Best Prometheus Alternatives for Distributed Tracing

Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct

Key Takeaways

• Prometheus is a metrics-only system and cannot store or query distributed traces, so teams must add a separate tracing backend.
• Three main categories of Prometheus-compatible tracing backends exist: open-source self-hosted (Tempo, Jaeger), unified open-source platforms (SigNoz, SkyWalking, OpenObserve), and commercial SaaS solutions.
• Grafana Tempo is the lowest-cost, best-integrated choice for teams already running Grafana, while Jaeger, SigNoz, SkyWalking, and OpenObserve each trade off cost, complexity, and UI preferences.
• Struct sits on top of any tracing backend as an automated investigation layer that correlates metrics, traces, logs, and code context the moment an alert fires and continuously verifies incident resolution.
• Automating your on-call runbook with Struct removes manual correlation work and can reclaim dozens of engineer-hours each month.

Why Prometheus Cannot Do Tracing and Where Tracing Backends Fit

Prometheus is a pull-based time-series metrics system. It scrapes numeric samples from instrumented endpoints, stores them in a local TSDB, and evaluates PromQL queries against aggregated data. Prometheus does not include native distributed tracing support, so teams must deploy a separate backend when they add traces to an existing Prometheus metrics stack.

The three categories of alternatives are:

• Open-source self-hosted backends, such as Grafana Tempo and Jaeger, which store traces independently and integrate with Prometheus through exemplars and Grafana datasource links.
• Unified open-source platforms, such as SigNoz, Apache SkyWalking, and OpenObserve, which combine traces, metrics, and logs in a single deployment with PromQL compatibility.
• Commercial SaaS platforms, such as Datadog APM and New Relic, which bundle tracing with managed infrastructure at higher per-host or per-span costs.

OpenTelemetry Collector configuration supports independent traces and metrics pipelines, so you can add trace ingestion without changing existing Prometheus pipelines. The exemplar mechanism, a (value, trace_id, timestamp) tuple attached to a metric observation, forms the primary bridge between Prometheus metrics and a dedicated tracing backend.

Choosing a Tracing Backend for Your Prometheus Stack

For teams that need automated incident resolution verification on top of their Prometheus and tracing stack, Struct is the top pick. Struct sits above any tracing backend as an investigation layer, automatically correlating Prometheus metrics, traces, logs, and code context the moment an alert fires. It then continuously verifies resolution against live observability data. Struct integrates in under 10 minutes and does not replace any existing tooling.

For the tracing backend itself, the right choice depends on your existing Grafana maturity, storage budget, and tolerance for operational complexity. The table below compares the five leading self-hosted and open-source options on the criteria that matter most to Prometheus-first SRE teams in 2026.

Backend	Prometheus Exemplar Support	Object-Storage Cost / GB-month (2026)	OTel Collector Config Complexity	Grafana Integration Depth
Grafana Tempo	Native, metrics_generator remote-writes span metrics + exemplars to Prometheus	Low, object storage on S3	Low, single OTLP exporter to Tempo endpoint	First-party, traces, metrics, logs, profiles in one UI
Jaeger	Via Grafana datasource link, Prometheus data source configured with Internal link to Jaeger as target	Higher due to indexing overhead on Elasticsearch	Medium, requires Elasticsearch/OpenSearch or ClickHouse backend	Good, Grafana Jaeger datasource plugin
SigNoz	ClickHouse backend, exemplar linking via OpenTelemetry SDK	Efficient storage with ClickHouse, self-hosted compute varies	Low, ships its own OTel Collector config	Built-in dashboards, Grafana plugin available
Apache SkyWalking	Prometheus fetcher for metrics, trace-metric correlation via its own UI	Self-hosted on Elasticsearch or BanyanDB, storage cost depends on backend choice	Medium, OAP server acts as collector, OTLP receiver available	Grafana datasource plugin, native UI preferred
OpenObserve	PromQL-compatible, exemplar support via OTLP ingest	Single storage layer for logs, metrics, and traces on object storage, comparable to Tempo	Low, replaces LGTM stack with one Helm chart	Built-in Grafana-compatible UI with PromQL

See how Struct adds automated investigation on top of your chosen backend and start a free 30-day pilot.

