Written by: Nimesh Chakravarthi, Co-founder & CTO, Struct
Key Takeaways for Growing Engineering Teams
- Open source observability platforms give Series A–C teams full data ownership and remove per-seat licensing fees while forming the diagnostic foundation for on-call rotations.
- SigNoz, OpenObserve, and Grafana LGTM each support native OpenTelemetry but differ in storage costs, scaling behavior, and operational overhead.
- Storage costs and high-cardinality metrics are the main reasons teams outgrow their first observability stack, with Parquet on S3 often far cheaper than ClickHouse.
- Incident resolution verification closes the gap between detecting problems and confirming fixes by running automated checks on top of existing observability platforms.
- Struct automates your on-call runbook and adds incident resolution verification to any platform in this guide in under 10 minutes.
SigNoz vs OpenObserve vs Grafana LGTM: 2026 Platform Snapshot
The table below compares the four attributes that matter most to Series A–C on-call teams: signal coverage, OpenTelemetry instrumentation effort, storage cost profile, and the explicit limitation you will hit at scale. Every figure is cited inline.
| Platform | Logs / Metrics / Traces | OTel Native Effort | Storage Cost Profile | Explicit Limitation |
|---|---|---|---|---|
| SigNoz | All three in one ClickHouse backend | OTLP-native, no shim required, query builder avoids PromQL/LogQL | Self-hosted free; cloud at $0.30/GB logs+traces, $49/mo base | ClickHouse merge pressure under continuous small-batch inserts can produce “too many parts” errors, smaller community than Grafana |
| OpenObserve | All three, single binary, Parquet on S3/GCS/Azure Blob | OTLP-native, stateless nodes, two-annotation Kubernetes deploy pattern | $0.50/GB ingested on cloud; ~$70/mo for 1 TB/day at 30-day retention on S3 vs ~$700/mo for ClickHouse equivalent | Younger ecosystem, fewer pre-built dashboard templates, AI incident features still maturing |
| Grafana LGTM (Loki + Mimir + Tempo + Grafana) | All three, separate storage per signal | OTel-compatible via Alloy collector, Beyla for eBPF zero-code | Cloud free tier: 10k series, 50 GB logs, 50 GB traces; $6.50/1k additional metric series | Self-hosted requires 2–4 weeks initial setup and 0.1–0.2 FTE ongoing, Grafana OnCall OSS archived 2026-03-24 |
| Prometheus + Jaeger (modular baseline) | Metrics + traces only, no native log management | Prometheus 3.0 accepts OTLP metrics via –web.enable-otlp-receiver flag, Jaeger OTLP-native | Zero licensing, storage cost depends on backend chosen | No built-in log management, unified UI, or AI remediation, requires additional tools and context-switching during incidents |
Automate your on-call runbook, and Struct will layer incident resolution verification on top of any platform in this table in under 10 minutes.
How Incident Resolution Verification Sits Above Your Stack
Incident resolution verification automatically confirms that an incident is actually resolved by querying observability data, not by trusting manual sign-off. It runs as a layer above your chosen open source observability platform and does not replace that platform.
The architecture follows a pattern Rootly describes as gathering symptoms, pulling recent changes, mapping dependencies, ranking hypotheses with evidence, proposing reversible mitigations first, and capturing the process into the incident record automatically. Incident.io’s 2026 AI SRE guide frames the same pattern as an overlay that integrates with monitoring systems such as Datadog, Prometheus, and Grafana plus code repositories like GitHub rather than replacing those systems.
Struct’s Incident Tracker implements this overlay pattern in product form. Launched August 3, 2026, it runs a ~1-minute automated verification loop against your existing observability data to confirm an incident is resolved. It tracks active incidents, keeps status current automatically, and surfaces the result directly in Slack so your team stops relying on tribal knowledge to decide when to close a ticket.
Storage Costs and Cardinality Trade-offs at Scale
Storage costs and cardinality constraints are the two most common reasons Series A–C teams outgrow their initial observability choice. The numbers are concrete enough to model before you commit.
Parquet compression can provide significant storage savings for observability data, though exact ratios vary by data type, compression settings, and content.
For large ingest volumes with extended retention, Apache Parquet on S3 can cost substantially less than JSON Lines on S3. That gap explains why OpenObserve’s Parquet-on-object-storage architecture produces a meaningfully lower bill than ClickHouse block storage at the same retention window.
