Top 8 Distributed Tracing Tools [Updated for 2025]

Modern applications run on microservices, containers, and serverless functions across cloud and hybrid setups. It boosts scalability, but also makes troubleshooting harder for teams. For instance, when one request touches dozens of services, finding the root cause becomes difficult.

Distributed tracing solves this by following a request across frontends, backends, and external APIs to reveal performance bottlenecks. By 2022, 47% of organizations were already using tracing, with 70% of the rest planning adoption within two years.

CubeAPM stands out as the best distributed tracing tool with an OpenTelemetry-native, cost-efficient option that unifies tracing, metrics, logs, and RUM—making it one of the best choices for teams seeking visibility with pricing transparency.

In this guide, we compare the top 8 distributed tracing tools of 2025, covering features, pricing, pros/cons, and use cases. 

Top Distributed Tracing Tools

1. CubeAPM
2. Datadog
3. New Relic
4. Dynatrace
5. SigNoz
6. Sentry
7. Elastic Observability
8. Honeycomb

What Is a Distributed Tracing Tool?

A distributed tracing tool is a core observability solution that tracks a single request or transaction as it travels across multiple services, APIs, databases, and infrastructure components. Technically, it records spans (individual operations) and assembles them into a trace (the full journey), giving developers end-to-end visibility into latency, dependencies, and failure points within complex, distributed systems.

Using distributed tracing tools, teams can reduce mean-time-to-resolution (MTTR), expose hidden bottlenecks, improve user experience, and help teams optimize performance costs. These tools also tie traces with logs and metrics for faster, more accurate root cause analysis.

Example

Imagine an e-commerce checkout request: when a user clicks “Buy,” that action may pass through a frontend service, payment API, inventory system, and shipping microservice. If the process slows down, distributed tracing shows exactly which step caused the delay—say, a payment API timeout—so engineers can resolve the issue quickly.

Why Teams Choose Different Distributed Tracing Tools?

Unpredictable tracing costs

Tracing emits lots of spans, and vendors often price by ingested spans / GB, indexed spans, hosts, and retention—a mix that’s hard to forecast. Many teams report “bill shock” when traffic or instrumentation coverage grows and multiple SKUs (APM, logs, profiling) are turned on. Official billing pages and community threads repeatedly flag this complexity, prompting buyers to seek cost-predictable options.

Sampling strategy: fidelity vs. spend

Head-based sampling can drop the very traces you need (rare errors, P99 latency). Teams want tail-based or policy-driven sampling so they can keep “interesting” traces without storing everything. OpenTelemetry documents why tail sampling decides after seeing the whole trace, letting you keep slow/error traces while staying within budget.

Instrumentation & context propagation pitfalls

Good tracing provides context for services, queues, and async hops. Teams struggle when libraries/frameworks don’t auto-propagate or when polyglot stacks (Node.js, Java, Go, .NET) need a different setup. OpenTelemetry stresses context propagation fundamentals; missing it yields broken waterfalls and gaps in dependency graphs. 

Kubernetes & serverless blind spots

Ephemeral compute makes tracing fragile: pods churn, autoscaling changes topology, and cold starts reset contexts. Buyers report spans that “fall on the floor” when workloads recycle quickly unless the vendor has first-class K8s/Lambda patterns. Recent guidance highlights ephemeral challenges and the value of OTel-native integrations in these environments. (AWS Documentation)

Steep UIs and noisy waterfalls

Even great platforms can overwhelm newcomers with dense span lists, nested views, and weak defaults for correlation. G2 reviews frequently mention learning curve and UI complexity, which slows adoption and incident response unless the tool surfaces “what changed” and “where time was spent” right away. (G2)

Proprietary agents & migration lock-in

Teams burned by proprietary collectors/agents now insist on OpenTelemetry-first pipelines to avoid re-instrumenting during a vendor change. Even AWS urges customers to adopt OTel moving forward, reinforcing the industry shift toward vendor-neutral standards for tracing data. 

Weak logs ↔ traces ↔ metrics correlation

Traces alone rarely close incidents. Engineers want one-click pivots between a slow span and its related logs/metrics. Vendors document how to inject trace.id/span.id into logs, but if correlation is clunky—or requires separate products—teams look elsewhere for unified triage. 

