> ## Documentation Index
> Fetch the complete documentation index at: https://docs.requesty.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Performance Monitoring

> Track AI model latency, error rates, and reliability metrics in real-time across all providers

Monitor the performance of every AI model you use, latency, error rates, throughput, and reliability, all from the Requesty analytics dashboard.

<Note>
  **[Monitor model performance](https://app.requesty.ai/model-analytics)** in the Requesty Console.
</Note>

## Latency Tracking

The **General tab** shows a real-time latency chart with three views:

| Metric      | What it measures                                 |
| ----------- | ------------------------------------------------ |
| **Average** | Mean response time across all requests           |
| **P50**     | Median. 50% of requests are faster than this     |
| **P90**     | 90th percentile, only 10% of requests are slower |

Switch between Average, P50, and P90 using the latency selector on the chart.

### What Latency Includes

Total request latency measures the full round-trip: your request hitting Requesty → routed to the provider → model inference → response streamed back. This is the real end-to-end time your users experience.

## Advanced Performance Analysis

Use the **Advanced tab** for deeper analysis:

### Latency by Model

* Set **Metric** to `latency_ms`
* Set **Group By** to `model`
* Set **Calculation** to `P50`, `P90`, `P95`, or `P99`

This shows you which models are fastest and which have the worst tail latency.

### Latency Over Time

* Set **Time Grouping** to `hour` or `day`
* Watch for latency spikes that correlate with peak traffic or provider issues

### Error Rate Analysis

* Set **Metric** to `requests`
* Filter by error status to see failure patterns
* Group by `model` or `provider` to identify unreliable providers

## Using Performance Data to Optimize

### Set Up Latency-Based Routing

If you see that one provider is consistently faster, create a [Latency Routing Policy](/features/latency-routing) to automatically use the fastest provider:

| Model                                       |
| ------------------------------------------- |
| `anthropic/claude-sonnet-4-5`               |
| `bedrock/claude-sonnet-4-5-v2@us-east-1`    |
| `bedrock/claude-sonnet-4-5-v2@eu-central-1` |

Requesty automatically routes to whichever is fastest at request time.

### Set Up Fallback for Reliability

If a provider has high error rates, create a [Fallback Policy](/features/fallback-policies) to automatically retry with another provider:

| Priority | Model                                       | Retries   |
| -------- | ------------------------------------------- | --------- |
| 1st      | `anthropic/claude-sonnet-4-5`               | 2 retries |
| 2nd      | `bedrock/claude-sonnet-4-5-v2@eu-central-1` | 2 retries |

### Reduce Latency with Caching

[Auto Caching](/features/auto-caching) can eliminate latency entirely for repeated requests. Check the **Savings tab** to see your cache hit rate, cached responses return in single-digit milliseconds.

### Use EU Routing for European Users

If your users are in Europe, route through the [EU endpoint](/features/eu-routing) (`https://router.eu.requesty.ai/v1`) to reduce network latency by 30-50%.

## Export Performance Data

From the **Advanced tab**:

1. Set **Metric** to `latency_ms`, **Calculation** to `P90`, **Group By** to `model`
2. Set time range and grouping
3. Click **Export CSV** to download the data

## Integration

* [Usage Analytics](/features/usage-analytics). Full dashboard with all metrics
* [Cost Tracking](/features/cost-tracking). Correlate performance with cost
* [Latency Routing](/features/latency-routing). Automatically pick the fastest model
* [Fallback Policies](/features/fallback-policies). Auto-retry on provider failures
* [Spending Alerts](/features/alerts). Get notified on anomalies