How obs-unified compares

What this page is

A long-form companion to the comparison table on obsunified.com. The landing-page table is intentionally terse — each cell footnote-links into this page for the underlying claim, source URL, and a representative quoted phrase. Use this page to:

• Verify any claim that appears on the landing page.
• Read the per-vendor profile in one place.
• See what was deliberately not compared (Scope, below).

Scope

We compared nine third-party tools along ten capability axes:

• In scope: Datadog, Sentry, PostHog, Honeycomb, New Relic, Grafana Cloud (LGTM stack), SigNoz, Uptrace, HyperDX.
• Capability axes: hosting model, pricing model, distributed traces / APM, structured logs, AI / LLM observability, session replay, product analytics, alerting, cross-signal correlation, data ownership.
• Out of scope: infrastructure-only tools (Prometheus, Zabbix, Nagios), incident-response platforms (PagerDuty, Opsgenie), single-signal frontend tools (LogRocket, FullStory, Highlight), legacy enterprise-only suites (Splunk Observability Cloud, Dynatrace, AppDynamics, Elastic Observability). These are interesting but cover only a slice of what obs-unified covers, and including them would inflate the "—" column count without adding signal.

We deliberately excluded performance benchmarks (ingest throughput, query latency, agent overhead) — those depend so heavily on deployment shape that any cross-vendor number would be misleading.

Methodology

1. For each vendor, the lead capabilities page or pricing page on the vendor's own domain was fetched (docs.datadoghq.com, sentry.io, posthog.com, honeycomb.io, docs.newrelic.com, grafana.com, signoz.io, uptrace.dev, hyperdx.io / clickhouse.com).
2. A factual claim was extracted with a quoted phrase that supports it.
3. Each claim got an anchor id (e.g. src-dd-pricing) that the landing-page table footnotes into.
4. Negative claims ("vendor does not have X") are flagged when the evidence is absence-of-marketing-page rather than an explicit denial — see vendor sections for which negatives are inferential.
5. Capability scoping is non-judgmental: a vendor scoring "—" on session replay isn't worse than one scoring "✓" — it just doesn't sell that capability.

TL;DR

The nine vendors split into three clusters:

• SaaS-only full-stack (Datadog, New Relic) — comprehensive coverage, opaque cost model, no data-residency option beyond region choice.
• Single-signal-led (Sentry on errors, PostHog on product analytics, Honeycomb on traces, Uptrace on traces) — strong in one area, partial elsewhere, all now growing into adjacent signals (Sentry has logs + AI agents; PostHog has logs + LLM analytics; Honeycomb has agent observability EA).
• OSS-first OTel-native (Grafana, SigNoz, Uptrace, HyperDX) — self-host friendly, OTLP-native; HyperDX is the only one in this cluster that ships session replay. Product analytics is absent across all four.

obs-unified sits in the OSS-first cluster, and is the only tool in the whole comparison that ships session replay (rrweb), LLM observability, and product analytics alongside traces/logs/metrics — i.e. the only tool here that doesn't force you to bolt on PostHog or LogRocket on the side.

Comparison criteria

The ten capability axes used in the landing-page table:

1. Hosting model — can you run the vendor's backend in your own infrastructure, or only consume it as SaaS?
2. Pricing model — what dimension is the bill metered on (host, GB, event, session, user)?
3. Traces / APM — distributed tracing, with attention to whether OTLP is a first-class ingest path or a translation layer.
4. Structured logs — log ingestion, parsing, search, and explicit trace correlation.
5. AI / LLM observability — tracing of prompts/responses/tools/cost/eval for LLM-backed applications, as a vendor-shipped product (not a community plug-in).
6. Session replay — DOM-level recording of browser sessions (typically rrweb-based).
7. Product analytics — funnels, retention, cohorts, journey analysis over user-behavior events.
8. Alerting — alert rule types and primary signals the rules can fire on.
9. Cross-signal correlation — vendor's stated mechanism for pivoting between signal types.
10. Data ownership — where customer telemetry physically lives, and what residency / self-host options exist.

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obs-unified

Self-hosted on Cloudflare Workers + D1 + R2 (or Node + Postgres + S3 via the storage interface). Ships traces, logs, metrics, session replay (rrweb), AI/LLM observability, product analytics, alerts, and profiles in one stack with one telemetry graph agents can traverse from user action to backend trace, logs, replay, AI cost, and CPU profile. The Connected rail is the human-facing version of that graph. Free; you pay your own infra bill.

