Better Stack vs HyperDX: A Complete Comparison for 2026

HyperDX built its reputation on a straightforward idea: full-stack observability without enterprise pricing overhead. Built on ClickHouse and OpenTelemetry, it gave teams unified logs, traces, session replay, and error tracking at a cost that was dramatically lower than traditional platforms. Its acquisition by ClickHouse in 2025 strengthened that positioning and folded the product into the broader ClickStack ecosystem.

Better Stack targets much of the same territory. It combines logs, metrics, traces, real user monitoring, error tracking, incident management, and status pages with the same OpenTelemetry-native and volume-based pricing philosophy.

The real difference is not in telemetry collection. It is in how much of the operational workflow each platform covers.

HyperDX is strongest for teams that are comfortable running or deeply customizing their own observability stack, especially if ClickHouse is already part of their infrastructure strategy. It gives you a flexible, developer-oriented observability layer with significant control over the underlying system.

Better Stack focuses on reducing operational overhead instead. It delivers fully managed observability together with on-call scheduling, incident management, AI-assisted investigation, and status pages in one platform. That means fewer moving parts, fewer integrations to maintain, and a faster path from alert to resolution.

If your team wants maximum control and is already invested in ClickHouse, HyperDX is a compelling fit.

If your goal is to simplify operations and manage the full incident lifecycle in one system, Better Stack is the stronger choice.

This comparison breaks down where each approach fits best.

Quick comparison at a glance

Platform architecture

Both platforms chose ClickHouse as a foundation. Better Stack built a fully managed observability warehouse on top of it with a proprietary storage layer and optional S3 export. HyperDX is an open-source UI and ingestion layer that runs directly on any ClickHouse cluster. That architectural choice determines almost everything else about how the two products feel to use.

Better Stack: managed unified platform

Better Stack's architecture removes the infrastructure question from the equation. You don't provision ClickHouse, tune memory settings, or maintain cluster health. The platform handles storage, indexing, and retention automatically.

The core principles behind Better Stack's data layer are relevant here: one collector, one storage model, one query language across logs, metrics, and traces. Watch how the Better Stack collector discovers services and begins capturing telemetry automatically:

eBPF-based collection is the key differentiator at the ingestion layer. Where HyperDX requires OpenTelemetry SDK instrumentation per service, Better Stack's eBPF collector operates at the kernel level. Deploy it as a Kubernetes DaemonSet and your HTTP traffic, database queries (PostgreSQL, MySQL, Redis, MongoDB), and service-to-service calls are captured without any application code changes.

Unified storage means SQL and PromQL work identically across logs, metrics, and traces. There's no "switch to the Logs product" or "open the APM view." Every alert resolves to a single view with all relevant context.

HyperDX: open-source observability on ClickHouse

HyperDX is worth understanding correctly. It is not a SaaS product in the traditional sense. The core product is an open-source observability UI and data ingestion layer that runs on top of ClickHouse. After the ClickHouse acquisition in March 2025, it became a component of ClickStack alongside the ClickHouse database and an OpenTelemetry collector.

What this means practically: if you self-host, you are running and maintaining a ClickHouse cluster alongside HyperDX. The Docker Compose quickstart is genuinely fast to get running, but production deployments require ClickHouse capacity planning, backup strategies, and upgrade management. HyperDX's v2 UI is a browser-only Next.js application that connects directly to ClickHouse, so the operational surface is actually simpler than v1, but ClickHouse itself is still your responsibility.

HyperDX Cloud exists as a managed option at the same pricing ($20/month base, $0.40/GB). It includes enterprise SSO and source maps as cloud-only features not available in the open-source build. The open-source version follows a model similar to GitLab's stewardship approach: core features in OSS, enterprise features in cloud.

The honest architectural tradeoff: HyperDX gives you full data ownership and no vendor lock-in on your observability storage. You can run SQL directly against your ClickHouse cluster using any tool you want. Better Stack gives you that same SQL access through its managed interface, but you don't own the underlying cluster unless you enable S3 export. For teams with existing ClickHouse infrastructure, HyperDX is a natural fit. For everyone else, the operational overhead of self-hosting ClickHouse is a real cost.

