DEV Community: logan zhang

Top 5 Open Source APM Tools in 2026

logan zhang — Sun, 05 Jul 2026 13:55:06 +0000

Choosing among open source APM tools in 2026 means matching architecture to your OpenTelemetry strategy, on-call workflow, and ops capacity â€” not copying a vendor-sponsored ranking. This guide compares Jaeger, Apache SkyWalking, DataBuff, Grafana Tempo, and Pinpoint with a structured evaluation framework, UI screenshots for each tool, and practical selection guidance.

Open source APM gives you inspectable code, self-hosted telemetry, and instrumentation you can carry between backends. The five tools below are the shortlist platform engineers actually deploy when searching for opensource apm â€” evaluated with original criteria, not recycled marketing copy.

Open source APM tools offer transparency, customization, and cost predictability at scale. Several factors drive careful evaluation:

Cost optimization â€” eliminate per-host SaaS tiers while keeping enterprise-grade tracing
Data sovereignty â€” keep telemetry on-premises or in specific regions
Vendor neutrality â€” instrument once with OpenTelemetry; swap backends without rewrites
AI and agent workflows â€” LLMs and IDE agents need query access to live spans
Operational fit â€” small teams prefer fewer stateful services; large orgs may compose LGTM

Why Teams Are Choosing Open Source APM Tools

Zero licensing fees â€” costs shift to infrastructure, not per-node commercial tiers
Complete data control â€” self-hosting supports GDPR, HIPAA, and internal audits
No vendor lock-in â€” OpenTelemetry-native backends enable backend migration
Transparency and security â€” audit source and verify ingest paths
Community innovation â€” CNCF and ASF projects evolve with cloud-native adoption

What to Look for in an Open Source APM Tool

When evaluating open source apm tools, assess these dimensions:

Unified observability â€” traces + metrics (+ logs) in one UI or composable stack; test with one slow request end-to-end
OpenTelemetry support â€” native OTLP ingest; point SDK at 4317/4318 and verify span fields
Storage efficiency â€” columnar, object storage, or search backends; benchmark retention at your cardinality
Query performance â€” trace search, TraceQL, or topology under load during peak windows
Deployment simplicity â€” Docker/Kubernetes paths and component count; time install â†’ first topology view
AI / MCP readiness â€” grounded AI on telemetry; Skill/MCP extensibility for IDE agents
Agent model â€” SDK-only vs bytecode agents vs eBPF; match Java-heavy vs polyglot estates
Community health â€” release cadence, docs quality, issue response over recent quarters

Top 5 Open Source APM Tools: Comparison & Use Cases

1. Jaeger

Jaeger is a CNCF-graduated distributed tracing platform originally developed at Uber. Jaeger v2 aligns with OpenTelemetry through a Collector-based architecture while preserving deep trace search workflows.

Jaeger UI Â· Trace search and service filtering

Jaeger Pros:

CNCF graduated â€” production-ready governance and long-term support
Battle-tested at scale â€” adaptive sampling; flexible storage (Elasticsearch, Cassandra, Kafka)
OpenTelemetry compatible â€” native OTLP on standard gRPC and HTTP ports
Service dependency graphs â€” automatic topology from span relationships
Mature trace exploration â€” waterfall and compare views for latency investigations

Jaeger Cons:

Tracing-centric â€” metrics and logs require companion tools
Production deployments often split collector, query, and storage roles
RED dashboards limited vs unified APM platforms

Integration / Mitigation:

Pair with Prometheus and Grafana for metrics and visualization
OpenTelemetry Collector enables dual-export during migrations
Jaeger Operator automates Kubernetes deployment

Best For: Teams focused on distributed tracing who already operate metrics/logging elsewhere.

2. Apache SkyWalking

Apache SkyWalking is a full-stack APM platform for microservices and cloud-native architectures â€” automatic agents, topology, metrics, and traces, plus OTLP receivers for OpenTelemetry migration.

Apache SkyWalking Â· Default observability dashboard

Apache SkyWalking Pros:

Full-stack APM â€” traces, metrics, logs, and service mesh observability
Automatic instrumentation â€” Java, .NET, Node.js, Python; bytecode-level JVM visibility
Service topology â€” automatic dependency mapping
Custom dashboards â€” layer- and entity-based customization
Apache Software Foundation governance â€” long enterprise track record

Apache SkyWalking Cons:

Agent-first heritage â€” OTLP-only shops may parallel-run agents during migration
Full deployment heavier than compact three-component backends
Smaller English-language community vs Grafana/Prometheus ecosystem

Integration / Mitigation:

OTLP receivers for hybrid OpenTelemetry instrumentation
Kubernetes operator and Helm charts for rollout
Horizon UI modernizes the operator experience on the same OAP backend

Best For: Java-heavy microservices wanting automatic instrumentation and APM dashboards without assembling LGTM.