Grafana Tempo for Prometheus-First Teams

Grafana Tempo is the default tracing backend for teams already running Prometheus and Grafana. It stores traces as objects in S3, GCS, or Azure Blob with no tag-based indexing, so monthly trace data storage costs stay low with object storage. At 10,000 requests per minute, a self-hosted Grafana stack with Tempo on S3 costs approximately $30–50 per month in storage with 30+ days of trace retention, compared to $800–2,500+ per month for Datadog APM at the same workload.

Tempo’s metrics_generator component remote-writes span metrics and service graphs to Prometheus with send_exemplars enabled, which creates bidirectional navigation between metrics and traces. The Grafana datasource provisioning block supports tracesToMetrics for span-to-metric pivots and exemplar links for metric-to-trace navigation.

The following YAML configures exemplar linking between Prometheus and Tempo in Grafana:

datasources: - name: Prometheus type: prometheus url: http://prometheus:9090 jsonData: exemplarTraceIdDestinations: - name: trace_id datasourceUid: tempo urlDisplayLabel: View in Tempo - name: Tempo type: tempo uid: tempo url: http://tempo:3200

Limitation: Tempo stores trace data independently and does not replace Prometheus metric collection, so teams still operate two separate backends. Best for: Prometheus-first teams already running Grafana who want the lowest object-storage cost and first-party UI integration.

Jaeger for Flexible Storage Control

Jaeger is a CNCF-graduated tracing backend that accepts OTLP traces natively when started with ./jaeger-all-in-one --collector.otlp.enabled. Prometheus exemplar linking is configured in Grafana by enabling Internal link on the Prometheus datasource, setting the exemplar label to trace_id, and selecting Jaeger as the target datasource. Jaeger supports Cassandra, Elasticsearch, OpenSearch, and ClickHouse storage backends, which gives teams full data control.

Limitation: Jaeger on Elasticsearch incurs significantly higher storage costs at scale because of indexing overhead, and costs can become substantial at high volumes. Best for: Teams that need flexible storage backend choices and full data sovereignty and are willing to operate Elasticsearch or ClickHouse themselves.

SigNoz as a Unified ClickHouse Platform

SigNoz is an open-source unified observability platform built on ClickHouse. It ships its own OpenTelemetry Collector configuration and provides built-in dashboards for traces, metrics, and logs. ClickHouse-based backends deliver significant compression and faster queries for time-series data compared to Elasticsearch. SigNoz Cloud pricing starts at $199/month, while self-hosted deployments are free aside from infrastructure costs.

Limitation: SigNoz replaces Grafana as the primary UI, which creates friction for teams with significant existing Grafana dashboard investment. Best for: Teams that want a single open-source platform for traces, metrics, and logs without the operational overhead of the full LGTM stack.

Apache SkyWalking for Deep APM and Service Meshes

Apache SkyWalking is a mature APM platform with distributed tracing, service mesh observability, and a Prometheus metrics fetcher. Its OAP (Observability Analysis Platform) server acts as the collector and accepts OTLP traces. SkyWalking is self-hosted and free, and infrastructure costs depend on the chosen storage backend, Elasticsearch or BanyanDB.

Limitation: SkyWalking’s native UI is preferred over Grafana for trace analysis, and its Prometheus integration is less seamless than Tempo’s first-party exemplar support. The learning curve for TraceQL-equivalent queries in SkyWalking’s OAL language is steeper for teams already fluent in PromQL. Best for: Teams running Java-heavy microservices or service mesh environments such as Istio and Envoy that need deep APM capabilities beyond basic distributed tracing.

OpenObserve for Replacing the LGTM Stack

OpenObserve replaces the entire Grafana LGTM stack with a single unified platform and one storage layer for logs, metrics, and traces while maintaining PromQL compatibility for existing Prometheus dashboards and alerts. It deploys through a single Helm chart and handles high-cardinality Kubernetes labels natively. OpenObserve Cloud starts at $0 for the free tier, and self-hosted deployments are open-source.

