Deep tracing and diagnostic tools built for enterprise-grade monitoring.
• Real-time anomaly detection across highly dynamic, distributed systems.
• Excellent for hybrid and multicloud architectures.

Cons:

• High pricing tiers, especially when using AI, log indexing, or security features.
• Proprietary agent model limits flexibility—less friendly to OpenTelemetry-native workflows.
• Custom dashboards are less flexible than open-source tools like Grafana or CubeAPM.
• While self-hosting is possible, setup is complex and best suited for enterprise IT teams.

Best For:

• Large enterprises managing thousands of services and requiring automated, AI-powered observability.
• Teams that want insight into not just what broke—but why it broke—across cloud and on-prem deployments.

Pricing & Customer Reviews:

• Pricing: $0.08/hour per 8 GiB host ($57.60/host/month)
• G2 Rating:  4.4/5
  Users appreciate Dynatrace’s automation and diagnostic depth, but often point to its rigid pricing model and steep learning curve as drawbacks.

Dynatrace vs Honeycomb:

Dynatrace delivers far more automation, dependency discovery, and AI-driven insights than Honeycomb—but at a significantly higher cost. While Honeycomb excels in trace-centric debugging, it lacks Dynatrace’s automation, service mapping, and business-context monitoring. However, Dynatrace’s lack of native OpenTelemetry support and closed architecture can be a dealbreaker for teams seeking flexibility and vendor-neutral ingest.

5. Splunk AppDynamics

Known for:

Splunk AppDynamics—born out of Cisco’s APM offering and now part of the broader Splunk ecosystem—focuses on tying application performance to business impact. It’s tailored for enterprises that require deep diagnostics of transactional workflows and full-stack visibility tied to KPIs, especially in traditional or hybrid environments.

Key Features:

1. Business Transaction Tracing

   Monitors end-to-end execution paths of business-critical workflows—spanning services, databases, and infrastructure layers.

2. Application Performance Monitoring (APM)

   Provides detailed code-level insights, performance baselines, and anomaly detection across app components.

3. Infrastructure Awareness

   Visualizes how server, container, and cloud performance affects application behavior in real time.

4. Custom Dashboards & SLA Rules

   Enables SLA-based alerting and business-aligned dashboards for both technical and executive stakeholders.

5. Synthetic Monitoring

   Simulates user interactions across web journeys to detect regressions and outages before users experience them.

Standout Features:

• Business-application linkage helps prioritize issues that impact revenue or user satisfaction.
• Prebuilt health rules and SLA violation tracking embedded into monitoring workflows.
• Visual code traces across tiers, functions, and service layers for Java, .NET, PHP, and Node.js apps.
• Now enhanced with Splunk’s logging and infrastructure monitoring modules for broader context.

Pros:

• Exceptional diagnostics for complex, layered applications.
• Useful for correlating technical issues with user or business impact.
• SLA-driven alerting and dashboards designed for cross-functional teams.
• Integrates naturally into enterprises already using Splunk’s logging and SIEM tools.

Cons:

• Initial configuration can be heavy—especially for custom instrumentation.
• Total cost of ownership can spike when bundling APM, infra, synthetics, and logging modules.
• Native OpenTelemetry support is limited; relies on proprietary agents.
• UI can feel dated and rigid compared to newer platforms like CubeAPM or Grafana.

Best For:

• Enterprise IT and engineering teams managing complex Java/.NET workloads.
• Organizations looking to tie app performance directly to business KPIs and SLAs.

Pricing & Customer Reviews:

• Pricing: ~$75/host/month (annual billing)
• G2 Review Rating: 4.3/5
  Users value its diagnostic depth and SLA features, but frequently mention the complexity of setup and high overall cost when deploying across the full stack.

Splunk AppDynamics vs Honeycomb:

AppDynamics delivers much deeper insight into business transactions and application internals than Honeycomb, but falls short in flexibility and OpenTelemetry adoption. Honeycomb is more agile and trace-focused, while AppDynamics is a better fit for large teams managing critical, legacy-heavy environments. For teams wanting the depth of diagnostics with modern standards and lower cost, CubeAPM offers the best of both worlds—with full MELT coverage, smart sampling, and a frictionless, self-hosted option that’s easier to deploy and scale.