3. DataBuff

DataBuff is an open source, AI-native OpenTelemetry APM backend â€” unified ingest, troubleshooting, and agent-era extensibility in a compact self-hosted footprint. Listed on OpenTelemetry Vendors as Pure OSS with Native OTLP Yes.

DataBuff Â· Service health overview (Rate, Errors, Duration)

DataBuff Pros:

OpenTelemetry-native â€” OTLP on gRPC 4317 and HTTP 4318; no proprietary agent lock-in
AI-native architecture â€” AI Brain orchestrates digital experts that query real metrics, traces, and alerts (not a bolt-on chat box)
Skill and MCP extensibility â€” override built-in skills; MCP both ways (Cursor/Claude call platform; platform registers external MCPs for Prometheus, SkyWalking, Zabbix)
Agent-era observability â€” LLM call chains, token usage, tool/skill invocations alongside RED metrics
Three-component stack â€” Ingest â†’ columnar store â†’ Web UI on port 27403
Bring-your-own-model â€” OpenAI-compatible and Anthropic APIs

DataBuff Cons:

Younger community mindshare than Jaeger or SkyWalking
Full eBPF zero-instrumentation on public roadmap â€” plan SDK/Collector for brownfield today
AI features require configuring a model endpoint first

Integration / Mitigation:

One-line install for Docker POC; optional demo workload generator
Dual-export via OpenTelemetry Collector from Jaeger or agent-based APM
Register external MCP tools to query legacy data from the AI console

Best For: OpenTelemetry-standard teams wanting self-hosted unified APM with AI-native triage and Skill/MCP extensibility â€” without five separate observability services.

curl -fsSL https://databuff.ai/databuff/ai-apm-install.sh | bash
# OTel SDK â†’ gRPC YOUR_HOST:4317 or HTTP http://YOUR_HOST:4318/v1/traces

4. Grafana Tempo

Grafana Tempo is an open source trace backend for object-storage economics, deeply integrated with Grafana, Loki, and Prometheus/Mimir â€” the trace pillar in the LGTM modular stack.

Grafana + Tempo Â· TraceQL search results in Explore

Grafana Tempo Pros:

Object-storage-friendly â€” cost-aware long-term trace retention
Native OpenTelemetry ingestion â€” OTLP recommended for new deployments
TraceQL â€” traces-first query language with visual Search builder in Explore
Signal linking â€” trace-to-log and trace-to-metrics with Loki and Prometheus
Traces Drilldown UI â€” queryless trace analysis for point-and-click workflows

Grafana Tempo Cons:

Requires assembling Grafana, Tempo, and often Prometheus/Loki for full-stack observability
APM semantics depend on how components are wired
Not a drop-in unified platform like SkyWalking or DataBuff

Integration / Mitigation:

Grafana Cloud for managed deployment
Helm charts and operators for Kubernetes
Adopt incrementally â€” Tempo first, add Loki/Mimir as maturity grows

Best For: Grafana-invested teams wanting object-storage trace retention and TraceQL/Drilldown in Explore.

5. Pinpoint

Pinpoint is open source APM for large-scale distributed Java applications â€” bytecode-level method tracing, server maps, and transaction analysis without modifying source code.

Pinpoint Â· Server map and service dependency topology

Pinpoint Pros:

Bytecode instrumentation â€” method call trees, SQL timings, external API latency
Server map â€” automatic topology for large microservice graphs
Low agent overhead â€” ~3% resource impact in community benchmarks
Transaction tracing â€” expandable call stacks for slow DB and remote calls
Scale-oriented storage â€” HBase-backed high-volume ingestion

Pinpoint Cons:

Primarily Java and PHP â€” polyglot services need complementary OTel backend
HBase operations add complexity vs lighter columnar stores
Not OpenTelemetry-native â€” parallel-run during OTLP migration
UI patterns reflect an earlier APM generation

Integration / Mitigation:

Docker quickstart before full HBase production topology
Pair with OTLP backend for non-Java services
MCP bridges can query Pinpoint-exposed APIs during hybrid migrations

Best For: Large Java estates needing bytecode-level APM depth and HBase-scale trace storage.

