# 🇧🇷 São Paulo Ping & Network Latency (GRU) | AS203314

## 🇧🇷 São Paulo (GRU)

São Paulo is the internet capital of South America, hosting the region's largest peering ecosystem at PIX. Hats Network's only South American PoP connects the continent to the global backbone.

Measurement round: `2026-08-16T04:07:28Z`.

This page provides round-trip time (RTT) latency metrics between the **São Paulo (GRU)** PoP and every other node on the Hats Network (AS203314) global backbone.

### PoP Highlights

* **Location:** São Paulo, Brazil (South America)
* **Region:** South America
* **Role:** Backbone interconnection point for Hats Network (AS203314)
* **Public city reference:** [GeoNames 3448439](https://www.geonames.org/3448439) — -23.54750, -46.63611
* **Coverage:** RTT measurements to 19 other PoPs across 4 continents

### Facility & Interconnection

* **Facility:** GRU1
* **Upstream transit:** NTT / Ascenty

> Interactive content is available on the canonical HTML page.

### Latency Summary

| Metric                | Value                                                                                                      |
| --------------------- | ---------------------------------------------------------------------------------------------------------- |
| Fastest Route         | [🇧🇷 São Paulo (GRU) → 🇺🇸 Miami (MIA)](/docs/network/latency/pairs/gru-mia-rtt) (**74 ms**) — Excellent |
| Slowest Route         | [🇧🇷 São Paulo (GRU) → 🇿🇦 Johannesburg (JNB)](/docs/network/latency/pairs/gru-jnb-rtt) (**333.8 ms**)   |
| Average RTT           | **203.4 ms**                                                                                               |
| Average Jitter        | **1.75 ms**                                                                                                |
| Average Packet Loss   | **0.05%**                                                                                                  |
| Best Fiber Efficiency | **86.6%**                                                                                                  |
| Intra-Region Peers    | 0 PoP in South America                                                                                     |
| Total Routes          | 19 outbound / 19 inbound (100% coverage)                                                                   |

> Jitter, packet-loss, percentile, and trend fields are estimated comparative indicators. Read the [theoretical fiber latency methodology](/docs/network/latency/theoretical-fiber-latency).

## Geographic & Network Position

São Paulo is Hats Network's only PoP in **South America**. The nearest measured backbone PoP is Miami (MIA) at **74 ms** RTT.

São Paulo is South America's internet capital, connecting the continent north to Miami and east across the Atlantic.

## Outbound Latency from São Paulo (GRU)

Round-trip time in milliseconds **from São Paulo** to all other backbone PoPs, sorted fastest to slowest.

