Better Stack vs Checkly: A Complete Comparison for 2026

Checkly does one thing better than most tools in the observability space: it lets developers write, version, and deploy synthetic monitors as code using Playwright and TypeScript, right from their IDE, alongside the application code they're already shipping. For teams whose reliability story is "test critical user journeys before and after every deploy," that's a genuinely strong proposition.

But synthetic monitoring is a slice of observability, not the whole picture. What happens when a check fires and you need logs, metrics, traces, and on-call coverage to resolve it? Checkly hands you the alert and points you elsewhere. Better Stack is built for that full loop: synthetic checks, uptime monitors, distributed tracing, log management, infrastructure metrics, error tracking, incident management, and status pages in one platform, with a unified pricing model that doesn't multiply costs as you expand coverage.

This comparison covers both platforms across every major capability area. Where Checkly genuinely leads, it's worth saying so. Where Better Stack covers ground Checkly doesn't, that gap matters for evaluation.

Quick comparison at a glance

Platform scope

The first question for any evaluation is whether the platform matches the problem you're trying to solve. These two tools have different answers to that question.

Better Stack: full-stack observability

Better Stack covers the full reliability surface: eBPF-based automatic instrumentation captures logs, metrics, and distributed traces from your infrastructure without code changes. That data lands in a unified warehouse, queryable via SQL or PromQL, alongside uptime monitors, incident management workflows, and status pages. When an alert fires, you're working in one interface, with all relevant context, rather than assembling a picture from separate tools.

The architecture deliberately sidesteps the "observability tax" that builds up when platforms charge separate rates for each data type. One ingestion pipeline, one storage layer, one query language, one bill.

Checkly: synthetic monitoring and reliability testing

Checkly is built around a specific philosophy: monitoring should live in your repository, be written in code, and deploy alongside your application. The Checkly CLI lets you define API checks, Playwright browser checks, and uptime monitors as TypeScript constructs, version them in Git, and push them to Checkly's global infrastructure with a single command.

This model enables monitoring of critical user flows in high intervals from headless browsers and gives teams confidence that applications work correctly from both technical and user experience perspectives. The monitoring-as-code workflow is a real differentiator for platform engineering teams who want monitoring to behave like infrastructure, with pull requests, code reviews, and CI/CD integration built in.

What Checkly is not is a general-purpose observability platform. It has no log management, no infrastructure metrics, no incident management, and no on-call scheduling. When a Playwright check fails, Checkly tells you what failed and gives you Rocky AI's analysis of why. Resolving it still requires separate tools for the deeper investigation.

Pricing comparison

Checkly's pricing is structured by plan tier with check run allowances per month. Better Stack's pricing is based on data volume and responder count. Neither model is inherently better, but the way costs scale at growth is quite different.

Better Stack: volume-based, no hidden multipliers

Better Stack charges on actual data volume rather than plan tiers or check counts. The formula is straightforward: GB ingested, GB stored, responders for incident alerting, and monitors.

Pricing structure:

• Logs: $0.10/GB ingestion + $0.05/GB/month retention (100% searchable, no indexing fees)
• 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

Costs scale linearly with actual usage. Adding tags to a metric doesn't change the bill. Indexing more logs doesn't require a pricing conversation. If you have a traffic spike, you don't carry elevated costs for the rest of the month (no high-water mark billing).

Checkly: tier-based by check run volume

Checkly's plans are built around check run volumes: Browser/Playwright runs and API check runs are the primary billing dimensions, with add-on packs available when you exceed plan limits.

Pricing structure (annual billing):

• Hobby: $0/month (10 uptime monitors, 1k browser runs, 10k API runs, 6 locations)
• Starter: $24/month (50 uptime monitors, 3k browser runs, 25k API runs, 6 locations)
• Team: $64/month (75 uptime monitors, 12k browser runs, 100k API runs, 22 locations)
• Enterprise: Custom pricing

Status pages and AI analysis (Rocky AI) are purchased on separate plans with separate monthly fees: - Status pages: Hobby $0 / Starter $9 / Team $30 / Enterprise custom - Resolve (Rocky AI): Hobby $0 / Starter $12 / Team $39 / Enterprise custom

A team on the Team plan with status pages and Rocky AI enabled pays $64 + $30 + $39 = $133/month before accounting for check overages.

