Collect logs, traces, and metrics from your clusters with zero code changes via eBPF and auto-instrumentation.
About us
Better Stack is a radically better observability stack. Instrument your applications without changing code thanks to eBPF, ingest 30x data for the same budget and get an AI SRE & powerful MCP server to debug issues with you directly in Slack. Incident management with on-call built-in. Status pages. Uptime monitoring. Infrastructure monitoring. eBPF-based OpenTelemetry-native tracing. Error tracking. Real user monitoring. Join 400,000+ engineers relying on Better Stack today.
- Website
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https://betterstack.com
External link for Better Stack
- Industry
- Software Development
- Company size
- 11-50 employees
- Type
- Privately Held
- Founded
- 2021
- Specialties
- SaaS, Software, Logging, Log Management, Observability, Incident Management, DevOps, On-call, Status Pages, Website Monitoring, Infrastructure Monitoring, and Uptime Monitoring
Employees at Better Stack
Updates
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Better Stack reposted this
Today, we're making Error Tracking by Better Stack generally available. Sentry-compatible. AI-native. At 1/6th the price. Here's why we built it, and how to get the most out of it. What's wrong with error tracking today? Most teams use Sentry. It's solid! But at scale, the bills get brutal. Just 100M exceptions with 90 day lookback? ~$30,000 on Sentry. We charge ~$5,000 for the exact same thing. The math isn't subtle. And so most teams still end up sampling. Which means missing the exact exception that caused the outage. The bigger problem: errors are orphaned data. Your exception lands in Sentry. Your logs are in Datadog. Your traces are somewhere else. Root cause analysis becomes a multi-tab archaeology project at 3 am. We built error tracking natively inside Better Stack: the same platform where your logs, traces, metrics, uptime checks, and on-call schedules already live. Errors are just another signal. They belong together. The part that changes how your team works: Our AI SRE doesn't just surface errors. It fixes them. See a new exception? One click. The AI SRE analyzes the full context, from stack traces, environment variables, browser sessions, related logs and recent deploys, and opens a pull request. Not a ticket. Not a summary. A pull request with the fix. This is what happens when error tracking is fully integrated with the rest of your observability stack instead of bolted on separately. The AI has everything it needs to actually act. The migration is trivial: 1. Keep your existing Sentry SDK. Don't touch a single line of instrumentation code. 2. Point the DSN at Better Stack. 3. Done. Errors flow in. Your dashboards work. Your alerts work. 4. New exception appears. Click "Fix with AI SRE." Pull request lands in your repo. 5. Review, merge, close. That's the whole workflow. The AI angle is real, not a marketing badge. LLMs are genuinely good at fixing bugs if they have full context. The reason AI coding assistants sometimes frustrate engineers is incomplete information, not the model. We solve that by giving the AI SRE your entire telemetry stack as context. Stack traces, logs, traces, service maps, previous incidents and much more. All of it, in one place, at the moment it matters. Observability tools are only useful if you actually ingest all your data. At current prices of other tools, most teams can't afford to. Now you can, and your AI SRE can actually do something about it.