Is your team already running ClickHouse in production? That question determines whether HyperDX's architecture is an advantage or a liability.

Pricing comparison

HyperDX's pricing model is genuinely clean: $0.40 per GB of uncompressed data ingested, zero per-seat fees, zero per-host fees, and unlimited users on paid plans. Better Stack is volume-based too, but the formula also includes responders and monitors. Both platforms represent a fundamentally different philosophy from Datadog's multi-dimensional billing.

Better Stack: transparent and feature-complete

Better Stack charges based on data volume, responders, and monitors. The pricing formula is stable and predictable.

Pricing structure:

• Logs: $0.10/GB ingestion + $0.05/GB/month retention (all searchable)
• Traces: $0.10/GB ingestion + $0.05/GB/month retention
• Metrics: $0.50/GB/month (no cardinality penalties)
• Error tracking: $0.000050 per exception
• Responders: $29/month (unlimited phone/SMS)
• Monitors: $0.21/month each

100-host deployment example: $791/month

• Telemetry (2.5TB/month): $375
• 5 Responders: $145
• 100 Monitors: $21
• Error tracking (5M exceptions): $250

This includes incident management, status pages, on-call scheduling, and AI SRE. Those are not add-on SKUs.

HyperDX: lowest base cost, narrower scope

Pricing structure:

• Free tier: 3 GB/month, 3-day retention, 1 user
• Starter: $20/month includes 50 GB/month, 30-day retention, unlimited users
• Overage: $0.40 per GB beyond the included 50 GB
• Metrics: $0.40 per 100 metric data points per minute (DPM)
• Enterprise: custom pricing, custom retention, SAML SSO, volume discounts

100-host deployment example (comparable telemetry):

If the same 100-host setup generates 2.5TB/month of data, HyperDX costs approximately:

• Base: $20
• Overage: (2,500 GB - 50 GB) × $0.40 = $980
• Total: ~$1,000/month

That's comparable to Better Stack's $791 for telemetry alone. But HyperDX's $1,000 does not include incident management, on-call, or status pages. Better Stack's $791 does. If you add PagerDuty for on-call (the typical integration HyperDX points to), you're looking at $49-83 per responder per month, adding $245-415/month for 5 responders.

Self-hosted cost: If you run HyperDX on your own infrastructure, you pay $0 for the software and cover your own ClickHouse hosting costs. For a 2.5TB/month workload, ClickHouse compute and storage on a major cloud provider typically runs $200-600/month depending on retention and query patterns, plus engineering time for operations. This is the genuinely attractive scenario for HyperDX: teams that already run ClickHouse and want to add an observability UI at minimal marginal cost.

Cost comparison: 3-year TCO (managed cloud)

For a 100-host deployment over 3 years, managed cloud versions only:

The TCO gap widens if HyperDX users also need a status page solution (Statuspage.io starts at $29/month, adding $1,044 over 3 years).

For self-hosted HyperDX with an existing ClickHouse cluster and engineering capacity to maintain it, the math flips: you could genuinely run the equivalent observability stack for $300-700/month all-in. That's the use case HyperDX is optimized for.

Distributed tracing and APM

Both platforms are OpenTelemetry-native, which sets them apart from proprietary-agent tools like Datadog. The instrumentation approach is where they diverge.

Better Stack: eBPF traces and OpenTelemetry

Better Stack's APM captures traces automatically using eBPF, requiring no code changes. Once the Kubernetes DaemonSet is deployed, HTTP and gRPC traffic between services produces distributed traces immediately. Here's how trace visualization and analysis work in practice:

Frontend-to-backend correlation is native in Better Stack. When a session replay shows a slow page load, the distributed trace from the browser request through your microservices and into the database is visible in the same interface. No separate RUM product configuration. No manually wiring correlation IDs between products.