Overage pricing: Browser check overages are $6.25-6.50 per 1k runs, API check overages are $2.50-2.60 per 10k runs. G2 reviewers have noted that pricing tiers can feel rigid for growing teams that fall between the Starter and Team plan limits, and more flexible usage-based billing would be helpful.

Cost comparison: 3-year TCO

For a growing engineering team running full observability (uptime, synthetics, logs, metrics, traces, incidents, status pages) over 3 years:

The honest framing: if your needs are pure synthetic monitoring with no log management or incident management, Checkly's pricing is competitive. The gap opens when you need full observability coverage.

Synthetic monitoring

Synthetic monitoring is Checkly's competitive core, and it's genuinely excellent. Better Stack has uptime monitoring and is building out Playwright-based synthetics, but it's not yet at Checkly's depth in this specific area.

Better Stack: uptime and growing synthetic coverage

Better Stack monitors cover HTTP/HTTPS, TCP, DNS, keyword monitoring, and SSL certificate tracking. These handle the standard "is my service up" question reliably across multiple global locations, with configurable alert thresholds and escalation.

The platform includes API checks and is expanding into Playwright-based browser checks, but if your requirements center on complex scripted user flows, visual regression testing, or the monitoring-as-code workflow Checkly has built, Better Stack isn't at feature parity there yet.

What Better Stack adds around its uptime monitoring is the rest of the observability stack. An uptime alert fires and immediately links to logs, traces, and error data from the same incident. You're not leaving the platform to investigate. Does that integration matter more to your team than deep scripted browser testing capability?

Checkly: Playwright-native synthetic monitoring

Checkly provides browser checks based on Playwright for deep synthetic testing: end-to-end user flows, login sequences, multi-step transactions, and form submissions from real browsers. API checks handle request-response validation, and multistep checks enable sequential API workflows with data passing between steps.

Monitoring as code is the architectural differentiator. Define a Playwright check in TypeScript:

import { BrowserCheck, Frequency } from "checkly/constructs"

new BrowserCheck('checkout-flow', {
  name: 'Checkout flow',
  frequency: Frequency.EVERY_5M,
  locations: ['us-east-1', 'eu-west-1'],
  code: {
    entrypoint: './checks/checkout.spec.ts'
  }
})

Commit it, open a PR, and it deploys through your standard review process. G2 reviewers consistently praise this as one of Checkly's strongest features: the ability to write API and browser checks in JS/TS using Playwright, commit them to Git, and run them globally feels like an extension of the development process rather than a separate tool to manage.

Visual regression testing prevents UI issues with layout, color schemes, and content by comparing screenshots against baselines. Automatic retries and parallel scheduling across all 22 global locations are available on Team and Enterprise plans.

Private locations let you run checks inside your own infrastructure against services that aren't publicly accessible.

CI/CD-native: Checkly integrates directly with GitHub Actions, GitLab CI, Jenkins, and other pipelines. Run checks against preview environments before production deployments.

The platform supports four check types: API checks, multistep API checks, Playwright browser checks, and Playwright Check Suites that let you import and run existing test suites with no rewriting.

Tracing

Checkly added an OpenTelemetry-native Traces product that connects synthetic check failures to the backend traces they generate. Better Stack's APM is eBPF-based: no code changes required, full distributed tracing from day one.

Better Stack: eBPF-based APM

Better Stack's APM deploys via a single Helm chart and automatically captures HTTP/gRPC traffic, database calls (PostgreSQL, MySQL, Redis, MongoDB), and service-to-service communication without touching application code. Every service in a Kubernetes cluster is traced from the moment the collector DaemonSet starts.

Frontend-to-backend correlation connects what users experience in the browser (from Better Stack's RUM) with what's happening in your backend services, in one view without switching products or manually stitching context.

OpenTelemetry-native, zero lock-in. Traces use the OTel format natively. Your instrumentation strategy isn't tied to Better Stack's proprietary agent; change a configuration line and your traces go elsewhere. No migration tax accumulates.

For polyglot environments running Python, Go, Java, Ruby, and Node.js side by side, the zero-code approach removes the per-language SDK maintenance overhead that agent-based instrumentation always brings.