6. New Relic

Known for:

New Relic is a cloud-based observability platform offering a full suite of telemetry tools under a unified dashboard. Recognized for its usage-based pricing model and its Telemetry Data Platform (TDP), it provides engineers with real-time insights across application performance, infrastructure health, logs, RUM, and synthetic monitoring—all accessible via its custom query language, NRQL.

Key Features:

1. Comprehensive MELT Monitoring

   Supports metrics, events, logs, traces, frontend performance, synthetics, and error tracking—streamlined into a single UI.

2. Telemetry Data Platform (TDP)

   Acts as a centralized ingestion and analytics engine for OpenTelemetry, Prometheus, and New Relic agents.

3. Customizable Dashboards with NRQL

   Enables real-time querying and visualization using New Relic Query Language—ideal for tailored reporting.

4. Frontend and Mobile Performance Tracking

   Includes RUM for browser analytics and SDKs for mobile telemetry across iOS and Android.

5. Integrations & Auto-Instrumentation

   Auto-detects services and containers, with built-in integrations for Kubernetes, AWS, Azure, and more.

Standout Features:

• Unified platform for full-stack telemetry with deep frontend and mobile visibility.
• Allows ingestion from third-party tools and protocols including OTEL and Prometheus.
• High-quality alerting and SLO management integrated into workflows.
• Extensive language support with auto-instrumentation options.

Pros:

• End-to-end visibility across the entire software lifecycle.
• Seamless ingestion from both New Relic agents and open telemetry pipelines.
• Strong RUM and synthetic monitoring capabilities built in.
• Dashboards and alerts highly customizable via NRQL.

Cons:

• Pricing can escalate quickly—charges by GB ingested and per user license.
• Full-access licenses are expensive, starting at $400/user/month.
• Data residency is restricted to New Relic’s infrastructure—no self-hosting or BYOC options.
• Smart sampling is absent; relies on traditional head-based sampling strategies.

Best For:

• Mid-size and enterprise teams looking for a quick-start observability solution.
• Organizations okay with cloud-only deployments and usage-based billing.

Pricing & Customer Reviews:

• Free Tier: Perpetual free tier and 100 GB/month of data ingest included.
• Paid: Ingestion based pricing of $0.35/GB + $400/user/month for full access
• G2 Rating: 4.4/5
  Users appreciate the UI and broad telemetry support, but frequently mention concerns about pricing complexity, vendor lock-in, and data locality restrictions.

New Relic vs Honeycomb:

Both New Relic and Honeycomb are SaaS-first observability platforms, but they differ in approach. New Relic offers a broader feature set—including frontend, infra, logs, and synthetics—while Honeycomb specializes in traces and debugging. However, New Relic’s high per-user cost and usage-based billing can quickly add up. For teams wanting OpenTelemetry-native ingest, full-stack coverage, and on-premise flexibility, CubeAPM provides a more scalable and cost-efficient option—especially for engineering orgs wary of surprise bills or vendor lock-in.

7. Better Stack

Known for:

Better Stack (formerly Better Uptime) blends log aggregation with uptime and incident monitoring in a visually polished, developer-friendly platform. It’s popular with engineering teams who want fast, minimal setup for monitoring APIs, websites, and services without the complexity of full APM suites.

Key Features:

1. Uptime and Health Checks

   Monitors endpoints using HTTP, ping, port, and SSL checks—alongside alert policies and on-call rotations.

2. Built-In Log Monitoring

   Offers structured log ingestion, real-time querying, and retention management with a SQL-like search syntax.

3. Incident Management & Escalation

   Provides status pages, alert routing, incident timelines, and native integrations with Slack, Teams, and email.

4. Custom Dashboards

   Clean, modern interface with markdown-based dashboards ideal for status communication or public visibility.

5. Developer-Centric Workflows

   Git-based alert configuration and YAML support make it easy to manage via code.

Standout Features:

• Extremely polished UI and fast onboarding—ideal for small teams.
• Combines uptime, logging, and alerting into one platform.
• Free tier includes generous limits for basic monitoring and logging needs.
• Public and private status pages for incident transparency.

Pros:

• Simple to deploy with little learning curve.
• Great for monitoring web services, APIs, and external uptime.
• Built-in alerting and logging reduce the need for multiple tools.
• Well-suited for early-stage teams or startups.