Comparison Summary: Top 5 Open Source APM Tools

Jaeger â€” Metrics: âŒ Â· Traces: âœ… Â· Unified UI: trace-focused Â· Native OTLP: âœ… Â· AI/MCP: âŒ Â· Best for: dedicated CNCF trace backend
SkyWalking â€” Metrics: âœ… Â· Traces: âœ… Â· Unified UI: âœ… Â· Native OTLP: âœ… receiver Â· AI/MCP: âŒ Â· Best for: agent-rich Java microservices
DataBuff â€” Metrics: âœ… Â· Traces: âœ… Â· Unified UI: âœ… Â· Native OTLP: âœ… native Â· AI/MCP: âœ… Skills + MCP Â· Best for: OTel + AI-native unified APM
Grafana Tempo â€” Metrics: âš ï¸ via Grafana Â· Traces: âœ… Â· Unified UI: âš ï¸ composable Â· Native OTLP: âœ… Â· AI/MCP: âŒ Â· Best for: trace store in LGTM stack
Pinpoint â€” Metrics: âœ… Â· Traces: âœ… Â· Unified UI: âœ… Â· Native OTLP: âŒ Â· AI/MCP: âŒ Â· Best for: deep Java bytecode APM

Conclusion

Jaeger and Grafana Tempo remain excellent trace specialists. SkyWalking and Pinpoint serve agent-driven Java estates. Among unified backends, DataBuff stands out for native OTLP ingest, AI-native investigation on live telemetry, and Skill/MCP extensibility â€” without operating five separate observability services.

Run the same afternoon POC for every finalist: deploy, point OTLP at 4317/4318, generate traffic, and confirm the UI views that matter â€” service list, topology, or TraceQL results â€” before committing production retention.

FAQs

What is the best open source APM tool in 2026?
No universal winner â€” match architecture to your stack. DataBuff fits OTel-native teams wanting AI/MCP; Jaeger/Tempo fit trace specialists; SkyWalking/Pinpoint fit agent-heavy Java.

Are open source APM tools production-ready?
Yes for mature projects (Jaeger, SkyWalking, Pinpoint). Validate newer unified backends with your cardinality and AI workflows in a POC.

How does DataBuff compare to SkyWalking and Pinpoint?
SkyWalking/Pinpoint excel with bytecode agents in JVM estates. DataBuff prioritizes native OTLP, a smaller stack, and AI-native Skill/MCP workflows.

Do I need Tempo if I already use Jaeger?
Usually not for tracing alone. Tempo matters for Grafana LGTM and object-storage retention.

References

Top 10 Open Source APM Tools in 2026

logan zhang — Sun, 05 Jul 2026 13:22:26 +0000

Choosing among open source APM tools in 2026 starts with one question: are you picking instrumentation, a trace backend, a composable stack, or a unified platform? This guide walks through ten names teams actually encounter when searching opensource apm â€” from OpenTelemetry and Jaeger through Grafana LGTM, Elastic APM, SigNoz, ClickHouse-backed analytics, and OTel-native unified backends like DataBuff â€” with selection criteria you can apply without copying vendor ranking lists.

Application performance monitoring under an open source license still wins when teams need data ownership, portable instrumentation, and the freedom to self-host without procurement friction. The through-line across every entry below is OpenTelemetry: most modern backends ingest OTLP, and the real choice is whether a tool is OTel-native from the ground up or bolts OTLP onto an older data model.

Top open source APM tools

Here are ten projects and patterns that dominate open source apm tools shortlists in 2026. The list mixes standards, trace specialists, full-stack platforms, composable stacks, storage engines, and unified backends â€” because production teams rarely pick just one layer.

1. OpenTelemetry

OpenTelemetry is not an APM product â€” it is the CNCF-backed standard for generating and exporting telemetry. SDKs, auto-instrumentation libraries, and the OpenTelemetry Collector form the instrumentation layer that every backend below consumes.

OpenTelemetry Â· SDKs, Collector, and OTLP export to observability backends

OpenTelemetry provides vendor-neutral APIs for traces, metrics, and logs; semantic conventions for HTTP, database, and messaging spans; and a Collector pipeline for sampling, routing, and fan-out to multiple destinations.