The rest of this page is the third-party comparison.

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Datadog

The SaaS-only full-stack incumbent.

Hosting model

SaaS-only. Customers pick a regional "site" (US1/US3/US5/EU1/AP1/AP2/Gov); data cannot cross sites and there is no general-purpose self-host or BYOC option for the Datadog backend itself.

• Source: Datadog Sites

• "Datadog offers different sites throughout the world ... you cannot share data across sites."

• Note: Datadog Observability Pipelines runs in customer infra, but only as a VM-deployed pre-processor — not a self-hostable backend.

Pricing model

Per-host for Infra ($15–$23/host/mo) and APM ($31–$40/host/mo); per-ingested/indexed GB for logs ($0.10/GB ingest, $1.70 per million indexed events); per-session for RUM; per-committer for code coverage; per-investigation for Bits AI SRE ($500 per 20 investigations).

• Source: Pricing | Datadog

• "$15 Per host, per month" (Infra Pro, annual), "$0.10 Per ingested or scanned GB, per month" (Log Ingestion).

Distributed traces / APM

Datadog APM provides distributed tracing. The default/recommended path is the Datadog Agent plus Datadog tracing SDKs; OTLP ingest is supported, but the direct OTLP traces endpoint is still in Preview as of May 2026 (contact-CSM gated).

• Source: Datadog OTLP Intake Endpoint

• "OTLP traces intake endpoint (in Preview): To request access for use, contact your Customer Success Manager."

Structured logs

Yes. Ingestion is decoupled from indexing for cost control; trace correlation is explicit.

• Source: Log Management

• "Connect your logs and traces to gain observability into your applications."

AI / LLM observability

Yes — a dedicated LLM Observability product traces prompts/responses/tools, tracks tokens/latency/cost, and runs evaluations including hallucination + prompt-injection + sensitive-data checks. Metered per LLM span.

• Source: LLM Observability | Datadog

• "Trace every request across prompts, model responses, retrieval steps, and tool calls".

Session replay

Yes — Session Replay ships as part of Real User Monitoring with link-out to backend traces.

• Source: Session Replay | Datadog

• "jump from session replays to backend traces for full-stack visibility."

Product analytics

Yes — Product Analytics is a distinct product within the Digital Experience suite (funnels, Sankey journeys, cohort/retention) sharing an SDK with RUM. (Up from "not shipped" in 2024.)

• Source: Product Analytics | Datadog

• "a single SDK for RUM and Product Analytics".

Alerting

Broad catalog: Metric, Anomaly, Forecast, Outlier, Change, Log, APM, Error Tracking, RUM, Synthetic, SLO, Composite, Watchdog (ML-based), Audit Trail, Database, Data Observability, CI, Cloud Cost, Network/NetFlow, Service Check.

• Source: Monitor Types

Cross-signal correlation

Marketed as a core differentiator; tag-based correlation across logs/metrics/traces.

• Source: Datadog Platform

• "Navigate seamlessly between logs, metrics, and request traces."

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Sentry

Errors-first, growing into the full observability tent.

Hosting model

SaaS (sentry.io) plus a Docker-Compose self-hosted distribution explicitly scoped to low-volume / proof-of-concept deployments.

• Source: getsentry/self-hosted

• "Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept."

Pricing model

Tier-priced base plan (Team $26/mo, Business $80/mo, Enterprise custom) with included quotas for errors / spans / replays / profiling; usage above quota is pay-as-you-go on per-unit dimensions (per-GB for logs and application metrics, per-hour for profiling, per-monitor for crons, per-alert for uptime).

• Source: Pricing | Sentry

• "Logs +$0.50/GB additional", "Continuous Profiling +$0.0315/hr".

Distributed traces / APM

Yes — Performance Monitoring with distributed tracing. OTLP traces and logs are supported via per-project DSN-derived endpoints (open beta); OTLP metrics are not supported.

• Source: OpenTelemetry Protocol (OTLP) | Sentry

• "Sentry can ingest OpenTelemetry traces and logs via OTLP endpoints. ... Sentry does not support OTLP metrics at this time."

Structured logs

Yes — a structured logs product that auto-links to active traces.

• Source: Logs | Sentry

• "send text-based log information from your applications, whether frontend or backend, to Sentry" — "searchable, trace-connected, and viewable alongside your errors."