OpenTelemetry-native, zero lock-in. Traces are stored in OTel format. If you want to pipe your traces elsewhere, you change a config line. There's no migration tax, no SDK rewrite.

Better Stack also accepts standard OTel collector output, so teams already shipping traces via OpenTelemetry Collector can redirect them without changing instrumentation. Watch how OTel integration works:

HyperDX: OpenTelemetry-first tracing with ClickHouse speed

HyperDX's tracing story starts and ends with OpenTelemetry. Unlike Better Stack's eBPF approach, HyperDX requires standard OTel SDK instrumentation per service. For Python, Node.js, Go, Java, Elixir, and React Native, HyperDX provides SDK packages or accepts any OTel-compatible output directly.

The payoff is query performance. ClickHouse is genuinely fast for analytical queries over spans. Full-text search across traces uses Lucene syntax, and the interface provides automatic event clustering that groups similar spans into patterns, making it easier to identify common failure modes across thousands of traces without writing complex queries.

Session replay correlation is a standout feature. HyperDX correlates browser session replays with backend traces automatically. When a user triggers an error in the browser, you can jump directly from the session replay to the backend span that caused it. This is something Better Stack also does, but HyperDX was doing it from early versions and the UX for it is well-polished.

What HyperDX lacks: Because it relies on OTel SDKs, every uninstrumented service is invisible. In polyglot microservice environments where some services lack OTel, you'll have gaps in your trace coverage that Better Stack's eBPF approach would fill automatically. There's also no eBPF-level database query tracing; database spans require SDK-level instrumentation.

Log management

The searchability problem is the central question in log management: what percentage of your logs are actually queryable when an incident fires? Both platforms take a different cut at it.

Better Stack: SQL-queryable logs with zero indexing decisions

Better Stack logs stores all ingested logs as structured data and makes every byte of it immediately searchable. There's no "indexed vs archived" split, no upfront decisions about which logs matter. Watch live log streaming and filtering via Live Tail:

SQL querying gives familiar syntax for aggregation, grouping, and time-window analysis:

SELECT 
  service_name,
  COUNT(*) as error_count,
  AVG(duration_ms) as avg_duration
FROM logs
WHERE level = 'error'
  AND timestamp > NOW() - INTERVAL '1 hour'
GROUP BY service_name
ORDER BY error_count DESC

Results from those queries can be directly visualized as charts. Here's how to build visual charts from SQL log queries:

Better Stack also supports saved query presets for common investigation patterns:

HyperDX: ClickHouse-backed logs with intuitive search

HyperDX's log management is also fully queryable. Every log ingested into ClickHouse is searchable. The interface offers Lucene-style full-text search ("level:error service:api") which is faster to type than SQL for ad-hoc queries, with SQL available for advanced analysis.

Automatic log pattern clustering is a feature Better Stack doesn't have in the same form. HyperDX groups similar log lines into clustered patterns, condensing billions of events into distinct categories. During an incident with a log flood, this is genuinely useful: instead of scrolling through 50,000 identical timeout errors, you see one cluster entry with a count, and you can drill in from there.

Native JSON parsing without configuration is another practical strength. Drop structured JSON logs from any source and HyperDX automatically parses field names for filtering and search. Better Stack handles structured logs too, but HyperDX's self-described "no additional configuration" approach to JSON is well-regarded in user feedback.

The main gap compared to Better Stack is the self-hosted operational requirement. If you're running HyperDX Cloud, log management is effectively comparable in operational terms. If you self-host, your ClickHouse cluster's disk performance and capacity planning directly affect log query speed at scale.

Infrastructure monitoring and metrics

Cardinality-based billing is the issue that drove many teams away from Datadog toward both Better Stack and HyperDX. Both platforms solve it with volume-based pricing.

