Concentrate AI vs VercelAI Gateway Benchmark Comparison
Vercel was faster in 2 of 4 shared AI gateway benchmarks, Concentrate AI in 1, and 1 was roughly even.
- Shared benchmarks
- 4
- Last run
- October 30, 2026
Scorecard
Median of each benchmark's primary metric; lower is better. Medians within 5% count as roughly even, and a benchmark gets no speed verdict when either provider completed fewer than 50% of runs.
| Benchmark | Concentrate AI | Vercel | Result | Ratio |
|---|---|---|---|---|
| Anthropic Cold end-to-end latency · Run October 30, 2026 | 608ms 100% success | 495ms 100% success | Vercel faster | 1.23x |
| OpenAI Cold end-to-end latency · Run October 30, 2026 | 990ms 100% success | 735ms 100% success | Vercel faster | 1.35x |
| Gemini Cold end-to-end latency · Run October 30, 2026 | 1803ms 100% success | 1966ms 100% success | Concentrate AI faster | 1.09x |
| Kimi Cold end-to-end latency · Run October 30, 2026 | 2167ms 100% success | 2209ms 100% success | Roughly even | 1.02x |
Anthropic
Run October 30, 2026Time for a cold request routed through the gateway to Claude Haiku 4.5 to return its complete response.
Cold end-to-end latency
Vercel had a 1.23x lower median cold end-to-end latency (495ms vs 608ms for Concentrate AI).
Median cold end-to-end latency over time
14 runs from July 31, 2026 to October 30, 2026.
OpenAI
Run October 30, 2026Time for a cold request routed through the gateway to GPT-5.4 Mini to return its complete response.
Cold end-to-end latency
Vercel had a 1.35x lower median cold end-to-end latency (735ms vs 990ms for Concentrate AI).
Median cold end-to-end latency over time
11 runs from August 21, 2026 to October 30, 2026.
Gemini
Run October 30, 2026Time for a cold request routed through the gateway to Gemini 3.6 Flash to return its complete response.
Cold end-to-end latency
Concentrate AI had a 1.09x lower median cold end-to-end latency (1803ms vs 1966ms for Vercel).
Median cold end-to-end latency over time
11 runs from August 21, 2026 to October 30, 2026.
Kimi
Run October 30, 2026Time for a cold request routed through the gateway to Kimi K3 to return its complete response.
Cold end-to-end latency
The medians are within 5% of each other (2167ms vs 2209ms), so this counts as roughly even.
Median cold end-to-end latency over time
11 runs from August 21, 2026 to October 30, 2026.
Methodology
- One workload, every provider. The same script, payload, and model hit every provider, addressed the way its own API expects. Nobody gets a tuned or special path.
- Repeated on a schedule. Every suite runs many iterations per provider, automated on a recurring schedule, so results reflect steady state rather than one lucky request.
- Raw results published. Every run is committed as JSON to the public benchmarks repo, next to the code that produced it. Nothing is cherry-picked or held back.
- How this comparison is built. Each benchmark compares the two providers' medians; results within 5% count as roughly even. A comparison is published only when both providers completed the latency benchmark on at least two of the same model families reliably: at least 50% success for each provider, in benchmark runs from the last 30 days.
Frequently asked questions
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Vercel was faster in 2 of 4 shared AI gateway benchmarks, Concentrate AI in 1, and 1 was roughly even. In Anthropic (October 30, 2026), Vercel had a median cold end-to-end latency of 495ms versus 608ms for Concentrate AI, 1.23x faster. In OpenAI (October 30, 2026), Vercel had a median cold end-to-end latency of 735ms versus 990ms for Concentrate AI, 1.35x faster. In Gemini (October 30, 2026), Concentrate AI had a median cold end-to-end latency of 1803ms versus 1966ms for Vercel, 1.09x faster. In Kimi (October 30, 2026), Concentrate AI had a median cold end-to-end latency of 2167ms and Vercel 2209ms, which counts as roughly even.