| Destination             | RTT (ms)                                             | Fiber Floor | Efficiency | Jitter  | Loss  | Tier      |
| ----------------------- | ---------------------------------------------------- | ----------- | ---------- | ------- | ----- | --------- |
| 🇺🇸 Miami (MIA)        | **[74](/docs/network/latency/pairs/gru-mia-rtt)**    | 64.11 ms    | 86.6%      | 0.62 ms | 0.16% | Excellent |
| 🇺🇸 New York (NYC)     | **[122](/docs/network/latency/pairs/gru-nyc-rtt)**   | 74.99 ms    | 61.5%      | 1.29 ms | 0%    | Good      |
| 🇺🇸 Ashburn (IAD)      | **[128](/docs/network/latency/pairs/gru-iad-rtt)**   | 74.79 ms    | 58.4%      | 0.6 ms  | 0.09% | Good      |
| 🇺🇸 Los Angeles (LAX)  | **[130.8](/docs/network/latency/pairs/gru-lax-rtt)** | 96.89 ms    | 74.1%      | 0.66 ms | 0%    | Good      |
| 🇺🇸 Seattle (SEA)      | **[155.9](/docs/network/latency/pairs/gru-sea-rtt)** | 106.76 ms   | 68.5%      | 1.54 ms | 0.19% | Fair      |
| 🇬🇧 London (LON)       | **[175.8](/docs/network/latency/pairs/gru-lon-rtt)** | 92.76 ms    | 52.8%      | 1.62 ms | 0%    | Fair      |
| 🇳🇱 Amsterdam (AMS)    | **[180](/docs/network/latency/pairs/gru-ams-rtt)**   | 95.79 ms    | 53.2%      | 1.53 ms | 0%    | Fair      |
| 🇫🇷 Paris (PAR)        | **[180.2](/docs/network/latency/pairs/gru-par-rtt)** | 91.83 ms    | 51%        | 1.95 ms | 0.14% | Fair      |
| 🇩🇪 Frankfurt (FRA)    | **[186.2](/docs/network/latency/pairs/gru-fra-rtt)** | 96.04 ms    | 51.6%      | 2 ms    | 0%    | Fair      |
| 🇫🇷 Marseille (MRS)    | **[187.7](/docs/network/latency/pairs/gru-mrs-rtt)** | 89.24 ms    | 47.5%      | 1.08 ms | 0.14% | Fair      |
| 🇩🇪 Berlin (BER)       | **[191.3](/docs/network/latency/pairs/gru-ber-rtt)** | 100.2 ms    | 52.4%      | 2.12 ms | 0%    | Fair      |
| 🇷🇺 Moscow (MOW)       | **[219.5](/docs/network/latency/pairs/gru-mow-rtt)** | 115.47 ms   | 52.6%      | 1.57 ms | 0%    | Fair      |
| 🇯🇵 Tokyo (TYO)        | **[230.3](/docs/network/latency/pairs/gru-tyo-rtt)** | 181.46 ms   | 78.8%      | 2.42 ms | 0%    | Fair      |
| 🇹🇼 Taipei (TPE)       | **[259.1](/docs/network/latency/pairs/gru-tpe-rtt)** | 184.29 ms   | 71.1%      | 3.01 ms | 0%    | High      |
| 🇦🇺 Sydney (SYD)       | **[265.5](/docs/network/latency/pairs/gru-syd-rtt)** | 131 ms      | 49.3%      | 1.66 ms | 0%    | High      |
| 🇭🇰 Hong Kong (HKG)    | **[273.5](/docs/network/latency/pairs/gru-hkg-rtt)** | 176.86 ms   | 64.7%      | 2.57 ms | 0%    | High      |
| 🇦🇺 Melbourne (MEL)    | **[274.7](/docs/network/latency/pairs/gru-mel-rtt)** | 128.34 ms   | 46.7%      | 1.71 ms | 0.08% | High      |
| 🇸🇬 Singapore (SIN)    | **[297.1](/docs/network/latency/pairs/gru-sin-rtt)** | 156.67 ms   | 52.7%      | 2.14 ms | 0.08% | High      |
| 🇿🇦 Johannesburg (JNB) | **[333.8](/docs/network/latency/pairs/gru-jnb-rtt)** | 72.88 ms    | 21.8%      | 3.1 ms  | 0.07% | High      |

## Fastest Routes to São Paulo (GRU)

The 10 fastest measured routes **to São Paulo**, ranked by average RTT. Min / max / stdev are computed from the same measurement round (50 ICMP echo probes per route, 100 ms interval); routes without a published per-sample round show the measured average RTT only.