AI / LLM observability

Yes — AI Agent Monitoring auto-captures agent runs / tool calls / model interactions. Seer is a separately-billed AI debugger that drafts root-cause analyses and autofix proposals over Sentry telemetry.

• Source (Agent Monitoring): AI Agent Monitoring | Sentry

• "automatically collect information about agent runs, tool calls, model interactions, and errors across your entire AI pipeline."

• Source (Seer): Seer | Sentry

• "Sentry's debugging agent ... reads the stack trace, traces the root cause through your codebase, and drafts a fix."

Session replay

Yes — Session Replay is GA for web and mobile (Android, iOS, React Native).

• Source: Session Replay | Sentry

• "video-like reproductions of user interactions ... browser-based applications and certain native mobile platforms, such as Android, iOS, and React Native."

Product analytics

Not offered as a product. The closest in-platform capability is Discover / Dashboards / Trace Explorer over telemetry. Inferential negative — Sentry's product index does not list a product-analytics SKU.

Alerting

Issue alerts on projects + Monitors, delivered via notifications, ticketing, webhooks, or integrations. Uptime monitoring and cron monitoring are billable line items in their own right.

• Source: Alerts | Sentry

• "An alert can send notifications, create tickets, call webhooks, or use other integrations — for issues coming from projects or Monitors."

Cross-signal correlation

The trace ID is the join key across signals; logs are explicitly positioned as "trace-connected."

• Source: Tracing | Sentry

• "The trace ID connects all the actions that take place, starting from the moment a user performs an action on the frontend ... all the way through to the actions this triggers across your application and services."

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PostHog

Product-analytics-led, aggressively growing into observability adjacencies.

Hosting model

Cloud (US + EU) is the recommended deployment; open-source self-host is positioned for hobbyist/small deployments only, receives no commercial support, has no access to paid-plan features, and the previous Kubernetes Helm chart is sunsetted.

• Source: Self-host PostHog

• "All paid-plan features are Cloud-only." / "New deployments of PostHog's paid open source product using Kubernetes are no longer supported."

• Supporting: Self-host disclaimer — "unlikely to scale past a couple 100ks events without significant effort."

Pricing model

Per-unit usage-based per product. Product Analytics from $0.00005/event; Session Replay $0.005/web recording; LLM Analytics from $0.00006/event; Logs from $0.25/GB; Error Tracking from $0.00037/exception; Feature Flags, Surveys, Data Warehouse, Pipelines, Workflows each have their own SKU.

• Source: PostHog pricing

Distributed traces / APM

No general-purpose APM / distributed tracing. The only "traces" PostHog ships are LLM-scoped (a collection of LLM generations and spans for a single user-LLM interaction).

• Source: LLM Analytics Traces

• "Traces are a collection of generations and spans that capture a full interaction between a user and an LLM."

Structured logs

Yes — Logs went GA on 2026-01-29, OTLP-compatible (ingests via standard OpenTelemetry SDKs, no PostHog package required), priced from $0.25/GB.

• Source: Logs | PostHog

• "a powerful logging solution that works with the OpenTelemetry Protocol (OTLP)"

• Supporting: Logs GA blog — "Logs is generally available, and it lives in the same place as your errors, session replays, and product data."

AI / LLM observability

Yes — LLM Analytics captures conversations, model performance, spans, costs, latency, and traces as PostHog events.

• Source: LLM Analytics

• "Track conversations, model performance, spans, costs, latency, and traces in LLM applications" — "all as regular PostHog events."

Session replay

Yes — built on rrweb.

• Source: Session replay ingestion

• "We use `rrweb` to collect "snapshot data" from the browser."

Product analytics

Flagship product. Autocapture + jump-from-graph-to-recording is the headline pivot.

• Source: Product Analytics

• "you can jump from a graph to a session recording to visually see why something happened" — autocapture "tracks every click and pageview automatically."

Alerting

Alerts are scoped to insights (trends only) — fixed-threshold, relative-change, and anomaly-detection modes. Notifications via in-app, email, Slack, and webhooks. Not a general alerting engine over logs/errors/LLM-cost thresholds.

• Source: Alerts | PostHog

• "Alerts enable you to monitor your insights and get notified when something important changes." / "alerts are supported on all trends."

Cross-signal correlation

Everything (analytics events, replays, errors, logs, LLM traces) lands on the same event store; the pitch is pivoting from a chart into the underlying session recording.