Support responsiveness during outages

Tracing is most valuable during P1 incidents—precisely when slow support hurts the most. Public reviews note that response times can vary by plan or be delayed, pushing teams toward vendors with faster, engineer-level help and clearer escalation paths. 

Ecosystem & Integration Needs

Some teams want tracing tightly integrated with their existing stack (e.g., Datadog if already using their metrics/logs, or Elastic if on ELK). Others want tools that work standalone or with Grafana/Loki/Prometheus.

Top 8 Distributed Tracing Tools 

1. CubeAPM

Overview

CubeAPM is a modern OpenTelemetry-native distributed tracing platform designed for organizations that want full-stack observability without unpredictable costs. It combines distributed tracing, logs, metrics, events, and profiling into a single system. What sets CubeAPM apart is its cost transparency—instead of charging per host, per user, or for retention, it simply bills for the volume of data ingested, making it one of the most predictable tracing tools in the market.

Key Advantage

The biggest advantage of CubeAPM is cost-predictable distributed tracing at scale, powered by smart sampling. Unlike head-based sampling used in most tools, CubeAPM applies context-aware sampling—keeping slow or error-heavy traces while discarding low-value ones. It ensures engineers get high-fidelity data for debugging without drowning in span noise or facing bill spikes. Combined with its flat-rate pricing model, CubeAPM enables teams to scale distributed tracing confidently without financial surprises.

Key Features

• OpenTelemetry-first instrumentation: Supports all major OTEL SDKs and offers easy setup for languages like Node.js (Express, NestJS), Java (Spring Boot), Go, Python, and .NET.
• Service maps & trace waterfalls: Auto-detects dependencies between microservices, databases, APIs, and third-party calls, giving a complete picture of request flows.
• Smart sampling engine: Retains traces tied to latency, high error rates, or anomalies, ensuring meaningful visibility while reducing storage overhead.
• Full MELT observability: Correlates distributed traces with logs, metrics, events, synthetic monitoring, and RUM—speeding up root cause analysis.
• Self-host or SaaS deployment: Organizations with compliance needs (HIPAA, GDPR) can host CubeAPM in their own cloud or data center.
• 800+ integrations: Works out-of-the-box with Kubernetes, AWS, GCP, Azure, and popular frameworks, databases, and messaging systems.
• Low-latency architecture: Stores data inside your cloud (if self-hosted), reducing transfer costs and improving query speeds.
• End-to-End Visibility: Trace requests across frontend, backend, APIs, and databases.
• Root Cause Fast: Pinpoint latency sources and failing services instantly.
• Granular Insights: Drill into spans, errors, and code-level details.
• Logs & Metrics Correlation: Jump from traces to logs and infra in one click.

Pros

• Transparent pricing with no host, egress charges, retention, or user-based fees
• 800+ prebuilt integrations, making it nearly as plug-and-play as Datadog
• Full-stack coverage (distributed tracing + MELT)
• Flexible deployment: SaaS for ease, or on-prem for compliance
• Direct engineer-level support with rapid TAT

Cons

• No cloud security management features, as CubeAPM is observability-focused 
• Businesses looking for SaaS only solutions may not find it suitable 

Pricing at Scale

CubeAPM charges a flat $0.15/GB of ingested data. For a mid-sized SaaS company ingesting 10 TB of distributed tracing data/month:

• 10,000 GB × $0.15 = $1,500/month total.

There are no extra charges for hosts, users, or retention. 

Tech Fit

CubeAPM fits organizations running polyglot microservices with Java, Go, Python, Node.js, and .NET, as well as Kubernetes-native and serverless environments. It is ideal for SaaS providers and compliance-driven industries that need predictable costs and the ability to self-host, while DevOps teams benefit from its 800+ integrations across CI/CD, cloud infrastructure, databases, and messaging queues.

2. Dynatrace

Overview

Dynatrace provides the PurePath® technology to provide end-to-end distributed tracing. It also offers auto-instrumentation for code-level spans and connects them to topology (Smartscape), logs, metrics, and user experience. It’s designed for enterprises that want deep, automated visibility across cloud-native and hybrid stacks, with optional OpenTelemetry ingest when you don’t want to rely solely on agents.