| Rank | Source                 | Avg RTT                                                 | Min       | Max       | Stdev   |
| ---- | ---------------------- | ------------------------------------------------------- | --------- | --------- | ------- |
| 1    | 🇺🇸 Ashburn (IAD)     | **[101.4 ms](/docs/network/latency/pairs/iad-gru-rtt)** | 98.16 ms  | 107.74 ms | 2.31 ms |
| 2    | 🇺🇸 New York (NYC)    | **[106.9 ms](/docs/network/latency/pairs/nyc-gru-rtt)** | 104.25 ms | 117.31 ms | 2.21 ms |
| 3    | 🇺🇸 Miami (MIA)       | **[128.7 ms](/docs/network/latency/pairs/mia-gru-rtt)** | 126.78 ms | 136.62 ms | 1.96 ms |
| 4    | 🇺🇸 Los Angeles (LAX) | **[131.3 ms](/docs/network/latency/pairs/lax-gru-rtt)** | 125.74 ms | 147.12 ms | 4.94 ms |
| 5    | 🇺🇸 Seattle (SEA)     | **[155.7 ms](/docs/network/latency/pairs/sea-gru-rtt)** | 152.75 ms | 163 ms    | 2.37 ms |
| 6    | 🇬🇧 London (LON)      | **[170.7 ms](/docs/network/latency/pairs/lon-gru-rtt)** | 165.28 ms | 185.16 ms | 4.35 ms |
| 7    | 🇳🇱 Amsterdam (AMS)   | **[175.5 ms](/docs/network/latency/pairs/ams-gru-rtt)** | 166.82 ms | 195.11 ms | 6.86 ms |
| 8    | 🇫🇷 Paris (PAR)       | **[175.8 ms](/docs/network/latency/pairs/par-gru-rtt)** | 173.09 ms | 186.7 ms  | 2.78 ms |
| 9    | 🇩🇪 Frankfurt (FRA)   | **[181.3 ms](/docs/network/latency/pairs/fra-gru-rtt)** | 176.28 ms | 197.75 ms | 3.73 ms |
| 10   | 🇩🇪 Berlin (BER)      | **[186.5 ms](/docs/network/latency/pairs/ber-gru-rtt)** | 179.2 ms  | 206.69 ms | 6.78 ms |

## Inbound Latency to São Paulo (GRU)

Round-trip time in milliseconds **from all other PoPs to São Paulo**. Network paths may be asymmetric — inbound and outbound RTT can differ for the same city pair.

| Source                  | RTT (ms)                                             | Fiber Floor | Efficiency | Jitter  | Loss  | Tier |
| ----------------------- | ---------------------------------------------------- | ----------- | ---------- | ------- | ----- | ---- |
| 🇺🇸 Ashburn (IAD)      | **[101.4](/docs/network/latency/pairs/iad-gru-rtt)** | 74.79 ms    | 73.8%      | 1.02 ms | 0%    | Good |
| 🇺🇸 New York (NYC)     | **[106.9](/docs/network/latency/pairs/nyc-gru-rtt)** | 74.99 ms    | 70.1%      | 0.74 ms | 0%    | Good |
| 🇺🇸 Miami (MIA)        | **[128.7](/docs/network/latency/pairs/mia-gru-rtt)** | 64.11 ms    | 49.8%      | 0.81 ms | 0.16% | Good |
| 🇺🇸 Los Angeles (LAX)  | **[131.3](/docs/network/latency/pairs/lax-gru-rtt)** | 96.89 ms    | 73.8%      | 0.87 ms | 0%    | Good |
| 🇺🇸 Seattle (SEA)      | **[155.7](/docs/network/latency/pairs/sea-gru-rtt)** | 106.76 ms   | 68.6%      | 1.76 ms | 0%    | Fair |
| 🇬🇧 London (LON)       | **[170.7](/docs/network/latency/pairs/lon-gru-rtt)** | 92.76 ms    | 54.3%      | 1.06 ms | 0.02% | Fair |
| 🇳🇱 Amsterdam (AMS)    | **[175.5](/docs/network/latency/pairs/ams-gru-rtt)** | 95.79 ms    | 54.6%      | 1.53 ms | 0%    | Fair |
| 🇫🇷 Paris (PAR)        | **[175.8](/docs/network/latency/pairs/par-gru-rtt)** | 91.83 ms    | 52.2%      | 1.26 ms | 0%    | Fair |
| 🇩🇪 Frankfurt (FRA)    | **[181.3](/docs/network/latency/pairs/fra-gru-rtt)** | 96.04 ms    | 53%        | 2.02 ms | 0.04% | Fair |
| 🇩🇪 Berlin (BER)       | **[186.5](/docs/network/latency/pairs/ber-gru-rtt)** | 100.2 ms    | 53.7%      | 2.2 ms  | 0.01% | Fair |
| 🇫🇷 Marseille (MRS)    | **[186.7](/docs/network/latency/pairs/mrs-gru-rtt)** | 89.24 ms    | 47.8%      | 1.4 ms  | 0.05% | Fair |
| 🇷🇺 Moscow (MOW)       | **[214.4](/docs/network/latency/pairs/mow-gru-rtt)** | 115.47 ms   | 53.9%      | 1.47 ms | 0%    | Fair |
| 🇯🇵 Tokyo (TYO)        | **[230.5](/docs/network/latency/pairs/tyo-gru-rtt)** | 181.46 ms   | 78.7%      | 1.53 ms | 0%    | Fair |
| 🇹🇼 Taipei (TPE)       | **[273](/docs/network/latency/pairs/tpe-gru-rtt)**   | 184.29 ms   | 67.5%      | 2.95 ms | 0.03% | High |
| 🇭🇰 Hong Kong (HKG)    | **[287.6](/docs/network/latency/pairs/hkg-gru-rtt)** | 176.86 ms   | 61.5%      | 2.69 ms | 0.03% | High |
| 🇦🇺 Sydney (SYD)       | **[296.7](/docs/network/latency/pairs/syd-gru-rtt)** | 131 ms      | 44.2%      | 1.93 ms | 0%    | High |
| 🇦🇺 Melbourne (MEL)    | **[305.3](/docs/network/latency/pairs/mel-gru-rtt)** | 128.34 ms   | 42%        | 1.69 ms | 0%    | High |
| 🇸🇬 Singapore (SIN)    | **[311.4](/docs/network/latency/pairs/sin-gru-rtt)** | 156.67 ms   | 50.3%      | 2.4 ms  | 0.1%  | High |
| 🇿🇦 Johannesburg (JNB) | **[328.7](/docs/network/latency/pairs/jnb-gru-rtt)** | 72.88 ms    | 22.2%      | 2.32 ms | 0.06% | High |