• Source: Logs GA blog

• "Logs is generally available, and it lives in the same place as your errors, session replays, and product data."

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Honeycomb

Tracing-first SaaS that pioneered the "wide events" data model.

Hosting model

SaaS, plus — as of November 19, 2025 — Honeycomb Private Cloud: a customer-hosted or Honeycomb-managed deployment that runs exclusively in the customer's AWS account (no other clouds, no on-prem).

• Source: Honeycomb Private Cloud

• "The power of Honeycomb, in your AWS environment."

• Supporting: Private Cloud deployment models

Pricing model

Event-volume based: Free up to 20M events/mo; Pro from $130/mo for up to 1.5B events/mo; Enterprise from a 10B events/year base allowance.

• Source: Honeycomb Pricing

Distributed traces / APM

OTLP-native — accepts OTLP over gRPC, HTTP/protobuf, and HTTP/JSON.

• Source: Send Data with OpenTelemetry

• "Honeycomb supports receiving telemetry data via OpenTelemetry's native protocol, OTLP, over gRPC, HTTP/protobuf, and HTTP/JSON."

Structured logs

No separate logs product — logs are modeled as structured events in the same wide-event store used for traces.

• Source: Events, Metrics, and Logs

• "a structured log can easily turn into a structured event."

AI / LLM observability

Launched 2026-05-12: Agent Observability (Agent Timeline, Canvas Agent, Skills, Auto-investigations) built on OpenTelemetry GenAI semantic conventions v1.40.0. As of May 2026, Agent Timeline is in Early Access (GA expected June 2026).

• Source: Honeycomb Launches Agent Observability

• Agent Timeline connects "every LLM call, tool invocation, agent handoff, and downstream system impact in real time."

Session replay

Not offered. Their Frontend Observability page lists Core Web Vitals, errors, traces, and user journeys — but not session replay. Honeycomb has publicly positioned against session-replay-led RUM.

Product analytics

Not offered. Inferential negative — there is no product-analytics page on honeycomb.io; the closest Analyze page positions analytics around telemetry, not user behavior.

Alerting

Triggers (threshold alerts on queries), BubbleUp (outlier dimensional analysis), Anomaly Detection. Notifications via PagerDuty, Slack, Microsoft Teams, webhooks, and email.

• Source: Triggers
• Supporting: BubbleUp

Cross-signal correlation

A single unified wide-event store: logs/traces/metrics share the same query substrate.

• Source: Log Analytics

• "Use one unified tool to manage logs, traces, and metrics."

Data residency

SaaS in AWS us-east-1 (US) or AWS eu-west-1 (EU); Private Cloud in customer's AWS account across US, European, or APAC regions.

• Source: Data Residency in Europe

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New Relic

The original SaaS APM, now usage-priced.

Hosting model

SaaS only across two regions (US and EU); accounts are pinned to a region at creation and data cannot be moved between them. No documented self-host / BYOC.

• Source: Choose your data center

• "Customer Data from existing New Relic accounts cannot be transferred or shared across regions."

Pricing model

Usage-based: 100 GB/mo free ingest, then $0.40/GB (Original) or $0.60/GB (Data Plus) — plus per-user seat fees (Core $49/user, Full Platform up to $349/user/year on Pro).

• Source: New Relic Pricing

Distributed traces / APM

Yes. Native OTLP is the recommended ingest path for OpenTelemetry data; regional endpoints (otlp.nr-data.net US, otlp.eu01.nr-data.net EU) over HTTP or gRPC.

• Source: New Relic OTLP endpoint

• "New Relic supports native OTLP ingest and recommends it as the preferred method for sending OpenTelemetry data."

Structured logs

Yes. Ingestion via APM agents, infrastructure agent, Fluentd/Fluent Bit/Logstash/Kubernetes, OTel Collector, or direct HTTP Log API. Server-side Grok parsing turns unstructured strings into queryable attributes.

• Source: Get started with log management
• Supporting: Parsing log data

AI / LLM observability

Yes — AI Monitoring is positioned as "APM for AI," with end-to-end visibility into latency/cost/quality across supported vendors (OpenAI, Bedrock, DeepSeek).

• Source: Introduction to AI monitoring

• "AI monitoring is our solution for application monitoring (APM) for AI. ... end-to-end visibility into performance, cost, and quality of supported models."