Key Advantage

High-fidelity PurePath® tracing plus Davis® AI context—Dynatrace automatically captures traces with code-level detail, correlates them to dependencies and user impact, and applies causal-AI to surface the probable root cause, which helps large teams triage faster in complex microservice environments.

Key Features

• PurePath® distributed tracing: Automatic, code-level spans across services, external calls, and databases with end-to-end stitching.
• Smartscape topology & service maps: Continuous dependency discovery and relationship mapping for microservices and infra.
• OpenTelemetry ingest & analytics: Bring OTel traces into Dynatrace and analyze alongside other signals.
• Davis® AI assistance: Causal-AI highlights anomalies and likely root causes across traces, metrics, and logs.
• Kubernetes & serverless coverage: Automated visibility for pods, containers, and functions with one-agent collection.

Pros

• PurePath® gives rich, code-level traces at enterprise scale
• Strong automation with Davis® AI and Smartscape correlation
• First-class Kubernetes coverage and broad cloud support
• Supports OpenTelemetry ingest for vendor-neutral pipelines

Cons

• Pricing model mixes host memory hours with per-GiB trace usage
• Total cost can rise quickly at high volumes or larger fleets
• UI depth and platform breadth can feel heavy for smaller teams

Pricing at Scale

Dynatrace lists distributed tracing at $0.20 per GiB ingested, plus retention at $0.0007 per GiB-day and query at $0.0035 per GiB-scanned. For a mid-sized company ingesting 10 TB/month (~10,000 GiB) of tracing data, ingest alone would cost about $2,000/month; 10 days of additional retention would add around $70/month. If Full-Stack Monitoring is used to capture traces, add $0.01 per memory-GiB-hour (around $0.08/hour for an 8 GiB host), which significantly raises costs depending on fleet size. 

In comparison, CubeAPM charges a flat $0.15/GB, so the same 10 TB costs about $1,500/month with no host or retention fees—making CubeAPM a much more affordable option at this scale.

Tech Fit

Dynatrace suits enterprises running large, polyglot microservices on Kubernetes and hybrid clouds that want automatic code-level tracing, topology mapping, and AI-assisted triage. It is best for organizations that prefer agent-driven capture but still want OpenTelemetry ingest flexibility, and value deep correlation and automation enough to justify a more complex, consumption-based cost model.

3. Datadog

Overview

Datadog is a comprehensive end-to-end distributed tracing platform that tightly integrates APM with infrastructure, logs, metrics, synthetic monitoring, and RUM. It delivers automatic instrumentation and service maps across languages and infrastructure components, designed for teams that want to unify observability at scale—but its modular, multi-dimensional pricing can quickly become complex and costly.

Key Advantage

Datadog shines in delivering rich context along the full request path, connecting frontend performance (RUM or synthetic) to backend traces and infrastructure metrics. Its unified dashboard surfaces latency, errors, and performance hotspots in one place, making it ideal for large-scale, cloud-native environments.

Key Features

• End-to-end tracing: From browser and synthetic tests through backend services and infrastructure.
• Ingestion control and sampling settings: Allows you to tune trace volume by service to manage costs.
• Automatic service maps and dependency graphs: Visualize service interactions instantly.
• Out-of-the-box instrumentation: for Node.js, Lambda, Kubernetes, RUM, Synthetic, and more.
• Full-stack integration: Seamless correlation of traces with logs, metrics, synthetic tests, RUM, and security workflows.

Pros

• Rich ecosystem with over 850 integrations
• End-to-end tracing correlated with logs, metrics, and synthetic monitoring
• Ingestion control mechanisms help manage trace volume and costs
• Strong UI tooling with real-time dashboards and visual trace analysis

Cons

• Pricing is very complex, based on hosts, spans ingested, indexed spans, and feature modules
• Cost can skyrocket as trace volume and hosting needs grow
• Onboarding can be overwhelming due to the density of features

Pricing at Scale

Datadog costs are calculated per host plus extra for span ingestion and indexing. APM pricing starts at $31/month per host (includes 1M indexed spans and 150 GB ingested spans per host). Extra spans incur $0.10 per GB ingested and $1.70 per million indexed spans beyond allowances.