Cons:

• No support for distributed tracing or deep APM features.
• Lacks native support for OpenTelemetry and synthetic monitoring.
• Not ideal for teams with complex backend or microservice environments.
• Scalability limitations when log ingestion grows past a few GB/day.

Best For:

• Solo developers, frontend teams, and early-stage startups who want simple monitoring without the overhead of full observability platforms.
• Teams replacing Pingdom or combining lightweight logging and uptime tools.

Pricing & Customer Reviews:

• Pricing:
  • Free tier includes 10 monitors and 500MB logs/day
  • Paid plans range from $25 to $850+ depending on log, trace, and metrics volume
• G2 Rating: 4.6/5
  Users love the modern UI and ease of use, but note the lack of backend observability features as a trade-off.

Better Stack vs Honeycomb:

BetterStack and Honeycomb serve very different purposes. BetterStack is built for uptime and incident alerting with basic log visibility, while Honeycomb focuses on trace-level debugging in distributed systems. Neither offers complete MELT observability. CubeAPM fills that gap—offering logs, traces, infra monitoring, RUM, and synthetics in a single, OpenTelemetry-native platform with flexible pricing and enterprise support.

8. Uptrace

Known for:

Lightweight, OpenTelemetry-native tracing and metrics with self-hosted flexibility. Uptrace appeals to small teams seeking low-cost, vendor-neutral observability that they can run within their own infrastructure.

Standout Features:

• Seamless OpenTelemetry ingestion with no proprietary agent dependencies
• ClickHouse-powered trace analysis with SQL-like queries
• Lightweight UI for core diagnostics and span inspection
• Basic Prometheus-compatible metric ingestion
• Low-overhead Docker-based deployment for custom setups

Key Features:

1. End-to-end distributed tracing via OpenTelemetry
2. Prometheus integration for infra-level metrics
3. SQL-based querying on traces using ClickHouse
4. Self-hosted deployment options (Docker/K8s)
5. Minimal alerting and dashboarding for essential monitoring

Pros:

• Fully OTEL-native and vendor-agnostic
• Cost-effective for low-volume environments
• Runs entirely on your infrastructure — no data leaves your network
• Integrates cleanly with existing Prometheus setups

Cons:

• No built-in support for RUM or synthetic monitoring
• No smart sampling or dynamic trace prioritization
• Rudimentary dashboards; limited customization
• Significant maintenance effort required to manage backend components
• Lacks enterprise features like SLAs or managed hosting

Best For:

• Engineering teams with in-house observability expertise
• Startups looking for a self-hosted, OTEL-native tracing tool
• Teams focused on trace debugging over full MELT stack

Pricing & Customer Reviews:

• Pricing: $0.08/GB for traces/logs; $0.001 per active time series
• ProductHunt Rating: 4.8 / 5

Uptrace vs Honeycomb:

Both platforms are OTEL-focused, but Honeycomb offers stronger debugging tools, tail-based sampling, and hosted simplicity—ideal for production-grade environments. Uptrace is better suited for smaller teams who want total control and don’t need advanced UX or frontend observability. CubeAPM sits in the middle, offering full MELT coverage, compliance-ready hosting, and smart sampling with predictable pricing.

Conclusion: Choosing the Right Honeycomb Alternative

As observability stacks mature, engineering teams are demanding more than just trace visualization and high-cardinality querying. While Honeycomb remains strong in exploratory debugging and event-level analysis, its SaaS-only model, unpredictable event-based pricing, and limited full-stack coverage make it less viable for teams seeking comprehensive observability and cost control at scale.

Why CubeAPM Leads the Pack

CubeAPM offers a modern, OpenTelemetry-native alternative that’s purpose-built for both depth and scale. With flat $0.15/GB pricing, support for full MELT observability (metrics, logs, traces, synthetics, RUM), smart sampling, and on-prem hosting, CubeAPM empowers teams to achieve 60–80% cost savings while retaining data control. Its developer-first experience, fast dashboards, and responsive Slack-based support make it ideal for startups and enterprises alike.

Whether you’re scaling past Honeycomb’s pricing model or looking for unified visibility across your stack, CubeAPM delivers a future-ready, cost-efficient, and compliance-friendly observability path forward.