<palign="center">Monitor your applications and troubleshoot problems in your deployed applications, an open-source alternative to DataDog, New Relic, etc.</p>
👉 You can see metrics like p99 latency, error rates for your services, external API calls and individual end points.
👉 You can find the root cause of the problem by going to the exact traces which are causing the problem and see detailed flamegraphs of individual request traces.
- Unified UI for metrics, traces and logs. No need to switch from Prometheus to Jaeger to debug issues, or use a logs tool like Elastic separate from your metrics and traces stack.
- Run aggregates on trace data (events/spans) to get business relevant metrics. e.g. You can get error rate and 99th percentile latency of `customer_type: gold` or `deployment_version: v2` or `external_call: paypal`
Being developers, we found it annoying to rely on closed source SaaS vendors for every small feature we wanted. Closed source vendors often surprise you with huge month end bills without any transparency.
We wanted to make a self-hosted & open source version of tools like DataDog, NewRelic for companies that have privacy and security concerns about having customer data going to third party services.
Being open source also gives you complete control of your configuration, sampling, uptimes. You can also build modules over SigNoz to extend business specific capabilities
We support [OpenTelemetry](https://opentelemetry.io) as the library which you can use to instrument your applications. So any framework and language supported by OpenTelemetry is also supported by SigNoz. Some of the main supported languages are:
Prometheus is good if you want to do just metrics. But if you want to have a seamless experience between metrics and traces, then current experience of stitching together Prometheus & Jaeger is not great.
Our goal is to provide an integrated UI between metrics & traces - similar to what SaaS vendors like Datadog provides - and give advanced filtering and aggregation over traces, something which Jaeger currently lack.
Moreover, SigNoz has few more advanced features wrt Jaeger:
- Jaegar UI doesn’t show any metrics on traces or on filtered traces
- Jaeger can’t get aggregates on filtered traces. For example, p99 latency of requests which have tag - customer_type='premium'. This can be done easily on SigNoz
We have published benchmarks comparing Elastic with SigNoz. Check it out [here](https://signoz.io/blog/logs-performance-benchmark/?utm_source=github-readme&utm_medium=logs-benchmark)
- SigNoz supports aggregations on high-cardinality data over a huge volume while loki doesn’t.
- SigNoz supports indexes over high cardinality data and has no limitations on the number of indexes, while Loki reaches max streams with a few indexes added to it.
- Searching over a huge volume of data is difficult and slow in Loki compared to SigNoz
We have published benchmarks comparing Loki with SigNoz. Check it out [here](https://signoz.io/blog/logs-performance-benchmark/?utm_source=github-readme&utm_medium=logs-benchmark)
You can find docs at https://signoz.io/docs/. If you need any clarification or find something missing, feel free to raise a GitHub issue with the label `documentation` or reach out to us at the community slack channel.
Join the [slack community](https://signoz.io/slack) to know more about distributed tracing, observability, or SigNoz and to connect with other users and contributors.