For a mid-sized company ingesting 10 TB (10,000 GB) of tracing data per month, assuming they already provision enough hosts to cover baseline spans, the cost of span ingestion alone would be:

10,000 GB × $0.10 = $1,000 in ingested span fees

But this excludes host charges and indexed span overages, which can add several thousand dollars. Real-world estimates often point to $8,000–$12,000/month for comparable distributed tracing setups. CubeAPM’s $1,500/month pricing for the same amount of telemetry data is more cost-efficient. 

Tech Fit

Datadog is well-suited for medium to large organizations with complex, polyglot environments—especially Kubernetes-heavy stacks, serverless or microservices architectures, and teams already using its logs and synthetic monitoring suite. It works across Node.js, Python, Java, Go, .NET, Lambda, and frontend (RUM), making it a powerful unified observability option as long as you can manage the cost and complexity.

4. New Relic

Overview

New Relic is a full-stack observability platform with end-to-end distributed tracing that ties spans to errors, logs, metrics, profiles, and front-end telemetry in one UI. Tracing is central to New Relic’s APM experience and includes dedicated views for trace lists, service maps, and per-span analysis to quickly isolate latency and error propagation across microservices.

Key Advantage

New Relic’s advantage is deep, guided troubleshooting: you can start from a user-facing symptom (RUM or Synthetics), jump into a distributed trace, and follow spans through services, external calls, and databases while pivoting to correlated logs and metrics—minimizing context switching during incidents.

Key Features

• End-to-end distributed tracing: Trace requests across services, queues, and external dependencies; drill into spans and attributes.
• Trace UI and service maps: Filter, group, and compare traces; visualize dependencies to spot hotspots fast.
• Flexible sampling options: Works with New Relic agents, OpenTelemetry pipelines, or Infinite Tracing models.
• OpenTelemetry ingest: Ingest OTel traces alongside other telemetry and keep them correlated in one place.
• Guided workflows: Purpose-built views to investigate errors, slow transactions, and cross-account traces.

Pros

• Mature distributed tracing with strong UI for span-level analysis and service maps
• 780+ integrations and broad language coverage for polyglot microservices
• Tight correlation across traces, logs, metrics, profiles, RUM, and synthetics
• Usage-based model with clear per-GB data pricing and seat types published publicly

Cons

• Data-ingest plus user seats can add up at higher scales compared to flat-rate models
• Data Plus (extended retention/governance) costs more per GB than the original data option
• Advanced features and rich UI can feel dense for smaller teams

Pricing at Scale

New Relic bills tracing data as part of data ingest: 100 GB/month free, then $0.40/GB (Original Data) or $0.60/GB (Data Plus). For a mid-sized company ingesting 10 TB/month (10,000 GB) of distributed tracing data, excluding user seats and optional add-ons:

• Original Data: (10,000 − 100) GB × $0.40 = $3,960/month
• Data Plus: (10,000 − 100) GB × $0.60 = $5,940/month

EU storage adds $0.05/GB if selected. Seat costs (e.g., full-platform users at $349/user/month on Pro, $418.80/month pay-as-you-go) are additional. CubeAPM is more affordable at just $1500/month for the same data ingestion volume. 

Tech Fit

New Relic fits organizations running polyglot microservices on Kubernetes and serverless that want tracing tightly integrated with the rest of their telemetry. It works well across Java, .NET, Node.js, Python, Go, and browser/mobile, supports OpenTelemetry ingest, and suits teams that value out-of-the-box workflows and a rich UI for trace-first debugging while accepting a per-GB + seat pricing model.

5. SigNoz

Overview

SigNoz is an open-source, OpenTelemetry-first distributed tracing platform that unifies traces, logs, and metrics in a single UI. It’s available as self-hosted (Community/Enterprise) or managed cloud, giving teams flexibility on data control and deployment while keeping the developer experience approachable with flamegraphs, Gantt views, and opinionated trace workflows.

Key Advantage

OTEL-native tracing with the freedom to self-host or use a managed cloud, so teams can standardize instrumentation, avoid vendor lock-in, and choose the right balance between operational control and convenience

Key Features

• End-to-end distributed tracing: Follow requests across services, queues, and databases with span detail, timing, and attributes.
• Trace → metrics → logs correlation: Jump between signals in a few clicks to speed up root-cause analysis.
• Flamegraphs & Gantt views: Visualize hot paths, parallelism, and blocking spans for faster performance tuning.
• OpenTelemetry ingest: Use native OTel SDKs/collectors and bring your existing pipelines without re-instrumentation.
• Flexible deployment: Choose Cloud, Self-Hosted Enterprise with support, or Community Edition to manage yourself.