Storage format is one cost lever; cardinality is the other. High-cardinality labels in Prometheus, such as user_id or request_id, cause linear memory growth and query degradation beyond ~10M active series. The practical rule is simple. Never use user-generated values as Prometheus labels, and keep label cardinality under 1,000 distinct values per label.
Qonto, a digital bank, compressed 231 TB of high-cardinality trace data to 376 GB in ClickHouse Cloud, a 99.84% compression ratio. They extended query windows from 2–3 hours to two full weeks of unsampled data. Their SRE tech lead summarized the shift: “As an observability guy, cardinality was a scary word for us. Now it’s something I actively root for.”
Full-text indexing of log data can increase storage requirements, which is why Elasticsearch-based stacks carry materially higher storage bills than columnar alternatives at the same ingestion rate.
GitHub Setup and Operational Overhead by Platform
For a 15–80 engineer team without a dedicated platform engineer, operational overhead often matters more than feature parity.
The self-hosted Grafana LGTM stack requires 2–4 weeks for initial setup and 0.1–0.2 FTE ongoing maintenance. That work covers Helm upgrades, cardinality pruning, and alerting rule hygiene, and it remains a recurring cost.
SigNoz reduces that burden by consolidating all three signals into one ClickHouse backend, which removes the need to operate Loki, Tempo, and Mimir separately. OpenObserve goes further with a single-binary deployment backed by object storage, which removes the ClickHouse operational surface entirely.
On the instrumentation side, OpenTelemetry Go Compile-Time Instrumentation reached stable v1 on July 16, 2026. It enables zero-code traces and metrics for Go services by replacing go build with the otelc tool, with no runtime overhead added. For Kubernetes environments, Adobe’s pattern of two Kubernetes annotations enabling full auto-instrumentation via the OTel Operator is the lowest-friction path for teams that can rebuild container images.
The March 2026 OpenTracing deprecation means any new instrumentation work should target native OTel APIs directly. Migration guidance lives in the official migration guide.
Slack, PagerDuty, and GitHub Integrations Across Platforms
All three major platforms support alerting to external channels, but the depth and style of integration differ.
Grafana’s unified alerting routes to PagerDuty, Slack, and OpsGenie natively through contact points and notification policies. With Grafana OnCall OSS archived on 2026-03-24, self-hosted paging now relies on Alertmanager routing to external providers rather than a built-in on-call scheduler. Teams usually fill that gap with PagerDuty, Rootly, or incident.io.
SigNoz supports webhook-based alerting to Slack and PagerDuty. OpenObserve provides alert destinations including Slack, PagerDuty, and email. Neither platform natively closes the loop by verifying that an alert condition has cleared after a fix is deployed.
Struct focuses on that verification gap. It integrates directly into Slack and PagerDuty as the alerting trigger layer, then automatically queries your existing observability backend, such as Grafana, Datadog, Prometheus, or SigNoz, to confirm resolution. GitHub integration provides code context so the root cause analysis includes the specific commit or PR responsible.
Automate your on-call runbook, and connect Struct to your existing Slack alerts and observability stack in under 10 minutes.
Matching Platforms to Team Size and Alert Volume
The decision matrix below maps team size and alert volume to the most practical choice and names commercial alternatives where they matter.
- 1–2 platform engineers, under 500 alerts/month: OpenObserve single-binary on S3 minimizes operational surface, which makes it the lowest-overhead choice when you can accept a smaller ecosystem. If you cannot modify application code at all, Coroot’s eBPF auto-instrumentation provides coverage without SDK changes. Datadog is the incumbent here but carries per-host pricing that scales poorly past 30 hosts, so it only fits when vendor support justifies the premium.
- 3–10 engineers, 500–5,000 alerts/month: SigNoz self-hosted or cloud gives Datadog APM-equivalent correlation without per-host fees. PagerDuty or incident.io handle on-call scheduling, and Rootly handles incident management. Struct adds incident resolution verification on top of that stack.
- 10–25 engineers, 5,000+ alerts/month: Grafana LGTM self-hosted or Grafana Cloud provides the broadest ecosystem and dashboard library, and operational overhead becomes justified at this scale. Datadog remains the dominant commercial alternative, and its Bits AI feature competes directly with automated investigation tools. Struct layers investigation and resolution verification without replacing Grafana.