Key strength: Industry-standard instrumentation that decouples how you observe code from where you store telemetry â€” the foundation every modern OSS APM stack builds on.

2. Jaeger

Jaeger is a distributed tracing system originally built at Uber and now a CNCF graduated project. Jaeger v2 re-architected around the OpenTelemetry Collector framework, aligning ingest with modern OTLP deployments while preserving deep trace search, adaptive sampling, and service dependency graphs.

Jaeger UPÂ· Trace search and service filtering

Jaeger focuses on the tracing pillar of APM. Teams typically pair Jaeger with Prometheus for metrics and Grafana for dashboards when they need full-stack observability outside a single product UI.

Key strength: Mature, CNCF-aligned distributed tracing with native OTLP ingestion and flexible pluggable storage.

3. DataBuff

DataBuff is an open source, AI-native APM platform built for the OpenTelemetry era. Listed on the OpenTelemetry Vendors page under Pure OSS with Native OTLP Yes, it ingests standard OTLP directly from applications and the Collector.

DataBuff Â· Service health overview (Rate, Errors, Duration)

Architecturally, DataBuff collapses observability sprawl into three runtime components: Ingest (OTLP gRPC 4317 and HTTP 4318), Apache Doris columnar storage, and a Web platform on port 27403 for service health, topology, alerting, and AI-assisted investigation.

curl -fsSL https://databuff.ai/databuff/ai-apm-install.sh | bash
# OTLP HTTP: http://YOUR_HOST:4318/v1/traces

Key strength: Compact, OpenTelemetry-native unified APM with columnar analytics storage, AI-native multi-agent triage, and MCP/skill extensibility for agent-era on-call workflows.

4. Apache SkyWalking

Apache SkyWalking is an open source APM system designed for microservices, cloud-native, and container-based architectures. It provides distributed tracing, service mesh telemetry analysis, metric aggregation, and topology visualization â€” with agents for Java, .NET, Node.js, Python, and other runtimes.

Apache SkyWalking Â· Default observability dashboard

SkyWalking predates the OpenTelemetry wave but now exposes OTLP receivers, letting teams migrate instrumentation gradually.

Key strength: Integrated APM for microservices and cloud-native environments with rich agent-based auto-instrumentation and topology maps.

5. Zipkin

Zipkin is one of the earliest open source distributed tracing systems. It collects timing data from services, stores spans, and provides a UI to look up traces and understand request paths and latencies.

Zipkin Lens Â· Distributed trace lookup

Zipkin's architecture is deliberately simple and focused. Like Jaeger, it handles tracing and relies on companion tools for metrics and logs.

Key strength: A mature, simple system dedicated to distributed tracing with a long track record in production microservice shops.

6. Grafana

Grafana is the visualization layer at the center of the LGTM stack â€” Loki for logs, Grafana for dashboards, Tempo for traces, and Mimir or Prometheus for metrics. Many teams treat this composable pattern as their open source observability platform.

Grafana + Tempo Â· TraceQL search in Explore

Trace-to-log and trace-to-metrics correlation work well when Loki, Tempo, and Prometheus are wired together. The trade-off is operational complexity: you operate and upgrade each component separately.

Key strength: Highly flexible dashboards and visualization that integrate with almost any telemetry backend â€” the de facto standard for composable OSS observability.

7. Elastic APM

Elastic APM is the application performance monitoring component of the Elastic Stack. It collects performance metrics, errors, and distributed traces, storing them in Elasticsearch and visualizing them in Kibana alongside log data.

Elastic APM Â· Service performance in Kibana

Teams already running Elasticsearch for logging often add Elastic APM for a unified search and analytics experience.

Key strength: Seamless APM integration for teams already standardized on the Elastic Stack for logging and search.

8. Pinpoint

Pinpoint is an open source APM tool inspired by Google Dapper, designed for large-scale distributed systems. Written primarily for Java and PHP, it uses bytecode instrumentation to trace requests without modifying application source code.

Pinpoint Â· Server map and service dependency topology

Pinpoint visualizes system topology and provides code-level visibility â€” expandable call trees showing which methods, SQL statements, and external APIs consumed latency inside a transaction.

Key strength: Deep code-level transaction tracing and topology maps for Java and PHP applications at scale.

9. SigNoz

SigNoz is a widely referenced open source observability platform that provides a unified backend for metrics, traces, and logs. It is built natively on OpenTelemetry and stores telemetry in a columnar analytics engine.