Pros

• Open-source with strong OpenTelemetry alignment
• Self-host option for data residency and governance
• Clean trace UI with flamegraphs and span exploration
• Correlated traces, logs, and metrics in one place

Cons

• Cloud pricing is usage-based and can rise with high trace volumes
• Smaller third-party ecosystem than large incumbents
• Self-hosting adds operational overhead for scale and upgrades

Pricing at Scale

SigNoz Cloud bills logs/traces as per usage; around $0.30 per GB for ingested traces/logs, plus you may have to pay extra for any seat or advanced retention add-ons. For a mid-sized company ingesting 10 TB/month (10,000 GB) of tracing data, the Cloud bill for ingestion alone is roughly $3,000/month. 

By comparison, CubeAPM charges $0.15/GB, so the same 10 TB is ≈ $1,500/month with no extra host or retention fees—making CubeAPM the more affordable option at this scale.

Tech Fit

SigNoz fits engineering teams standardizing on OpenTelemetry that want developer-friendly distributed tracing with the option to self-host for compliance or run a managed cloud for simplicity; it works well across polyglot microservices on Kubernetes and popular runtimes like Java, Go, Python, Node.js, and .NET, and suits organizations that value open-source flexibility with straightforward trace-first debugging workflows.

6. Sentry 

Overview

Sentry pairs error monitoring with end-to-end distributed tracing, letting teams follow a request from the first user action through backend services and databases with a trace waterfall, span details, and service maps. Tracing is first-class in Sentry’s APM experience, with trace/transaction views, span metrics, and opinionated workflows that help developers move from symptom to slow span quickly.

Key Advantage

Developer-centric tracing that’s tightly integrated with error monitoring and frontend telemetry — you can start from a user-facing issue, jump into the trace, inspect slow spans and DB queries, and pivot to related errors and metrics without leaving the UI.

Key Features

• End-to-end distributed tracing: Follow user interactions through services, external calls, queues, and databases; drill into span attributes and timings.
• Trace view and span explorer: Filter, group, and compare traces; search spans by attributes to isolate hotspots.
• Span metrics and performance alerts: Turn spans into time series for latency and throughput; alert on percentiles or regressions.
• SDKs plus OpenTelemetry ingest: Keep existing instrumentation by using Sentry’s SDKs or by bridging OTel signals. 
• Frontend ↔ backend correlation: Connect RUM/JS transactions with backend traces for full request context.

Pros

• Intuitive UI for developers and product teams
• Strong frontend-to-backend correlation and error overlap
• Lightweight setup with popular SDKs and frameworks
• Span metrics and opinionated workflows speed root-cause

Cons

• Event/span-based billing can be hard to forecast at scale
• Deep infrastructure views are lighter than full platform suites
• Mapping GB of data to spans requires assumptions

Pricing at Scale

Sentry bills tracing by spans (beyond included quotas). Published rates show tiered per-span prices (team/business, reserved/PAYG). Because pricing is per span — not per GB — mapping a “10 TB per month” scenario depends on the average bytes per span in your organization. As a practical illustration:

• If your average stored payload is ~2 KB per span, 10 TB ≈ 5.0 billion spans; at ~$0.0000014 per span (high-volume reserved), that’s about $7,000/month for spans alone (excluding seats and other products).
• If it’s closer to 1 KB per span, 10 TB ≈ 10 billion spans → about $14,000/month in span charges.

By contrast, CubeAPM prices its tracing at $0.15/GB, so 10 TB (10,000 GB) ≈ $1,500/month with no extra host or retention fees — meaning CubeAPM is typically more affordable at this scale.

Tech Fit

Sentry fits teams that want developer-friendly, trace-first debugging across JavaScript/TypeScript frontends and popular backends (Node.js, Python, Java, .NET, Go), need tight linkage between errors and slow spans, and prefer event/span-based control over what they send; it’s especially strong for product-led organizations that start from user-visible performance symptoms and then drill into distributed traces.

