---
title: Guaranteed Availability
sidebarTitle: Guaranteed Availability
description: Ensure 100% availability of prompts by pre-fetching them on application startup and providing a fallback prompt.
---

# Guaranteed Availability

Implementing this is usually not necessary as it adds complexity to your application. The Langfuse Prompt Management is highly available due to multiple [caching layers](/docs/prompt-management/features/caching) and we closely monitor its performance ([status page](https://status.langfuse.com)). However, if you require 100% availability, you can use the following options.

The Langfuse API has high uptime and prompts are [cached locally](/docs/prompt-management/features/caching) in the SDKs to prevent network issues from affecting your application.

However, `get_prompt()`/`getPrompt()` will throw an exception if:

- No local (fresh or stale) cached prompt is available -> new application instance fetching prompt for the first time
- _and_ network request fails -> networking or Langfuse API issue (after retries)

To guarantee 100% availability, there are two options:

1. Pre-fetch prompts on application startup and exit the application if the prompt is not available.
2. Provide a `fallback` prompt that will be used in these cases.

## Option 1: Pre-fetch prompts

Pre-fetch prompts on application startup and exit the application if the prompt is not available.

<LangTabs items={["Python (Flask)", "JS/TS (Express)"]}>
<Tab>

```python
from flask import Flask, jsonify
from langfuse import Langfuse

# Initialize the Flask app and Langfuse client
app = Flask(__name__)
langfuse = Langfuse()

def fetch_prompts_on_startup():
    try:
        # Fetch and cache the production version of the prompt
        langfuse.get_prompt("movie-critic")
    except Exception as e:
        print(f"Failed to fetch prompt on startup: {e}")
        sys.exit(1)  # Exit the application if the prompt is not available

# Call the function during application startup
fetch_prompts_on_startup()

@app.route('/get-movie-prompt/<movie>', methods=['GET'])
def get_movie_prompt(movie):
    prompt = langfuse.get_prompt("movie-critic")
    compiled_prompt = prompt.compile(criticlevel="expert", movie=movie)
    return jsonify({"prompt": compiled_prompt})

if __name__ == '__main__':
    app.run(debug=True)
```

</Tab>
<Tab>

```ts
import express from "express";
import { LangfuseClient } from "@langfuse/client";

// Initialize the Express app and Langfuse client
const app = express();
const langfuse = new LangfuseClient();

async function fetchPromptsOnStartup() {
  try {
    // Fetch and cache the production version of the prompt
    await langfuse.prompt.get("movie-critic");
  } catch (error) {
    console.error("Failed to fetch prompt on startup:", error);
    process.exit(1); // Exit the application if the prompt is not available
  }
}

// Call the function during application startup
fetchPromptsOnStartup();

app.get("/get-movie-prompt/:movie", async (req, res) => {
  const movie = req.params.movie;
  const prompt = await langfuse.prompt.get("movie-critic");
  const compiledPrompt = prompt.compile({ criticlevel: "expert", movie });
  res.json({ prompt: compiledPrompt });
});

app.listen(3000, () => {
  console.log("Server is running on port 3000");
});
```

</Tab>
</LangTabs>

## Option 2: Fallback [#fallback]

Provide a fallback prompt that will be used in these cases:

<LangTabs items={["Python SDK", "JS/TS SDK"]}>
<Tab>

```python /fallback="Do you like {{movie}}?"/ /fallback=[{"role": "system", "content": "You are an expert on {{movie}}"}]/
from langfuse import Langfuse
langfuse = Langfuse()

# Get `text` prompt with fallback
prompt = langfuse.get_prompt(
  "movie-critic",
  fallback="Do you like {{movie}}?"
)

# Get `chat` prompt with fallback
chat_prompt = langfuse.get_prompt(
  "movie-critic-chat",
  type="chat",
  fallback=[{"role": "system", "content": "You are an expert on {{movie}}"}]
)

# True if the prompt is a fallback
prompt.is_fallback
```

</Tab>
<Tab>

```ts /fallback: "Do you like {{movie}}?"/ /fallback: [{ role: "system", content: "You are an expert on {{movie}}" }]/
import { LangfuseClient } from "@langfuse/client";

const langfuse = new LangfuseClient();

// Get `text` prompt with fallback
const prompt = await langfuse.prompt.get("movie-critic", {
  fallback: "Do you like {{movie}}?",
});

// Get `chat` prompt with fallback
const chatPrompt = await langfuse.prompt.get("movie-critic-chat", {
  type: "chat",
  fallback: [{ role: "system", content: "You are an expert on {{movie}}" }],
});

// True if the prompt is a fallback
prompt.isFallback;
```

</Tab>
</LangTabs>

<!-- 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/docs/prompt-management/features/guaranteed-availability.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>.