Better Stack: PromQL-native, cardinality-free metrics

Better Stack metrics charges based on data volume regardless of unique metric combinations. Add as many tags as your analysis requires without cost consequences. Full PromQL support means existing Prometheus workflows migrate without changes:

Both PromQL chart building and a drag-and-drop chart builder are available:

HyperDX: metric DPM billing

HyperDX metrics are billed per 100 data points per minute (DPM). The formula: 100 machines reporting CPU and memory every minute equals 200 DPM, charged at $0.40 per 100 DPM. Higher reporting frequency increases cost linearly, which incentivizes appropriate sampling. Lower frequency reduces cost.

This is a reasonable model, but it does introduce one dimension of cost awareness that Better Stack's flat per-GB approach avoids: you do need to think about reporting frequency when setting up exporters. Over-eager metric collection can produce surprising bill increases similar to cardinality explosions in Datadog, even if the mechanism is different.

HyperDX's metrics visualization uses an intuitive chart builder that works directly against your ClickHouse data. The graphs are fast given ClickHouse's analytical query performance, and the interface supports grouping, filtering, and time-range selection without writing SQL for most use cases.

What HyperDX doesn't have: PromQL support. If your team uses Prometheus extensively and wants to write PromQL queries directly, Better Stack is the stronger fit. HyperDX uses ClickHouse SQL, which is powerful but requires translating PromQL muscle memory.

Incident management

This is the clearest differentiation between the two platforms. Better Stack was built from the ground up as an incident management tool that added observability. HyperDX is an observability tool that integrates with incident management via PagerDuty.

Better Stack: incident management as a core product

Better Stack incident management includes on-call scheduling, escalation policies, unlimited phone and SMS alerting, Slack-native incident channels, automatic post-mortems, and AI-powered investigation, all at $29/month per responder.

Here's the complete incident lifecycle in Better Stack:

Teams that work in Slack can manage incidents end-to-end without leaving the chat interface:

On-call rotation management with timezone-aware scheduling:

Automatic post-mortem generation from incident timelines:

Advanced multi-tier escalation workflows for enterprise teams:

HyperDX: alerting with external escalation

HyperDX generates alerts on log patterns, metrics thresholds, and trace anomalies. For notification and escalation, it integrates with PagerDuty, Slack, and email. The alert configuration is solid for an observability-first tool: you can define alerts visually in the chart builder or via the API.

What HyperDX does not have: on-call scheduling, escalation policy management, phone/SMS calling, incident channel creation, post-mortems, or status pages. These are not features HyperDX is building toward; they are outside its stated scope. If you need them, you need additional tools.

For teams already running PagerDuty or OpsGenie and simply want better log and trace visibility without changing their incident workflow, HyperDX's approach is completely reasonable. For teams building their incident management stack from scratch and looking to consolidate tools, Better Stack eliminates the need for a separate escalation platform.

What's the real cost of the tools you're stitching together alongside HyperDX? PagerDuty Teams runs $49/user/month, and OpsGenie's Standard plan is $9/user/month. Over 3 years with 5 responders, that's $8,820 to $26,460 depending on PagerDuty tier, not counting the coordination overhead of running separate systems.

Session replay and real user monitoring

Session replay is where both platforms show genuine depth. HyperDX built correlated session replay as a core feature from its earliest versions. Better Stack added RUM including session replay more recently but built it directly into the same data model as backend telemetry.

Better Stack: unified RUM and backend correlation

Better Stack RUM captures JavaScript errors, Core Web Vitals (LCP, CLS, INP), session replays, product analytics, and website analytics. Because frontend events share the same data warehouse as backend logs, metrics, and traces, you can query a session replay alongside the backend trace it triggered using the same SQL syntax.

Session replay filters by rage clicks, dead clicks, errors, and frustration signals. Playback runs at 2x speed with dead-time skipping. PII exclusion happens at the SDK level.

Product analytics auto-captures user events for funnel analysis without pre-instrumentation. You define the funnels after the fact, querying historical data.

Pricing: $0.00150/session replay, no per-session indexing fees.

HyperDX: mature session replay with trace correlation

HyperDX's session replay has been a central product feature since launch, and the correlation UX between replays and backend traces is well-regarded among users. From a replay, you can navigate directly to the exact backend span that triggered a JavaScript error. The correlation is automatic.