Checkly: OTel traces for synthetic check correlation

Checkly's Traces product accepts OpenTelemetry trace data from your application and correlates it with Playwright and API check runs, so when a synthetic check fails you can see the backend trace that the check triggered. This is a genuinely useful capability for teams that already emit OTel traces and want to understand what the check was doing on the backend when it failed.

The setup involves pointing your OTel exporter at Checkly's endpoint:

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({
    url: 'https://otel.eu-west-1.checklyhq.com',
    // headers with API key
  })
})

The traces product is currently in Enterprise plan access, with early access available on request for other plans. It does not provide full APM: there's no eBPF-based auto-discovery, no automatic database tracing, no service map built from continuous instrumentation. The traces it shows are the ones generated by your synthetic checks.

Uptime monitoring

Both platforms run checks from multiple global locations and alert on failures. The differences show up in frequency options, protocol coverage, and what happens after an alert fires.

Better Stack

Better Stack uptime monitoring checks HTTPS, TCP, DNS, keyword presence, and heartbeat endpoints. Configure alerts in under a minute, and when something degrades, you're looking at the same interface that shows your logs and traces. No tab-switching to investigate.

Incident management is built in: when an uptime check fires, it opens an incident, pages the on-call rotation, and creates a Slack channel for the investigation, all automatically. The status page updates simultaneously. Resolving the alert closes the incident and notifies subscribers.

Checkly

Checkly's uptime monitoring supports HTTPS, TCP, DNS, ICMP, and Heartbeat protocols across all global locations, with automatic retries to reduce false positives and configurable alert channels including email, Slack, SMS, phone, and webhooks.

The protocol coverage is solid. What Checkly doesn't provide is native incident management when those checks fire. Connecting a Checkly alert to an on-call rotation means integrating PagerDuty, OpsGenie, Incident.io, or Rootly as a separate tool. That integration works, but it adds a dependency, a separate bill, and a handoff point where context can get lost.

Alerting

Both platforms send alerts via email, Slack, SMS, and webhooks. The routing logic and what happens next differ significantly.

Better Stack

Better Stack alerting routes alerts to the right person based on on-call schedules, with unlimited phone and SMS calls included at $29/responder/month. Multi-tier escalation policies, time-based rules, and metadata filters are all built in without a third-party tool.

On-call scheduling supports rotation management, timezone-aware handoffs, and override rules for holidays or coverage gaps. Watch how to configure on-call rotations:

Checkly

Checkly's alerting integrates with email, Slack, webhooks, SMS, phone, PagerDuty, OpsGenie, Incident.io, Rootly, and Microsoft Teams. Alerts include detailed check result data and links to trace artifacts.

The Starter plan includes 100 SMS/month and no phone calls. The Team plan includes 200 SMS and 200 phone alerts. For teams that need reliable on-call phone escalation beyond those limits, integration with PagerDuty or OpsGenie is the intended path.

Rocky AI root cause analysis results can now be sent directly to alert channels: when a check fails, the analysis fires automatically and the RCA summary lands in Slack, Teams, or email alongside the standard alert. That's a genuinely useful feature for triage speed.

Rocky AI analysis vs Better Stack AI SRE

Both platforms have AI features aimed at accelerating failure investigation. They operate at different points in the reliability workflow.

Better Stack: AI SRE

AI SRE activates autonomously during incidents. It queries your service map, scans recent logs, reviews deployment history, and produces a probable root cause hypothesis without you prompting it manually. During a 3am page, you're not starting from a blank terminal; you're validating or refuting a hypothesis that was waiting when you opened the incident.

The AI SRE operates across the full observability dataset: logs, metrics, traces, and service topology. That breadth is what makes autonomous investigation possible. An AI that can only see synthetic check artifacts can only reason about synthetic check failures.

Better Stack also has an MCP server that connects Claude, Cursor, or any MCP-compatible AI client directly to your observability data. Query logs with ClickHouse SQL through natural language, check who's on-call, acknowledge incidents, or build dashboard queries from your IDE:

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

Checkly: Rocky AI root cause analysis

Rocky AI is Checkly's AI agent, designed to automatically determine user impact and root cause on any failing check using error messages, code, metrics, traces, and logs.

Rocky AI's automated RCA can analyze any failing Playwright, API, multistep, TCP, DNS, and ICMP check. It breaks the failure into logical chunks, starting from the user perspective, through the test steps to the underlying root cause, and investigates artifacts including Playwright trace files, binary PCAP files, trace routes, and ICMP logs.

