Runpod Overdrive
Same model. Fewer failed requests. Steadier under load.
We ran identical inference traffic against Together AI's serverless API and Runpod Serverless at concurrency 30 — no optimization layer, no cherry-picked runs. Here's exactly what the logs showed, including the metric where Together comes out ahead.
Comparison
The numbers, unfiltered
Pulled straight from our internal benchmark log. Both platforms tested at concurrency 30 on an H100 SXM-class endpoint, same LLM.
| METRIC | RUNPOD SERVERLESS | TOGETHER AI | RESULT |
|---|---|---|---|
| Request throughput (req/s) | 12.72 | 13.34 | Similar |
| Output tokens/sec | 3,256 | 3,414 | Similar |
| Mean time-to-first-token (ms) | 1,189 | 546 | Together 2.2× faster |
| Mean TPOT (ms) | 3.15 | 6.15 | Runpod 2× faster |
| Failed requests | 2 | 7 | Runpod 3.5× more reliable |
| Est. end-to-end latency (ms) | 1,992 | 2,114 | Runpod 1.1× faster |
Note from our test log: at concurrencies other than 30, Together AI's failure rate climbed further. Together AI's own rate limits appear to be the cause — a handful of high-volume callers can consume the shared bandwidth other tenants are counting on.
Why Overdrive
Reading the trade-off honestly
Together AI wins the sprint. Runpod wins the mile. Time-to-first-token and per-token pace measure different things. Together's shared serverless fleet gets your first token out faster — but the request has to survive to finish.
Where Together AI is ahead
A 546ms mean time-to-first-token is a real, measured advantage — useful for latency-sensitive chat UIs where the first visible token matters most. That's Together's shared-fleet routing doing what it's built for.
Where Runpod is ahead
Once tokens start streaming, Runpod holds a steadier per-token pace (2× lower TPOT), finishes the full request faster on average, and drops less than a third as many requests under identical load — before any Overdrive optimization is applied.
Performance
What changes with Overdrive
This was the baseline. Overdrive is the upgrade. Every number above is Runpod Serverless running the stock LLM — before Overdrive touches it. Overdrive's published optimization results show where that TTFT gap goes next.
Three steps
How it works
- 01
Tell us the workload
Model, context length, and traffic pattern. We benchmark your current endpoint's real performance first — not a synthetic best case. - 02
We optimize it
Your model loads into Runpod Overdrive. No re-packaging, no new framework — if it's an LLM you're already serving, we can tune it. - 03
It runs on Serverless
Sub-200ms cold starts, zero idle cost, and it scales on the same account you already use for training and experimentation.
