I've been conducting a series of controlled performance benchmarks on the Gemini API suite, specifically focusing on Flash 1.5 for its promised cost-to-performance ratio in high-volume, low-latency tasks. Over the past 72 hours, my monitoring has captured significant and erratic latency spikes that do not correlate with my load patterns, and I'm attempting to determine if this is a localized issue or a broader systemic trend.
My test harness is designed to isolate provider latency from network overhead. It's a simple Go service deployed in us-central1, making synchronous calls to `gemini-1.5-flash-latest` with identical parameters. The payload is a 150-token retrieval-augmented generation (RAG) context with a straightforward summarization instruction.
The observed P99 latency, which had been relatively stable at 1.2-1.5 seconds, has shown intermittent jumps to between 4.8 and 7.2 seconds. These spikes are not gradual but rather sudden, lasting for batches of 5-10 requests before returning to baseline. Crucially, there are no corresponding increases in error rates or token usage deviations.
My configuration and a simplified version of the measurement loop:
```go
// core measurement function
func benchmarkRequest(ctx context.Context, client *genai.Client, prompt string) (latencyMs int64, err error) {
start := time.Now()
model := client.GenerativeModel("gemini-1.5-flash-latest")
model.SetTemperature(0.1)
resp, err := model.GenerateContent(ctx, genai.Text(prompt))
latencyMs = time.Since(start).Milliseconds()
// ... validate resp
return latencyMs, err
}
```
Key environmental controls:
* Concurrency level fixed at 10 workers.
* Request cooling-off period of 100ms between dispatches per worker.
* All runs target the `us-central1` endpoint.
* No changes were made to the codebase or infrastructure during the observation window.
I have ruled out:
1. **Regional network issues:** Cross-region pings and control calls to a different provider (Claude Haiku) show no correlated latency.
2. **Cold starts:** The pattern persists deep into sustained test runs (>500 requests).
3. **Payload size variance:** Input and output tokens are logged and show <5% variation.
4. **Client-side throttling:** My rate is well below the documented quotas (900 RPM, 15k TPM).
The questions I'm posing to the community:
* Are others observing similar non-linear latency degradation with Gemini Flash, particularly in the last few days?
* If so, does it appear to be region-specific? I'm planning to run comparative tests in `europe-west1` next.
* Has anyone successfully correlated these spikes with specific times of day or internal Google Cloud platform metrics?
* Most importantly, has anyone engaged with Google support and received a meaningful root-cause analysis? My preliminary ticket yielded only the standard "no ongoing incidents" response.
I'm skeptical of attributing this to mere "noisy neighbor" issues given the magnitude of the P99 shift. For our use case—where predictable latency is more critical than absolute speed—these spikes are problematic. I'll be sharing my structured benchmark results, including comparison baselines against GPT-4o-mini and Claude Haiku, in a follow-up post once I gather more data. Any shared observations or diagnostic approaches would be valuable.
Trust but verify.
Interesting that you're focusing on the latency promise. But are you actually seeing any cost impact from these spikes? The whole sales pitch for Flash is the cost-to-performance ratio, right? If your P99 jumps but you're still paying per-request, the only thing that changes is your user's frustration. I'd be checking my billing data for those time windows to see if the erratic performance translated into any financial anomaly, or if Google just bills you the same while serving you slop.
cost_observer_42
Yeah, those are some significant spikes. Your setup looks solid for isolating the variable, especially with the consistent payload and region.
I haven't seen anything flagged on the community status board yet, but a few folks in the dev channels have mentioned similar blips in the last day or so. It's often a regional load-balancing thing that settles down. Have you tried pinging the same endpoint from a different GCP region, just as a quick sanity check? Might help confirm if it's localized to us-central1.
Stay constructive
Interesting, we've been running similar RAG workloads for our sales forecasting reports and saw the same pattern late last night Pacific time. Our baseline is more like 1.8 seconds, but we definitely got hit with those sudden 5+ second batches.
Since you're also in us-central1, I'm leaning toward this being a regional backend issue, not your setup. One thing I'd add, check if your spikes correlate to the top of the hour. We've sometimes seen queuing behavior when other scheduled jobs fire off. Might be worth a quick cross-check with your cloud logging timestamps.
Have you opened a ticket with GCP support? They're usually pretty quick to confirm if it's a known incident.
Top of the hour is a good call. I've seen that exact behavior with scheduled exports hitting other API services, it's like a mini-DDoS from your own ecosystem.
But honestly, a 5-second batch for sales forecasting? Your team must have the patience of saints. That's where the real cost is - idle time, not the API bill. If this becomes regular, I'd be looking at queue-and-retry logic, or just pre-generating those reports.
CRM is a means, not an end.
Queue-and-retry logic is a band-aid on an availability problem. It's the wrong fix. The real cost, as you point out, is idle time. But queuing just makes that latency visible to your users as stale data or loading spinners.
Pre-generating reports is the pragmatic workaround if your latency spikes are predictable. It's what we did with our forecasting dashboards after hitting similar issues. You move the compute cost to an off-peak window and serve static JSON. The trade-off is data freshness, but for a daily forecast, is five minutes of lag really a problem? Probably not.
I wouldn't build a complex queuing layer for a vendor's intermittent slowness. Either they fix it or you work around it by not calling them during peak.
That 1.2-1.5 second baseline is interesting. I'd be checking the real dollar cost per request in those 4.8+ second windows. My bet is it's identical. So much for the cost-to-performance ratio they're selling. The real performance hit is your team waiting for reports to load, not the bill.
Your setup is solid, but this is classic for these "fast" models. I saw the same junk with Salesforce's Einstein a few years back. They'd blame the region, your VPC, your cat's birthday. The pattern is the tell: sudden, batched, no error increase. That's their autoscaling hitting a wall.
If it's truly erratic, queue-and-retry just adds complexity for a problem you can't fix. Can your forecast accept slightly stale data? Cache the last good response for a few minutes during a spike.
been there, migrated that
Tickets are just noise unless you're paying for premium support. Their SLA starts at 99.9%, so your 5-second batches don't even register as an incident.
You should check your actual cloud logging timestamps, though. A spike at the top of the hour isn't "queuing from other jobs," it's their own billing cron jobs hitting the same shared infra. The real cost is your idle compute waiting for those 5+ second replies.
show the math