Session replay

Yes — a Pro / Pro+SPA browser-agent feature (agent v1.260.0+); DOM-based (not screen video); 8-day retention; PII masked by default.

• Source: Session replay | New Relic

Product analytics

No standalone product-analytics SKU. Behavioral capabilities (Sankey navigation paths, drop-offs, engagement) are folded into Browser Monitoring + ad-hoc NRQL.

Alerting

NRQL-based conditions are the recommended path; conditions are organized within policies.

• Source: Create NRQL alert conditions

• "We recommend creating an alert using a NRQL alert condition."

Cross-signal correlation

NRQL is the SQL-like query language that spans every telemetry type (events, metric timeslices, dimensional metrics, spans, logs).

• Source: Introduction to NRQL

• "The New Relic Query Language (NRQL) is a powerful tool you can use to query and understand nearly any type of data."

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Grafana Cloud (LGTM stack)

Multi-product OSS-led platform: Grafana + Loki + Tempo + Mimir + Pyroscope + Faro + Alloy + Beyla + OnCall + k6.

Hosting model

Grafana Cloud (SaaS) or self-host any of the LGTM components as open source. All ten OSS projects are first-party Grafana Labs maintained.

• Source: Grafana Open Source Projects
• Supporting (self-hosting): each project has its own OSS repo under github.com/grafana/{loki,tempo,mimir,pyroscope,faro,alloy,beyla}.

Pricing model

Free tier + Pro from $19/mo + usage-based per-unit charges: Metrics $6.50 per 1k series; Logs/Traces/Profiles $0.05/GB process + $0.40/GB write + $0.10/GB retain; Frontend Observability $0.75 per 1k sessions; Grafana Assistant $20/active AI user. Enterprise from $25k/year.

• Source: Grafana Cloud Pricing

Distributed traces / APM

Tempo for traces; Grafana Cloud accepts OTLP for metrics, logs, and traces.

• Source: Grafana Cloud OTLP

• "Grafana Labs supports the ingestion of metrics, logs, and traces through OTLP into Grafana Cloud."

Structured logs

Loki — "a horizontally scalable, highly available, multi-tenant log aggregation system using the same powerful data model as Prometheus."

• Source: Grafana Open Source → Loki

AI / LLM observability

Inferential negative. Grafana Cloud ships Grafana Assistant ($20/active AI user) — but Assistant is a GenAI-powered helper for navigating Grafana itself, not an LLM observability product for tracing customers' LLM applications. We could not find a dedicated Grafana LLM-observability product page; mark as "Assistant only."

• Source (for Assistant pricing existence): Grafana Cloud Pricing

Session replay

Not offered. Faro (Frontend Observability) captures Core Web Vitals, errors, logs, and client-side traces — but not session replay.

• Source: Frontend Observability

• "automatically captures real user performance metrics, errors, logs, and client-side traces." (No mention of session replay.)

Product analytics

Not offered. Inferential negative — no product on the Grafana platform page targets funnels / retention / behavior.

Alerting

Grafana Alerting — unified alert rules across data sources (metrics, logs, multi-dimensional).

• Source: Alerting overview

Cross-signal correlation

Per-data-source plumbing — trace-to-logs / metrics-to-traces (exemplars) / split-pane views in Grafana Explore. Correlation is configured per-data-source rather than implicit in a single store.

• Source: Grafana Open Source (platform-level framing)

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SigNoz

OTel-native OSS alternative.

Hosting model

SigNoz Cloud (SaaS) and self-hosted open-source — mix-and-match supported.

• Source: SigNoz/signoz on GitHub

• "Open-Source - you can use open-source, our cloud service or a mix of both based on your use case."

Pricing model

Pay-as-you-go: $0.30/GB ingested for traces and logs; $0.10 per million samples for metrics. Selectable retention (15 days–1 year for traces/logs; 1–13 months for metrics).

• Source: SigNoz Pricing

• "$0.3/GB ingested" (traces and logs); "$0.1/mil samples" (metrics).

Distributed traces / APM

OTLP-native — core pitch. Built directly on the OpenTelemetry Collector.

• Source: Distributed Tracing | SigNoz

• "Auto-instrument your applications with OpenTelemetry across all major languages and frameworks."

Structured logs

Yes — Logs Explorer with attribute filters, multiple view modes, aggregation operators.

• Source: Logs | SigNoz

AI / LLM observability

Yes — dedicated LLM Observability surface tracing agent workflows, token usage, cost; auto-instruments OpenAI, Anthropic, Bedrock, LangChain, LlamaIndex, CrewAI.