SigNoz Â· Application overview (RPS, latency percentiles, error rate)

SigNoz offers application overview dashboards, log management, infrastructure monitoring, service maps, and alerting in one UI.

Key strength: OpenTelemetry-native unified observability with columnar storage optimized for correlated metrics, traces, and logs.

10. ClickHouse

ClickHouse is not an APM UI â€” it is an open source columnar OLAP database that powers the storage layer behind several OpenTelemetry-native observability platforms. High-cardinality trace and metrics data benefits from columnar compression and fast analytical queries.

ClickHouse Â· SQL analytics on high-cardinality telemetry data

When teams mention "ClickHouse APM," they usually mean an observability backend that ingests OTLP into ClickHouse tables and exposes SQL or a product UI on top.

Key strength: High-performance columnar storage for telemetry analytics â€” the engine behind multiple OTel-native APM backends.

Top open source APM tools at a glance

Read the list by role, not rank:

OpenTelemetry â€” Instrumentation standard; defines OTLP; portable telemetry export
Jaeger â€” Trace backend; traces only; OTLP-native
DataBuff â€” Unified platform; metrics + traces; OTLP-native; AI-native APM
SkyWalking â€” Full-stack APM; metrics + traces; OTLP receiver
Zipkin â€” Trace backend; traces only; legacy OTLP support
Grafana â€” LGTM composable stack; metrics via Mimir/Prom, traces via Tempo, logs via Loki
Elastic APM â€” Full-stack APM for ELK-centric estates
Pinpoint â€” Java/PHP APM with bytecode-level tracing
SigNoz â€” OTel-native unified platform; metrics + traces + logs
ClickHouse â€” Columnar storage engine for telemetry analytics

OpenTelemetry and ClickHouse are infrastructure layers. Jaeger and Zipkin are trace specialists. Grafana, SkyWalking, Elastic APM, SigNoz, and DataBuff compete for "where on-call lives."

Choosing the right open source APM tool

Trace-only pain â€” Start with Jaeger or Zipkin; add Prometheus and Grafana if RED metrics matter.
Java-heavy agent preference â€” Evaluate SkyWalking or Pinpoint for bytecode depth; plan OTLP receivers for new services.
Existing Grafana practice â€” Extend with Tempo and Loki rather than replacing the stack on day one.
ELK-standardized logging â€” Elastic APM keeps search and APM in one vendor ecosystem.
Greenfield OpenTelemetry â€” Shortlist OTel-native unified backends (SigNoz, DataBuff) and validate with the same OTLP POC script.
AI-assisted on-call â€” Weight backends that ground LLM workflows in live spans and expose MCP or skill hooks for IDE agents.

Validation script: same steps for every finalist

Deploy with the project's published install path.
Point an OpenTelemetry SDK or Collector exporter at OTLP gRPC 4317 or HTTP 4318.
Generate traffic for five to ten minutes.
Confirm services appear, dependency topology renders, and a slow trace is searchable end-to-end.
Record retention defaults, sampling behavior, and resource usage on your target VM class.

For DataBuff, open the web UI on port 27403 after install. Full eBPF zero-instrumentation coverage remains on the public roadmap â€” plan SDK or Collector instrumentation for brownfield services today.

FAQ

What is the best open source APM tool in 2026?

There is no single best tool. Jaeger excels at dedicated tracing; Grafana LGTM wins for composable stacks; SkyWalking and Pinpoint serve agent-driven Java fleets; SigNoz and DataBuff target OTel-native unified platforms. Match the category to your signal mix and ops capacity.

Is OpenTelemetry an APM tool?

No. OpenTelemetry is the instrumentation and export standard. APM tools are backendsPand UIs that receive OTLP and provide service health, trace search, and alerting workflows.

Why include ClickHouse in an APM list?

Because several OTel-native APM platforms store telemetry in ClickHouse or similar columnar engines. Understanding that layer helps you plan retention, cardinality, and query cost.

Can open source APM replace Datadog or New Relic?

Modern OSS platforms offer comparable tracing, metrics, logs, and alerting for many workloads. The trade-off is self-hosting operations versus managed SaaS convenience.

How does DataBuff differ from SigNoz?

Both ingest OTLP into columnar storage for unified observability. DataBuff emphasizes a three-component footprint (Ingest, Apache Doris, Web), AI-native multi-agent triage grounded in live telemetry, and Skill/MCP extensibility for IDE agent workflows.