- Strict SLA environments (fintech, payments): Any platform choice must be paired with automated incident resolution verification. A Series A fintech with 40 engineers cut triage time by 80% and protected SLAs by layering Struct on top of their existing alerting stack, reducing a 30–45 minute investigation to under 5 minutes.
Migration signals that indicate it is time to change platforms include dashboard sprawl beyond 5–6 actively used dashboards, engineers manually grepping logs by timestamp to match traces, rising MTTR despite a stable codebase, and incidents where the first 20 minutes are spent deciding where to investigate.
Frequently Asked Questions
Does the March 2026 OpenTracing deprecation affect my existing instrumentation?
If your services use OpenTracing shims today, they continue to work during the deprecation period because removal from the specification cannot happen before March 2027. New instrumentation work should target native OpenTelemetry APIs and SDKs directly. For Series A–C teams, that means any greenfield service or new language SDK should use OTLP-native instrumentation from day one, and existing OpenTracing-instrumented services should be scheduled for migration before the 2027 removal window. The official migration guide at opentelemetry.io/docs/compatibility/migration/opentracing/ is the authoritative starting point.
What Parquet compression ratio should I use for storage cost modeling?
Parquet compression ratios for observability data vary depending on the specific data type, structure, and algorithm used. The variance depends on cardinality, field count, and the proportion of structured versus freeform text. The cost advantage mentioned earlier compounds as telemetry volume grows, which makes format choice a critical decision for teams planning multi-year retention.
Can eBPF instrumentation replace SDK-based OpenTelemetry instrumentation for Go services?
For most Kubernetes workloads, eBPF and compile-time instrumentation work as complementary approaches rather than interchangeable ones. OpenTelemetry eBPF Instrumentation (OBI) captures distributed traces and RED metrics from the Linux kernel without code changes, which makes it practical for legacy services, Rust, C++, and large-scale clusters where rebuilding binaries is impractical. The OpenTelemetry Go Compile-Time Instrumentation v1, released July 16, 2026, covers Go services that can be rebuilt. Replacing go build with otelc adds zero runtime overhead and instruments net/http, database/sql, gRPC, Redis, and Go runtime metrics automatically. Teams with mixed language stacks typically use eBPF for coverage breadth and compile-time or SDK instrumentation for depth on critical services.
How do I layer incident resolution verification on top of an existing open source observability stack without replacing it?
The pattern is integration, not replacement. Your observability platform, such as SigNoz, Grafana, Prometheus, or OpenObserve, continues to own signal collection, storage, and dashboards. An incident resolution verification layer sits above it, listens to your alerting channels like Slack or PagerDuty, automatically queries your observability backend when an alert fires, correlates logs and traces with recent GitHub commits, and runs a continuous verification loop to confirm the incident is resolved against real telemetry data.
Struct implements this pattern with a 10-minute setup. You connect your Slack or PagerDuty channel, authenticate your observability source and GitHub repository, and Struct begins investigating automatically. The Incident Tracker checks your observability data continuously until the alert condition clears, then updates status without requiring manual sign-off.
Next Steps for Strengthening Your Telemetry Stack
The platform choice, whether SigNoz, OpenObserve, or Grafana LGTM, is primarily a storage and operational overhead decision. All three support native OpenTelemetry instrumentation, integrate with Slack and PagerDuty, and share the same gap: they tell you something is wrong but do not automatically verify that it is fixed.
Before selecting a platform, audit the foundation that any observability tool depends on. Start with your current telemetry quality, asking whether you have trace IDs, structured logs, and meaningful alert thresholds. Then review your runbook coverage, checking whether every alert has a documented investigation path or whether resolution depends on one senior engineer’s memory. Teams evaluate at current telemetry volumes but face bills at 5–10x those volumes once service proliferation occurs, so model your storage costs at that future scale, not today’s.
Struct’s customer Arcana cut investigation time from 30 minutes to 2 minutes, reclaimed 56 engineer-hours per month, and now runs 2,100+ automated investigations monthly on top of their existing observability stack without replacing it.
Automate your on-call runbook, and book a 30-minute demo to see how Struct layers incident resolution verification on your current telemetry stack, whether you run SigNoz, Grafana, Datadog, or any combination.