By default, Checkly uses OpenAI's GPT-5.1 model for AI features, but you can bring your own model and provider via a custom AI provider configuration. That bring-your-own-model option is a genuine differentiator for organizations that can't send data to specific third-party AI providers.

Rocky AI ships code fix suggestions for TypeScript-based checks, meaning when a Playwright or API check fails for reasons Rocky can diagnose, it will suggest the corrected TypeScript code alongside the failure analysis.

The constraint is scope: Rocky can only analyze what Checkly sees, which is synthetic check data. It cannot correlate a check failure with a spike in infrastructure metrics, a log line from a dependent service, or a deployment event that happened 10 minutes before. That correlation requires external tools.

Checkly's broader AI story includes agent skills for the CLI, which let AI tools like Cursor and Copilot configure, test, and deploy monitoring infrastructure through natural language prompts from your IDE, without switching to a separate UI.

Status pages and dashboards

Better Stack

Better Stack Status Pages are included with the incident management platform and sync automatically with your incident timeline. Public and private pages, custom domains, full CSS control, and multi-channel subscriber notifications (email, SMS, Slack, webhooks) are all built in.

Status page updates happen automatically as incident status changes, so the team managing the incident doesn't need to remember to update subscribers separately.

Checkly

Checkly status pages automatically reflect monitoring results, creating a professional interface for incident communication. Plans support 20-100 services and 250-2,000 subscribers depending on tier, with custom domain support on paid plans and custom CSS and password protection on Team and above.

Heartbeat monitors can now be linked to a Checkly Status Page, so incidents are automatically created when a check fails. Status page subscriber management is available directly in the web interface and via API.

The status page feature is competent. Note that it's a separate billing plan: $9/month (Starter), $30/month (Team). Dashboards for visualizing monitoring metrics are also on this plan, with 1 dashboard on Starter and 10 on Team.

Log management

This is the clearest gap in the Checkly feature set. Checkly has no log management product. When you need logs to investigate a check failure, you're leaving Checkly and going to a separate log aggregation tool.

Better Stack

Better Stack logs indexes 100% of ingested log data, immediately searchable via SQL. No decision required about what to index, no archived logs you can't search during an incident.

The pricing is transparent: $0.10/GB ingestion plus $0.05/GB/month retention. 100GB/month costs $15 total. Logs, metrics, and traces share the same warehouse, so a SQL query can join log events with trace spans in the same statement.

Checkly

No log management. This isn't a missing feature on the roadmap; it's a scope decision. Checkly is a synthetic monitoring and reliability testing tool, not an observability platform.

For teams that need log aggregation, the common pairings are Checkly + Datadog, Checkly + Better Stack (logs only), or Checkly + a hosted ELK/OpenSearch setup. Each adds cost and a context-switching step in incident investigation.

Infrastructure monitoring

Same story as log management. Checkly has no infrastructure metrics product. If you need host-level CPU, memory, network, and disk metrics, that's a separate tool.

Better Stack

Better Stack infrastructure monitoring is Prometheus-compatible, with no cardinality penalties on metric pricing. Add high-cardinality tags freely without triggering unexpected bill increases.

PromQL queries work natively. Drag-and-drop chart builder for teams who prefer visual dashboards. All metrics sit alongside logs and traces in the same interface, so connecting an alert on api_request_latency to the logs and traces from the same time window is a single view rather than a cross-tool exercise.

Checkly

Checkly's Prometheus endpoint integration is available on Team and Enterprise plans, allowing teams to export Checkly monitoring metrics (check results, response times, availability percentages) into an existing Prometheus or Grafana setup.

Exporting Checkly's own monitoring metrics is not the same as collecting infrastructure metrics. Checkly has no agent, no host-level collection, and no metrics storage.

Incident management

The absence of native incident management in Checkly is arguably the largest gap for teams operating production systems. When a check fires at 3am, Checkly alerts you. Everything that happens next, paging the right person, managing the escalation, coordinating the response in Slack, updating the status page, and writing the post-mortem, requires other tools.

Better Stack

Better Stack incident management covers the full on-call lifecycle. At $29/responder/month with unlimited phone and SMS, five responders costs $145/month with no additional per-call fees and no third-party tool required.