Limitation: OpenObserve is a newer project with a smaller community and ecosystem than Grafana or Jaeger, so teams with deep Grafana plugin dependencies may experience migration friction. Best for: Teams frustrated by the operational overhead of managing four separate LGTM components who want PromQL compatibility without running Mimir, Loki, Tempo, and Grafana independently.

How Struct Closes the 3 AM Incident Gap

Choosing a tracing backend solves storage and query but leaves a gap in the middle of the incident workflow. At 3 AM an alert fires, a Prometheus metric crosses a threshold, and an on-call engineer must manually open Grafana, click through exemplar diamonds to Tempo or Jaeger, correlate trace spans with log lines, determine blast radius, apply a fix, and then confirm the system has recovered. Without a shared trace_id on metrics via exemplars, joining metrics to traces relies on time windows alone, which is lossy at non-trivial QPS. Every tab switch costs roughly 30–40 seconds of attention, and with 20 to 40 pivots typical in an investigation, context switching accounts for most of MTTR rather than the actual fix.

Struct removes this manual loop. When an alert fires in a Slack or PagerDuty channel, Struct automatically ingests the signal, queries Prometheus metrics, pulls correlated traces from the connected backend such as Grafana Tempo, Jaeger, or any OTLP-compatible store, correlates logs from AWS CloudWatch, GCP, or Datadog, and maps GitHub deploy history. It then produces a cited root-cause hypothesis and a dynamically generated dashboard before the engineer opens their laptop. Arcana cut investigation time from 30 minutes to 2 minutes and reclaims 56 engineer-hours per month after adding Struct on top of their existing observability stack.

The flagship expression of Struct’s incident resolution verification is its Incident Tracker, launched August 3, 2026. It runs an approximately one-minute automated verification loop against live Prometheus metrics and trace data to confirm an incident is actually resolved, not just acknowledged. This closes the loop that every other tool in this comparison leaves open. Practitioners on Reddit threads about Docker Swarm deployments and storage cost blow-ups consistently describe the same pain: they applied a fix, the alert stopped firing, and then the issue returned 20 minutes later because no one verified recovery against real observability data. Struct’s incident resolution verification loop addresses this directly.

Struct also encodes team-specific on-call runbooks so that every automated investigation follows the exact operational procedures a senior engineer would apply. For teams running Prometheus with exemplars enabled, Struct reads exemplar trace IDs from metric spikes and navigates directly to the representative trace. The workflow an engineer would perform manually completes automatically in under five minutes.

Connect your Prometheus stack and tracing backend to Struct in under 10 minutes and eliminate manual correlation with an automated incident verification loop.

Prometheus + Tracing Decision Framework

Use your current Grafana and Prometheus maturity to guide the right backend choice.

• Already running Grafana Cloud or self-hosted Grafana with Prometheus: Add Grafana Tempo, which offers native exemplar linking and the lowest object-storage costs of any option while keeping your operational surface within the Grafana ecosystem. To enable the exemplar bridge between Prometheus and Tempo, start Prometheus with --enable-feature=exemplar-storage and set send_exemplars = true in Grafana Alloy.
• Running Prometheus with no Grafana investment and needing full data control: Evaluate Jaeger with a ClickHouse backend, because ClickHouse-based solutions can have lower costs at high scale compared to Elasticsearch-backed Jaeger.
• Small platform team, under five SREs, wanting to reduce LGTM operational overhead: Evaluate OpenObserve or SigNoz, since both replace multiple components with a single deployment while preserving PromQL compatibility.
• Java-heavy or service-mesh environment: Evaluate Apache SkyWalking for its deep APM and Istio integration, then layer Struct on top for automated investigation and incident resolution verification.
• Any of the above, needing automated investigation and closed-loop resolution confirmation: Add Struct as the investigation layer. Struct ingests alerts the moment they fire and correlates them with metrics, traces, cloud infrastructure, and GitHub deploy history to produce a cited root-cause hypothesis.