Closing perspective

The open source apm tools landscape in 2026 is healthier because OpenTelemetry decoupled instrumentation from backend choice. Use this top-ten map to identify which layer you are actually selecting â€” standard, trace specialist, composable stack, storage engine, or unified platform â€” then run the same OTLP validation script on every finalist.

References: OpenTelemetry Â· OTel Vendors Â· Jaeger Â· SkyWalking Â· DataBuff

Full-Link Monitoring Tools: OTLP Ingest and Unified APM in Practice

logan zhang — Sun, 05 Jul 2026 08:34:04 +0000

When teams evaluate full-link monitoring tools, the same questions keep coming up: Is Jaeger enough? Do we need SkyWalking? How do we assemble the LGTM stack? This guide maps the spectrum from trace-only backends to unified APM, then walks through a four-step acceptance path — topology → aggregate stats → single trace → dependency contribution — using OTLP ingest on ports 4317 (gRPC) and 4318 (HTTP).

Topics: distributed tracing · OTLP APM · open source observability · OpenTelemetry

The monitoring tool spectrum

Rather than a feature matrix (Dev.to and many MD renderers break on tables), here is how categories differ in practice:

Trace-only — Jaeger, Zipkin: distributed call chains; metrics and alerting usually live elsewhere.
Unified APM — SkyWalking, DataBuff: traces + metrics + topology + alerting in one platform.
Cloud-native assembly — Tempo + Prometheus + Loki + Grafana (LGTM): flexible, but you operate and wire dashboards yourself.

If your requirement explicitly says OpenTelemetry and full-link monitoring, prioritize an OTLP-native backend instead of a system that only supports legacy proprietary agents.

OpenTelemetry ingest essentials

Typical production path:

App (OTel SDK) → OTLP gRPC 4317 or HTTP 4318 → Ingest/Collector → Storage → Web topology & trace UI

A common migration pattern is a dual-export Collector: one receiver forwards to an existing Jaeger while you parallel-run a new APM backend, then compare trace fields and topology consistency (Collector configuration).

Quick env vars for a demo service:

export OTEL_EXPORTER_OTLP_ENDPOINT=http://<ingest-host>:4318
export OTEL_SERVICE_NAME=demo-service

Tool comparison (2026)

Jaeger — OTLP native; topology and span waterfall yes; best when you want a lightweight trace backend.
SkyWalking — OTLP supported; strong topology; mature Java microservice community.
LGTM — Tempo-native OTLP; topology via Grafana; familiar if your team already lives in Grafana.
DataBuff — OTLP as the primary path; auto topology; multi-protocol spans; unified service-flow contribution metrics.

Demo acceptance: topology to a single trace

The four screenshots below come from a DataBuff Demo (last 24 hours). They show the full drill-down loop teams should expect from a modern distributed tracing platform.

1. Architecture-level full-link view

Figure 1 · Global topology — Auto-drawn service-a → service-b edges across MySQL, Redis, Kafka, Elasticsearch, and remote HTTP. Node colors reflect health/alerts. First acceptance test for any full-link tool: can it show cross-service dependencies with zero manual CMDB?

2. Trace aggregate statistics

Figure 2 · Trace aggregates — Trace volume bars (~30 traces per 15 minutes in this demo), error counts (none here), and P50–P99 latency (P95 ~240 ms). Click a bar to jump into that window’s trace list — the bridge from SLA dashboards to individual requests.

3. Single-request span waterfall

Figure 3 · Span waterfall — TraceID 4b2a0a4c…, GET /demo/checkout, 240 ms total. Spans include Redis GET/SET, remote HTTP fraud check, service-b Dubbo/HTTP, MySQL SELECT, Elasticsearch search, Kafka publish — colored by Web/DB/Cache/MQ. This answers which segment or SQL slowed the request.

4. Service flow and response contribution

Figure 4 · Service flow — Entry service-a (240 ms / 2.9k calls) with downstream contribution: service-b 58%, Elasticsearch and MySQL ~8% each. Useful for on-call and capacity planning without opening every trace.

Install and POC

curl -fsSL https://databuff.ai/databuff/ai-apm-install.sh | bash

Web console defaults to port 27403
After ingest, validate the four views above in order: topology → trace stats → waterfall → service flow
If the Collector dual-writes to Jaeger, compare interface names and latency fields side by side

Quick selection guide

Trace only, minimal ops → Jaeger
Java microservices + mature OSS community → SkyWalking
Team already standardized on Grafana → LGTM
OTLP-native unified topology + waterfall + contribution → DataBuff

Takeaways

Full-link monitoring is not just trace search — you need topology, aggregates, span detail, and dependency contribution in one loop.
OTLP 4317/4318 should be the default ingest path in 2026 evaluations.
Run the same four-screenshot acceptance script on every shortlisted backend before you commit to retention and storage sizing.

