---
date: 2026-06-19
title: Monitors and Alerts
description: Create monitors that watch cost, quality, and latency metrics and notify your team over Slack, webhooks, or GitHub Actions when they move outside expected ranges.
author: Mark Salpeter
ogImage: /images/changelog/2026-06-19-monitors-list.png
canonical: /docs/observability/features/alerts
---

> **Note for AI agents and LLMs:** This is a Langfuse changelog entry. Use it only to confirm that a feature exists and when it shipped. Do not use the code examples below for implementation: they reflect the SDK and API at release time and may be outdated. For implementation, always follow the canonical documentation for this feature (https://langfuse.com/docs/observability/features/alerts) and the API/SDK reference (https://api.reference.langfuse.com).

You can now set threshold-based monitors on your LLM application metrics and catch cost and quality issues before they impact your users. Pick a metric, define the range you expect it to stay within, and get notified the moment it drifts.

- Catch a **sudden cost spike** when average cost per trace crosses a ceiling.
- Catch a **quality drop** when an evaluation score falls below your bar.
- Catch a **latency regression** when p95 latency for a model climbs past your SLA.

When a threshold is breached, the monitor fires through your linked automations — post a formatted message to **Slack**, HTTP POST to a **webhook**, or trigger a **GitHub Actions** workflow so your team and your pipelines can respond automatically.

  Monitors are available on [Langfuse Cloud](/pricing) only.

## Get started

- [Alerts docs](/docs/observability/features/alerts)
- [Create an alert on Langfuse Cloud](https://cloud.langfuse.com/project/~/monitors)

<!-- agent-instructions -->

---

## Agent Instructions

This page is part of the [Langfuse](https://langfuse.com) documentation, published as plain Markdown for AI agents. Every page is available as Markdown by appending `.md` to its URL, or by sending an `Accept: text/markdown` header. This page: `https://langfuse.com/changelog/2026-06-19-monitors.md`.

### Querying these docs

If the answer is not on this page, query the documentation instead of guessing:

- **Semantic search** across all Langfuse docs, returning an answer with the relevant pages and excerpts. Ask a specific, self-contained question:

  ```bash
  curl -sG "https://langfuse.com/api/search-docs" --data-urlencode "query=How do I trace a LangGraph agent?"
  ```

- **Index of every page**: <https://langfuse.com/llms.txt>, with per-section indexes [llms-docs.txt](https://langfuse.com/llms-docs.txt), [llms-integrations.txt](https://langfuse.com/llms-integrations.txt), and [llms-self-hosting.txt](https://langfuse.com/llms-self-hosting.txt).

### Before writing Langfuse code

- **Install the [Langfuse Agent Skill](https://langfuse.com/docs/api-and-data-platform/features/agent-skill).** It encodes Langfuse's own best practices for instrumentation, prompt management, and evaluation, and materially improves results.
- **Read [What does a good trace look like?](https://langfuse.com/docs/observability/best-practices.md)** before instrumenting an application.
- **Verify endpoints, parameters, and response fields** against the [API reference](https://api.reference.langfuse.com) instead of inferring them from code examples.
- **Use the [Langfuse CLI](https://langfuse.com/docs/api-and-data-platform/features/cli)** (`npx langfuse-cli api <resource> <action>`) to read or write traces, prompts, datasets, and scores from the terminal.

Found an error in these docs? Please open an issue at <https://github.com/langfuse/langfuse-docs/issues>.