• Source: LLM Observability | SigNoz

Session replay

Not offered. Maintainers have publicly stated it is not on the roadmap.

• Source: Discussion #3846

• "As of now, this not in our near term roadmap (next 3-4 months)."

Product analytics

Not offered. Inferential negative — no product-analytics surface on signoz.io.

Alerting

Five alert types: metric-based, log-based, trace-based, exceptions-based, anomaly-based.

• Source: Alerts Management

Cross-signal correlation

OTel SDKs auto-inject trace_id + span_id into log records; UI exposes "Go to related logs" from a trace.

• Source: Correlate Traces and Logs

Data ownership

Self-hosted: ClickHouse cluster behind a SigNoz-flavored OTel Collector. Cloud: US, EU, and India regions.

• Source: Architecture | SigNoz

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Uptrace

OTel + ClickHouse APM, OSS-first.

Hosting model

Self-host (open-source, AGPL-3.0) or Uptrace Cloud. ClickHouse for telemetry + PostgreSQL for metadata.

• Source: uptrace/uptrace on GitHub

• "Uptrace uses OpenTelemetry framework to collect data and ClickHouse database to store it. It also requires PostgreSQL database to store metadata such as metric names and alerts."

Pricing model

Per-GB ingest: traces $0.10/GB, logs $0.10/GB, metrics $0.025 per million datapoints. 50 GB/mo free; 28-day default retention. No per-seat fees.

• Source: Uptrace Pricing

• "50 GB of traces, logs, and metrics free every month."

Distributed traces / APM

OTLP-native. (The internal obs-unified Uptrace migration doc notes Uptrace supports OTLP/gRPC and OTLP/HTTP; obs-unified is OTLP/HTTP-only as of this writing.)

• Source: Uptrace product

• "OpenTelemetry-native observability platform" — "Uptrace unifies traces, metrics, and logs in a single platform."

Structured logs

Yes. Logs ingested via OTel; integrated with trace context.

• Source: Uptrace product page — "unifies traces, metrics, and logs."

AI / LLM observability

Not offered. Inferential negative — no LLM observability product is listed on uptrace.dev as of May 2026.

Session replay

Not offered.

Product analytics

Not offered.

Alerting

Metric monitors and Error monitors. Notification channels: email, Slack, Mattermost, Telegram, Microsoft Teams, PagerDuty, Opsgenie, AlertManager, webhooks.

• Source: Uptrace Alerting

Cross-signal correlation

Traces, metrics, logs in one ClickHouse-backed store, queried via UQL.

• Source: Uptrace product

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HyperDX

ClickHouse-backed OSS observability; acquired by ClickHouse Inc. in March 2025 and now also offered as a managed component inside ClickHouse Cloud ("ClickStack").

Hosting model

Three paths: (a) self-hosted OSS on your own ClickHouse cluster (MIT-licensed), (b) standalone HyperDX Cloud at hyperdx.io, (c) managed inside ClickHouse Cloud as part of ClickStack. The OSS project remains actively maintained after the acquisition.

• Source: ClickHouse acquires HyperDX

• "HyperDX Cloud will continue serving and onboarding new customers" and "the open-source project remains actively maintained and developed."

• Supporting: hyperdxio/hyperdx (MIT licensed); ClickStack in ClickHouse Cloud (2025-08-06).

Pricing model

Three tiers: Free ($0/mo, 3 GB/mo, 3-day retention, 1 user); Starter ($20/mo flat, 50 GB/mo included, 30-day retention, unlimited users, $0.40/GB overage, $0.40 per 100 DPM); Enterprise (custom, adds SAML SSO).

• Source: HyperDX Pricing

• "Includes 50 GB/mo," "$0.40 per additional 1 GB," "$0.40 per 100 metrics (1 DPM)," "Unlimited Users, Flat Rate."

Distributed traces / APM

OpenTelemetry-native — accepts OTLP over both HTTP (https://in-otel.hyperdx.io) and gRPC (in-otel.hyperdx.io:4317) for traces, logs, and metrics.

• Source: OpenTelemetry | HyperDX Docs

• "HyperDX accepts telemetry directly from OpenTelemetry code instrumentation or collectors."

Structured logs

Yes — full-text search, native JSON parsing, live tail, and a "Log Patterns" feature that clusters related logs, all on top of ClickHouse.