References: OpenTelemetry Collector config · OTLP spec · Jaeger docs · DataBuff on GitHub · Install script

How to Pick an OpenTelemetry APM Backend in 2026

logan zhang — Sun, 05 Jul 2026 04:56:53 +0000

A practical guide to choosing an OpenTelemetry APM backend â€” evaluation criteria, common open-source patterns, and a hands-on look at a three-component stack.

If you instrument once with OpenTelemetry and still spend nights jumping between Jaeger, Prometheus, and log tabs, the problem is usually not the SDK â€” it is the backend. This article walks through how to evaluate backends, what the main open source APM patterns look like in 2026, and how to validate a candidate in an afternoon.

Why the backend matters

OpenTelemetry standardizes how applications emit traces, metrics, and logs. Your backend decides where telemetry lands, how you query it under incident pressure, and whether traces and RED metrics stay correlated.

Apps export OTLP (gRPC 4317 or HTTP 4318).
OpenTelemetry Collector (optional) receives and forwards telemetry.
APM backend stores and indexes data for UI and alerts.

Three common patterns

Trace-first (Jaeger, Zipkin)

Mature distributed tracing; Jaeger v2 uses the OpenTelemetry Collector framework. Best when tracing is primary and metrics/logs live elsewhere.

Modular LGTM (Grafana ecosystem)

Loki, Grafana, Tempo, Mimir/Prometheus â€” maximum flexibility, more services to operate.

All-in-one OSS APM

Unified UI for traces and metrics â€” e.g. widely referenced open source observability platforms built on OpenTelemetry. Apache SkyWalking remains strong in Java-heavy microservice environments with OTLP receivers alongside native agents.

Evaluation checklist

Before you shortlist vendors or OSS projects, answer these:

OTLP-native ingest â€” Does the backend consume OTLP without proprietary agents?
Correlation â€” Can you pivot from a slow service to spans in one UI?
Ops footprint â€” How many stateful components do you run in prod?
Storage â€” What is the cost and ops burden at your trace volume?
On-call UX â€” Service map, RED, trace search â€” usable at 3 a.m.?
Exit strategy â€” If you leave, is instrumentation still portable via OTel?

Example: three-component OpenTelemetry APM

DataBuff illustrates a compact opentelemetry apm backend: listed on the OpenTelemetry Vendors page as Pure OSS with Native OTLP Yes.

Figure 1 Â· Vendors entry (Native OTLP)

The stack collapses ingest, storage, and UI into three components:

Ingest â€” OTLP gRPC/HTTP intake
Apache Doris â€” unified storage for traces and metrics-shaped analytics
Web platform â€” dashboards, topology, and AI-assisted investigation

Figure 2 Â· Ingest â†’ Doris â†’ Web

UI expectations

After OTLP ingest, you should see service-level RED and dependency topology derived from traces.

Service RED overview

Figure 3 Â· Service RED overview

Global topology from traces

Figure 4 Â· Topology from traces

AI-assisted triage

Prefer AI that queries the same trace store â€” not a disconnected chat window.

Figure 5 Â· Alert diagnosis on live OTel data (topology red â†’ root cause + remediation)

Quick POC script

For any shortlisted self-hosted OpenTelemetry backend, run the same acceptance script:

Point demo app at OTLP 4318 (HTTP) or 4317 (gRPC).
Generate traffic for five minutes.
Confirm services, topology, and trace search.
Record ports and resource use on your target VM.

Example HTTP endpoint (replace host):

http://YOUR_HOST:4318/v1/traces

Web UI typically on port 27403 after install per project docs.

Takeaways

Pick on OTLP fidelity, correlated troubleshooting, and operable storage.
Open source apm spans trace-only, LGTM, and unified platforms.
Validate with a repeatable POC before standardizing retention.
For Native OTLP OSS with a small footprint and AI on live spans, evaluate DataBuff alongside SigNoz, SkyWalking, and LGTM.

References: OpenTelemetry docs Â· Vendors list Â· DataBuff on GitHub Â· Jaeger Â· SigNoz