7. Elastic APM

Overview

Elastic APM brings end-to-end distributed tracing into the Elastic Observability suite so you can follow a request across services, queues, and databases while pivoting directly into logs and metrics. You can see waterfalls, impact-based sorting, service relationships, etc. via the UI. Also, Elastic agents use the W3C Trace Context to stitch spans all the way. Elastic also supports native OpenTelemetry (OTLP) ingest, so you can send traces from the OTel Collector straight into Elastic without translation.

Key Advantage

Powerful log ↔ trace correlation with search-first workflows—you can chase a slow span into raw logs, query with KQL/EQL, and come back to the trace timeline, all in one place. That combination of tracing plus Elastic’s search and visualization makes it easy to move from symptoms to the exact source of latency or errors.

Key Features

• End-to-end distributed tracing: Visualize full request journeys with spans and transactions tied together via W3C Trace Context.
• Trace UI & explorer: Waterfall timelines, impact-based sorting, and an experimental Trace Explorer with KQL/EQL queries.
• OpenTelemetry ingest: APM Server natively accepts OTLP (HTTP/gRPC), so your OTel Collector can ship traces directly.
• RUM and backend agents: Browser (RUM) and Node.js agents support distributed tracing across front-end and back-end hops.
• Serverless pricing option: Ingest- and retention-based pricing with optional egress and support tiers.

Pros

• Strong log–trace correlation with Kibana search
• Native OTLP ingest and W3C Trace Context support
• Flexible deployment: serverless, hosted, or self-managed
• Powerful KQL/EQL analytics across spans and transactions

Cons

• Hosted/self-managed clusters add operational overhead at scale
• The pricing for serverless hosting requires you to pay extra for retention and support 
• Cost governance can get tricky if you keep long retention or move lots of data out

Pricing at Scale

Elastic Observability Serverless (Complete) lists ingest “as low as” $0.150/GB, retention “as low as” $0.020/GB-month, and egress at $0.05/GB after 50 GB; support is an additional 5–15% based on subscription tier. For 10 TB/month (10,000 GB) of tracing data, ingest is about $1,500/month, plus around $200/month if you retain the full 10 TB for one month, plus any egress and support percentage. 

By comparison, CubeAPM is $0.15/GB with no extra host, retention, or support fees, so 10 TB ≈ $1,500/month flat—typically more affordable once Elastic’s retention and support add-ons are included.

Tech Fit

Elastic APM fits teams that already run ELK or want a search-first approach to troubleshooting—polyglot microservices on Kubernetes, front-end apps instrumented with RUM, and services emitting traces via OpenTelemetry. It’s a strong choice when deep log analytics and trace correlation live at the center of your workflow, and you’re comfortable with ingest + retention cost controls or operating Elastic in hosted/self-managed modes.

8. Honeycomb

Overview

Honeycomb is an observability platform built around event-based distributed tracing, letting teams explore high-cardinality data interactively and follow a request across services with a fast trace UI, BubbleUp anomaly detection, and service maps. It’s popular with teams that want to ask ad-hoc questions of tracing data, slice by any field, and uncover patterns that metric dashboards often miss.

Key Advantage

Exploratory, query-anything tracing that treats every span field as a first-class dimension—engineers can pivot instantly from a slow trace to the exact attributes, users, regions, or endpoints involved, then visualize outliers with BubbleUp for rapid, data-driven debugging.

Key Features

• Distributed tracing with interactive exploration: Trace waterfalls, span inspection, and filters on any field to pinpoint bottlenecks.
• High-cardinality analytics: You can query each span field and build ad-hoc breakdowns, heatmaps, etc., without aggregating data beforehand. 
• Tail-based sampling with Refinery: Keep the most valuable traces (errors, high latency) while controlling storage and costs.
• Service map and SLOs: Visualize service relationships and track user-impacting objectives alongside traces.
• OpenTelemetry support: Send OTLP directly, reuse your existing OTel collectors and instrumentation.

Pros

• Exceptional query-first UX for debugging distributed systems
• Powerful high-cardinality analysis and BubbleUp outlier detection
• Tail-based sampling option (Refinery) to keep “interesting” traces
• Solid OpenTelemetry support and modern developer ergonomics

Cons

• Event-based pricing can be hard to translate from raw GB of traces
• Included Pro volumes cap out at 1.5B events/month before Enterprise tiers
• Requires a careful sampling design to control costs at very high traffic

Pricing at Scale

Honeycomb Pro starts at $130/month and includes fixed event volume tiers (e.g., 100M, 450M, or 1.5B events/month) with 60-day retention; higher volumes move into Enterprise. Mapping 10 TB/month of tracing data depends on your average event/span size: at ~1 KB per event, 10 TB is roughly 10 billion events, which far exceeds Pro’s 1.5B tier and would require Enterprise-level volumes; at ~2 KB per event, you’re still around 5 billion events. 