• Source: hyperdxio/hyperdx

• "An open source observability platform unifying session replays, logs, metrics, traces and errors powered by ClickHouse and OpenTelemetry."

AI / LLM observability

No first-party LLM observability product. HyperDX is a documented OTLP destination for OpenLLMetry (Traceloop), OpenLIT, and Mirascope — so you get LLM tracing/token-cost via OTel-based instrumentation rather than a vendor-shipped LLM product.

• Source: LLM Observability with HyperDX and OpenLLMetry

• HyperDX is "an open source observability platform that natively supports OpenTelemetry"; integration sets TRACELOOP_BASE_URL=https://in-otel.hyperdx.io.

Session replay

Yes — browser-side session replay via the HyperDX OTel browser SDK, automatically linked to the corresponding logs and traces. (The session-replay engine is not named on hyperdx.io pages; reported as rrweb-based elsewhere, but flagged here as "industry-standard browser session replay" rather than asserted.)

• Source: HyperDX

• "Automatically link session replays with backend logs and traces"; "Unify Session Replays, Logs, Traces, Metrics and Errors."

Product analytics

Not offered. Confirmed absent — no funnels, retention, or user-journey analytics on the docs nav, marketing site, or pricing page.

• Source: HyperDX Docs

Alerting

Search-based and dashboard-chart-based alerts; threshold + duration + check-interval configurable (1m–1d). Notifications via Slack, Email, PagerDuty, or Slack Webhook.

• Source: Alerts | HyperDX Docs

• "Set the threshold, duration, and notification method for the alert (Slack, Email, PagerDuty or Slack Webhook)."

Cross-signal correlation

Auto-linking across signals is the marketed differentiator — session replays, frontend events, backend traces, and logs share IDs so users one-click pivot between them.

• Source: HyperDX

• "Trace every request from a user's browser and phone to your backend servers and async workers, automatically."

Data ownership

Self-hosted: data lives entirely in your own ClickHouse cluster. Cloud: hosted in the United States; no published EU region as of this review.

• Source: DeploySentinel DPA

• "DeploySentinel (HyperDX) will host and process Customer Personal Data in the United States."

• Supporting: OSS vs Cloud

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Discussion

This section is editorial (analysis, not vendor-cited facts). Where it points at vendor capabilities, the relevant anchor is linked back to the cited claim above.

Storage architecture as a cost-and-flexibility lever

The vendors split along their primary storage backend, and that split shows up in pricing and operational shape:

• ClickHouse-backed open-source platforms (SigNoz, Uptrace, HyperDX) inherit columnar compression and high-cardinality query performance from ClickHouse, which is what lets their per-GB ingest pricing (SigNoz $0.30/GB, Uptrace $0.10/GB) undercut per-host SaaS by an order of magnitude.
• Proprietary backends (Datadog, New Relic) meter on dimensions that map to their internal cost model (per host, per indexed event, per GB ingest + per user) and bill at higher per-unit rates.
• Wide-event store (Honeycomb) is a third shape — single event store with event-volume pricing.
• PostHog uses ClickHouse internally for events but exposes per-product SKUs (per event, per recording, per LLM span) rather than per-GB; storage shape and pricing shape are decoupled here.
• obs-unified deliberately does not use ClickHouse. The default backend is SQLite via Cloudflare D1, with a Postgres adapter via the storage interface. The trade-off: D1 caps at ~100M hot rows per project (then archive sweep or move to Postgres), in exchange for zero operational cost on the Workers tier and a single-image local deploy.

The right backend depends on what you're optimizing for. ClickHouse pays off for high-cardinality search across billions of rows. SQLite/D1 pays off for projects whose hot-data ceiling is bounded and who want a single-binary or single-Worker deploy. No one shape is universally better.

Two product shapes: suites vs. graphs

The vendors fall into two product shapes:

• Multi-product suites (Datadog, Sentry, PostHog, New Relic, Grafana Cloud) — each signal has its own product UI and SKU under a common brand. Correlation works within the suite (Datadog by tag, Sentry by trace_id, PostHog by event store, New Relic by NRQL, Grafana per-data-source). Strong on breadth, integration ecosystem, and enterprise controls.
• Unified telemetry graphs (SigNoz, HyperDX, Uptrace, Honeycomb, obs-unified) — all signals are nodes in one store and one UI. Pivots are first-class because there's nothing to pivot between. Stronger on cross-signal flow at the cost of fewer per-product surface features.