What HyperDX's RUM includes: session replays, correlated backend traces, error tracking linked to replays, basic user session analytics. What it's lighter on compared to Better Stack: website analytics (referrers, UTM, real-time traffic sources), Core Web Vitals alerting, product analytics funnels, and mobile RUM via React Native.

For teams primarily debugging frontend errors and correlating them with backend service failures, HyperDX's replay feature is genuinely strong. For teams that also want to understand where traffic is coming from, track marketing campaigns through to conversion, and monitor Core Web Vitals with alerts, Better Stack's RUM is more comprehensive.

AI capabilities and MCP integration

AI tooling in observability is moving fast, and this is currently one of the more meaningful gaps between the two platforms.

Better Stack: AI SRE and MCP server

AI SRE activates autonomously during incidents. Rather than waiting for an engineer to open the platform and start querying, it analyzes the service map, reviews recent deployments, queries relevant logs, and generates a root cause hypothesis automatically. At 3am during an outage, you're starting from a lead rather than a blank screen.

Better Stack MCP server is generally available to all customers. It connects Claude, Cursor, and any MCP-compatible AI client directly to your observability data. Instead of copying log snippets into a chat window, your AI assistant queries Better Stack directly:

Setup is a single configuration block:

{
  "mcpServers": {
    "betterstack": {
      "type": "http",
      "url": "https://mcp.betterstack.com"
    }
  }
}

From there, natural language queries like "show me all monitors currently down," "who's on-call right now?", or "build a query to find HTTP 500 errors in the last hour" work against your live data. The MCP server covers uptime monitoring, incident management, log querying, metrics, dashboards, error tracking, and on-call scheduling.

HyperDX: no AI SRE, no MCP server

HyperDX does not currently have an AI SRE or an MCP server. The platform focuses on query speed and data visualization rather than AI-native workflows. For teams whose primary need is fast, SQL-based investigation by human engineers, this gap is irrelevant. For teams integrating AI into their incident response workflow or using Claude/Cursor as a daily debugging tool, the absence is meaningful.

Now that HyperDX is part of ClickHouse, the roadmap may include AI features, but as of now there's nothing publicly available. ClickHouse itself has some AI query assistance features in its cloud product, but these don't extend into HyperDX's observability layer.

Error tracking

Better Stack: Sentry-compatible with AI debugging

Better Stack Error Tracking accepts Sentry SDK payloads natively. If you're currently running Sentry, you can redirect your DSN to Better Stack without changing instrumentation.

AI-native debugging generates Claude Code and Cursor prompts pre-populated with the error context, stack trace, and relevant logs. Instead of reading a stack trace manually, you paste the generated prompt into your AI coding agent and it starts with full context.

Full trace context is automatic: every error links to the distributed trace that caused it, showing the complete request path from the service that threw the exception back through upstream callers.

HyperDX: integrated error tracking

HyperDX's error tracking is built into its unified view. Errors surface alongside logs and traces from the same service, and the session replay correlation means you can watch the exact user interaction that triggered a JavaScript exception. Stack traces link to source maps for cloud customers.

What HyperDX's error tracking lacks relative to Better Stack: Sentry SDK compatibility (you need to use HyperDX's own SDKs or OTel), AI-assisted debugging prompts, and escalation to incident management. Errors stay in the observability layer; there's no direct path to creating an on-call alert from an error spike.

Deployment and integration

Better Stack: Helm chart + OpenTelemetry + Vector

Deploy Better Stack's eBPF collector to Kubernetes via a single Helm chart. The DaemonSet discovers services automatically. For existing OTel pipelines, redirect your collector endpoint. For log shipping, Vector is a first-class integration:

For an overview of how data collection works end-to-end:

Integrations: Better Stack connects to 100+ covering all major stacks: MCP, OpenTelemetry, Vector, Prometheus, Kubernetes, Docker, PostgreSQL, MySQL, Redis, MongoDB, Nginx, and more.