Frequently Asked Questions

This section covers common questions about adding distributed tracing to Prometheus stacks and using Struct for automated incident resolution verification.

What is a Prometheus exemplar and how does it link metrics to traces?

A Prometheus exemplar is a (value, trace_id, timestamp) tuple attached to a specific metric observation, typically a histogram bucket recording request latency. When Prometheus is started with the --enable-feature=exemplar-storage flag and targets are scraped over the OpenMetrics content type, exemplars are stored in a fixed-size circular buffer per series. In Grafana, exemplars appear as diamond-shaped points on histogram panels, and clicking one deep-links the trace_id into a connected backend such as Grafana Tempo or Jaeger, which navigates directly from a latency spike on a dashboard to the representative distributed trace for that request. Only histogram and summary metric types support exemplars, while counters and gauges do not. Adding trace_id as a Prometheus label instead of an exemplar attachment causes cardinality explosion and must be avoided.

How do you add an OpenTelemetry Collector for traces without disrupting an existing Prometheus metrics pipeline?

The OpenTelemetry Collector supports independent traces and metrics pipelines under service.pipelines. A safe approach adds an OTLP receiver for traces and routes it to a Tempo or Jaeger exporter in a separate pipeline, while leaving the existing Prometheus receiver and prometheusremotewrite exporter pipeline untouched. The otelcol-contrib build is preferred over the core build because it includes the Prometheus receiver, Loki exporter, and prometheusremotewrite exporter.

A production-ready configuration uses a memory_limiter processor first and a batch processor last to reduce OOM risk during telemetry spikes. Avoid routing the same metrics through both a prometheusremotewrite exporter and a separate Prometheus scrape job for the same service, because this produces duplicate time series and erratic rate calculations.

What are realistic 2026 storage costs for distributed tracing data at scale?

Storage costs vary significantly by backend and volume. Grafana Tempo on S3 offers low-cost object storage for trace data, which makes long retention practical. A self-hosted Grafana stack with Tempo can have modest storage costs with sufficient retention. Jaeger on managed Elasticsearch incurs higher storage costs because of indexing overhead. ClickHouse-based solutions can be more cost-efficient than Elasticsearch at high volumes. Tail-based sampling in the OpenTelemetry Collector is the primary cost-control lever at high volume.

What is incident resolution verification and why does my current tracing stack not provide it?

Incident resolution verification is the automated process of confirming that an incident is actually resolved by checking live observability data. Prometheus metrics must return to baseline, error traces must disappear, and SLO burn rates must normalize, instead of relying on a human to manually declare the incident closed. Standard tracing backends such as Tempo, Jaeger, and SigNoz store and query traces but do not monitor ongoing incident state or trigger verification checks after a fix is applied.

Struct’s Incident Tracker runs an approximately one-minute automated verification loop against live Prometheus metrics and trace data, continuously confirming recovery and updating incident status automatically. This closes the loop that tracing backends leave open and prevents the common failure mode where an incident is marked resolved prematurely and recurs within minutes.

Conclusion: A Prometheus Stack That Stops Waking You Up

Prometheus remains the right choice for metrics and should stay in place. The right architecture for Prometheus-first SRE teams in 2026 is to add a dedicated tracing backend, such as Grafana Tempo for lowest cost and tightest Grafana integration, Jaeger for storage flexibility, or SigNoz or OpenObserve for unified platform simplicity, connected through OpenTelemetry Collector exemplars, and then layer Struct on top as the investigation and incident resolution verification layer.

Struct handles the investigation automatically the moment an alert fires, correlating Prometheus metrics, traces, logs, and code context into a single cited root-cause report. It then runs continuous automated verification to confirm the incident is actually resolved against real observability data. Teams like Arcana see similar results, and the 56 engineer-hours per month they reclaimed translate directly to faster feature delivery and reduced on-call burden.

Let Struct handle your next 3 AM alert automatically. Start your free pilot and reclaim dozens of engineer-hours each month.