## Frequently Asked Questions

**What is the fastest route to São Paulo?**

The fastest measured route to São Paulo (GRU) is [Ashburn (IAD) → São Paulo (GRU)](/docs/network/latency/pairs/iad-gru-rtt), averaging **101.4 ms** RTT (Good).

**What is the fastest route from São Paulo?**

The fastest measured route from São Paulo (GRU) is [São Paulo (GRU) → Miami (MIA)](/docs/network/latency/pairs/gru-mia-rtt), averaging **74 ms** RTT (Excellent).

**How well connected is São Paulo to the Hats Network backbone?**

São Paulo (GRU) maintains measured routes to all 19 other PoPs across 4 continents. The slowest route, [São Paulo (GRU) → Johannesburg (JNB)](/docs/network/latency/pairs/gru-jnb-rtt), averages **333.8 ms** RTT.

## Open Data

Measured latency data for São Paulo (GRU) is published daily under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) as part of the Hats Network open latency dataset:

* **Per-route files** — every directed route from São Paulo is published as `gru-<destination>.pings.{csv,json,yaml}` under [`/opendata/latency/latest/pairs/`](/opendata/latency/latest/pairs/) — for example [gru-mia.pings.csv](/opendata/latency/latest/pairs/gru-mia.pings.csv), the 50-probe ICMP echo round for [São Paulo (GRU) → Miami (MIA)](/docs/network/latency/pairs/gru-mia-rtt).
* **Dataset documentation** — [/opendata/latency/](/opendata/latency/) covers file formats, versioning, checksums, and the aggregated full-matrix releases.

***

*Data auto-generated on August 16, 2026. Explore the [full interactive latency matrix](/docs/network/latency) for side-by-side comparison across all 20 PoPs.*

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## License and attribution

- **Canonical source:** [View the human-readable HTML page](https://hatsnet.io/docs/network/latency/gru-sao-paulo).
- **Original documentation:** © Hats Network Inc., licensed under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/).
- **Public latency data:** © Hats Network Inc., licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
- **Full terms:** [Content and Data License](https://hatsnet.io/docs/legal/content-and-data-license) — includes attribution requirements, third-party material, trademarks, source code and other exclusions.