HyperDX: OpenTelemetry collector + language SDKs

HyperDX deploys via Docker Compose (quickstart) or Kubernetes with ClickStack (production). The stack includes ClickHouse, HyperDX UI, and an OpenTelemetry Collector. Instrumentation uses OTel SDKs for the languages HyperDX supports: Node.js, Python, Java, Go, Elixir, and React Native, plus Vercel, Fly.io, Heroku, AWS EC2, Kubernetes, and Cloudflare Workers via platform-specific guides.

The agent-free approach is part of HyperDX's marketing, but this refers to not needing a vendor-specific agent running on each host. You still need to instrument your application code with OTel SDKs. For services without SDK instrumentation, there's no automatic discovery equivalent to Better Stack's eBPF approach.

For teams already using OpenTelemetry Collector, pointing it at HyperDX's OTLP endpoint is genuinely simple. No SDK changes, no code modifications. This is the fastest migration path for teams already on the OTel ecosystem.

Status pages

HyperDX has no status page feature. This is a complete gap.

Better Stack: built-in status pages

Better Stack Status Pages is included in the platform and syncs automatically with incident management.

When an incident fires, your status page updates automatically. Subscriber notifications go out via email, SMS, Slack, and webhooks. Custom branding, custom domains, password protection, and SAML SSO for private pages are all included.

Pricing: $12-208/month for advanced features, included with Better Stack's incident management at no additional platform cost.

HyperDX: no status page capability

Teams using HyperDX who need public status pages must use a separate tool (Statuspage, Instatus, or similar). This adds cost and breaks the incident-to-communication workflow that Better Stack makes automatic.

Enterprise readiness

Both platforms have SOC 2 Type II certification. Beyond that, the enterprise feature sets diverge based on the products' different maturity levels.

Better Stack covers the requirements most enterprise procurement processes need: SOC 2 Type II, GDPR compliance, SSO via Okta/Azure/Google (SAML/OIDC), SCIM provisioning, RBAC, audit logs, data residency in EU and US regions, optional S3 data export, a dedicated Slack support channel, a named account manager, and enterprise SLAs.

HyperDX's enterprise features are available on the cloud plan: SAML SSO, custom retention, and volume discounts. The self-hosted version has no enterprise SSO in the open-source build. SCIM, audit logs, and dedicated support are not listed as available features. Given the acquisition by ClickHouse, the enterprise roadmap may expand, but current enterprise capabilities are thinner than Better Stack's.

For regulated industries: neither platform is HIPAA or FedRAMP compliant. If you're in healthcare or government, both platforms have the same gap. Datadog remains the choice if those certifications are hard requirements.

Final thoughts

Better Stack and HyperDX share the same core philosophy: OpenTelemetry-native observability, volume-based pricing, and a unified view across logs, traces, session replay, and errors. Both are modern alternatives to expensive, fragmented observability stacks.

The difference is less about telemetry and more about how much infrastructure and operational complexity your team wants to own.

HyperDX is a strong fit for teams already invested in ClickHouse or committed to a fully open-source stack. If you are comfortable operating your own infrastructure and already rely on tools like PagerDuty or OpsGenie for incident response, HyperDX gives you flexibility and control with minimal vendor lock-in.

Better Stack focuses on removing that operational burden instead. It combines observability, on-call scheduling, incident management, status pages, AI-assisted investigation, and an MCP server in one fully managed platform. With eBPF-based auto-instrumentation, teams can get visibility across services without maintaining SDKs, while the integrated workflow reduces the need to coordinate across multiple tools during incidents.

The trade-off is straightforward. HyperDX gives you maximum flexibility and ownership.

Better Stack gives you a complete, production-ready operational system with less infrastructure to manage and fewer moving parts during incidents.

For most teams, especially those prioritizing speed, simplicity, and operational efficiency, Better Stack is the more practical choice.

You can explore it here: https://betterstack.com or connect your AI tools directly through the MCP server: https://betterstack.com/docs/getting-started/integrations/mcp/

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