Neither shape is wrong. Big organizations with established team boundaries (frontend RUM team vs. SRE vs. data team) often map cleanly to a suite. Smaller teams that own a vertical slice (backend → frontend → AI) often get more from a graph.

What's actually convergent

A real trend across all nine vendors over the past 18 months: every one of them has added either LLM observability or structured logs (or both) to their offering. The vendor lines are blurring:

• Sentry shipped Structured Logs (cited above) and AI Agent Monitoring + Seer (cited).
• PostHog shipped Logs (GA 2026-01-29, cited) and LLM Analytics (cited).
• Honeycomb shipped Agent Observability in Early Access (2026-05-12, cited).
• Datadog shipped Product Analytics (cited) and LLM Observability (cited).
• SigNoz, New Relic, Grafana Cloud, and HyperDX all shipped LLM observability or LLM-instrumentation paths in the same window.

obs-unified's positioning is the bet that the graph shape (one identity chain, one store, one agent-traversable telemetry graph) is the right organizing structure for that convergence — not that any specific vendor is wrong. Most of the vendors above will persist; the design hypothesis is just that convergence rewards graph shape over suite shape, and that "we own our data plane" is a multiplier on that.

Buyer guidance

The factual table at the top is the answer to "what does each vendor do." This is the answer to "which one should I pick":

• If you need enterprise controls, a wide integration ecosystem, and quick deployment with a per-host cost model your finance team has already approved → Datadog or New Relic.
• If you need errors + replay + traces but don't want to think about infra → Sentry.
• If you need product analytics + replay and observability is secondary → PostHog.
• If you need deep trace analysis and you'll build the rest yourself → Honeycomb.
• If you want OSS, OTel-native, and ClickHouse-shaped pricing → SigNoz, HyperDX, or Uptrace (mostly differ on UX taste; HyperDX is the only one with session replay; Uptrace is the only one with native OTLP/gRPC; SigNoz has the most alert types).
• If you want a fully composable LGTM stack you can scale independently → Grafana Cloud (or self-host the components).
• If you want everything-in-one (traces, logs, metrics, replay, LLM, analytics) self-hosted on your infra and built for agentic debugging → obs-unified, with the trade-offs in the next section.

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Where obs-unified fits

obs-unified is the only tool in this comparison that is simultaneously self-hostable, OTLP-native, and covers session replay + LLM observability + product analytics in one stack. The closest neighbors are:

• HyperDX — closest in graph shape: OSS, OTel-native, auto-linked replay + logs + traces. Differs in backend (ClickHouse vs. SQLite/D1), in LLM coverage (HyperDX inherits via OpenLLMetry rather than shipping LLM eval first-party), and in product analytics (absent).
• PostHog — same multi-signal breadth (now that they ship logs + LLM analytics), but Cloud-only for paid features; primary scaffolding is product-analytics-shaped, not trace-shaped.
• SigNoz — same OSS + OTel-native posture, but no session replay, no product analytics, no LLM cost tracking with eval (LLM observability is trace-shaped only).
• Grafana Cloud — same OSS-friendly multi-component posture, but no session replay, no product analytics, no LLM observability for customer apps.

The trade-offs working against obs-unified versus this set:

• Maturity — early. Production deployments are limited; storage scale is bounded by the SqlDb adapter you pick (D1 caps at ~100M hot rows; Postgres is fine to billions).
• OTLP/gRPC — not yet (OTLP/HTTP only as of this writing). Migrate gRPC exporters to HTTP or front with an OTel Collector.
• SSO / RBAC — single-password auth today (see Auth & multi-tenancy gap in the Uptrace comparison).
• Free-form dashboards / query language — Analyses are LLM-narrative-shaped, not panel-shaped. There's no PromQL/NRQL/UQL equivalent.
• Generic infra metrics — the Resources dashboard is Cloudflare-shaped first, with Linux-host mode added via the OTel hostmetricsreceiver. Non-Linux non-Cloudflare deployments have less curated coverage.

These are the right axes to weigh obs-unified on. If any of the above are blockers, one of the other eight tools above is likely the right pick.

Refresh schedule

Last full review: 2026-05-19 (this document). Next scheduled review: 2026-08-19 (quarterly). On each refresh, every cited URL is re-fetched and updated; any claim where the cited evidence has changed is updated or removed.