By contrast, CubeAPM charges $0.15/GB with no host or retention add-ons, so 10 TB ≈ $1,500/month, which is typically more affordable at this scale.

Tech Fit

Honeycomb fits teams that want a tracing-first, exploratory workflow for complex microservices, need to ask open-ended questions of high-cardinality span data, and are comfortable designing tail-based sampling policies; it works well across polyglot stacks with OpenTelemetry, Kubernetes, and modern cloud services, especially for engineering orgs that prioritize fast, iterative investigation over rigid, prebuilt dashboards.

How to Choose the Right Distributed Tracing Tool

1. Budget & Pricing Model

Pricing for tracing can vary wildly—some vendors charge per host, per span, or per GB ingested, while others add extra fees for retention and users. This makes forecasting costs a major challenge, especially at scale. If you’re a startup or SaaS provider, a flat per-GB model like CubeAPM’s is easier to manage, while enterprise tools like Datadog or Dynatrace may balloon in cost once ingestion grows. Always model a scenario (e.g., 10 TB/month) before committing.

2. Telemetry Standards (OpenTelemetry Support) 

OpenTelemetry has become important for distributed tracing. Tools that natively ingest OTEL data help you avoid vendor lock-in and allow reuse of the same pipelines across multiple backends. When choosing a tool, ensure it doesn’t require proprietary agents only and that it supports context propagation standards like W3C Trace Context. This ensures long-term flexibility and easy migration if your needs change.

3. Deployment Model

Some teams are comfortable with SaaS observability, while others—particularly those in healthcare, finance, or government—require self-hosted or hybrid deployments for compliance. SaaS-first tools like Datadog may not fit regulated industries, while CubeAPM, Elastic APM, and SigNoz offer on-prem or cloud-hosted options. The right choice depends on whether data residency, latency, or vendor-managed convenience is your top priority.

4. Ecosystem Fit & Integrations

Distributed tracing only delivers value if it works with your full stack. Check for integration coverage across languages, frameworks, messaging systems, and cloud services. Tools like Datadog and CubeAPM offer 800+ integrations, while open-source platforms rely on OTEL compatibility for breadth. The closer a tool fits your environment (e.g., Kubernetes, AWS Lambda, or JVM-heavy apps), the faster adoption and value will be.

5. Compliance & Security

Data privacy regulations like GDPR, HIPAA, and PCI-DSS mean your tracing tool must provide strong access control, encryption, and clear data storage policies. SaaS platforms often store data globally, which may not meet residency requirements. If compliance is critical, consider vendors offering self-hosted deployments or region-specific storage. Always review how retention, deletion, and data usage policies align with your obligations.

6. Ease of Onboarding & Usability

Even the most powerful tracing system won’t help if your team struggles to adopt it. Look for tools with automatic instrumentation, clean dashboards, and intuitive workflows. Developer-first solutions like Sentry and CubeAPM emphasize quick setup, while enterprise suites like Dynatrace and Datadog can be overwhelming initially. Usability directly impacts incident response speed and the tool’s overall ROI.

Conclusion

Choosing the right distributed tracing tool is rarely simple. Teams struggle with unpredictable costs, steep learning curves, and limited flexibility in handling cloud-native and hybrid environments. Many tools lock customers into rigid pricing or force them into vendor-specific agents, leaving little room for customization.

This is where CubeAPM stands out as the best distributed tracing tool provider. With full OpenTelemetry support, seamless MELT observability, smart sampling, and 800+ integrations, CubeAPM delivers enterprise-grade tracing without the enterprise-level pricing. Its flat $0.15/GB model ensures complete cost predictability.

Whether you’re a fast-scaling SaaS, a Kubernetes-heavy team, or a compliance-driven enterprise, CubeAPM gives you the flexibility, affordability, and reliability you need. Start modernizing your observability stack today with CubeAPM.