Slack and Teams-native incidents create a dedicated channel per incident with investigation tools embedded:

Post-mortems generate automatically from the incident timeline:

Checkly

Checkly integrates with PagerDuty, OpsGenie, Incident.io, Rootly, and Microsoft Teams for alerting and incident workflows. These integrations are reliable and well-documented. You're not blocked from building a solid on-call workflow; you're just building it outside Checkly with a separate tool.

For 5 responders, PagerDuty runs $245-415/month on top of your Checkly subscription. OpsGenie is similar. Does your team want incident management as a first-class product that shares context with your monitoring data, or is a webhook to an external tool sufficient?

Deployment and integrations

Better Stack

Deploy the eBPF collector via Helm chart. One DaemonSet across the cluster begins capturing traces, logs, and metrics automatically. No per-service SDK installation, no per-language version management.

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

Checkly

Checkly integrates natively with the open source and developer tools teams already use: CI/CD pipelines (GitHub Actions, GitLab, Jenkins), alerting tools (PagerDuty, OpsGenie, Incident.io), collaboration tools (Slack, Teams, Telegram), and observability backends (Coralogix, Prometheus, Grafana) via webhooks and exporters.

The Terraform provider and Pulumi provider let platform engineering teams manage Checkly monitoring infrastructure alongside other cloud resources. The Checkly CLI handles the day-to-day development workflow.

Checkly's monitoring-as-code approach makes it immediately usable with AI coding tools. Because checks live in the repository as TypeScript files, Cursor, GitHub Copilot, and other agents can read the codebase and generate new monitors, update configurations, or refactor checks through natural language instructions without any special plugin.

Enterprise readiness

Better Stack enterprise features

Better Stack meets standard enterprise procurement requirements: SOC 2 Type II, GDPR compliance, SSO via Okta, Azure, and Google, SCIM provisioning, RBAC, audit logs, and optional data residency in EU or US regions. Enterprise customers also get optional self-hosted data storage in their own S3 bucket.

Support at the enterprise tier includes a dedicated Slack channel and a named account manager. When something breaks, you have a direct line to someone who knows your account.

Checkly enterprise features

Checkly's Enterprise plan includes SAML/SSO, SOC 2 Type II compliance, 99.9% uptime SLA, client certificates, a dedicated customer success engineer, onboarding support, 24/7 phone escalation, and security review availability. SAML/SSO is Enterprise-only; it's not available on Team or Starter plans.

G2 reviewers have noted limited data residency options as a concern, particularly for EU-based teams with strict compliance requirements around where monitoring data is stored.

Final thoughts

If your priority is ensuring critical user flows work correctly in production, **Checkly is a good option. Its monitoring-as-code approach with Playwright integrates directly into your CI/CD workflow, making it ideal for teams that ship frequently and want testing and monitoring to move with their code. For developer-first teams focused on synthetic checks, it is one of the best tools available.

Better Stack operates at a broader level. It is the better fit when you need full observability and incident response in one place. Instead of jumping between logs, metrics, APM, and on-call tools, you get correlated data, AI-driven investigation, and the full resolution workflow in a single platform.

There is also a clear difference in cost structure. Better Stack’s volume-based pricing is predictable, avoiding the complexity of multiple tools and billing models.

You can try it here: https://betterstack.com

Got an article suggestion? Let us know

Explore more

Better Stack vs groundcover: A Complete Comparison for 2026

Better Stack vs groundcover compared across pricing, eBPF APM, logs, incident management, AI SRE, and BYOC architecture to help you pick the right observability platform in 2026.

Better Stack vs Honeycomb

Better Stack and Honeycomb both offer unified telemetry with no cardinality penalties, but Better Stack adds incident management, status pages, RUM with session replay, and error tracking in one platform. This comparison covers architecture, pricing, tracing, logs, metrics, AI capabilities, and enterprise readiness so you can decide which fits your team

Better Stack vs Logz.io: Full comparison for 2026

Better Stack vs Logz.io compared across logs, metrics, tracing, pricing, incident management, AI, SIEM, and more. See which observability platform fits your team

Better Stack vs SigNoz: a complete comparison for 2026

A detailed comparison of Better Stack and SigNoz covering architecture, pricing, distributed tracing, log management, infrastructure monitoring, incident management, RUM, AI features, and enterprise readiness.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.