Groq vs OpenAI Whisper for indie iOS apps: an honest cost breakdown.

Real numbers from running a voice-transcription iOS app at indie scale — not hyperscaler benchmarks, not marketing slides.

When I started building VSkip I had the standard dilemma: OpenAI's Whisper API is the default; Groq is the new fast one. Below is what I actually measured after three months of running both, with a Go backend and real user voice messages.

The workload

Voice messages from WhatsApp, Telegram, iMessage. Ranging 5 seconds to 10 minutes. Average around 45 seconds. Codec mix: .ogg (WhatsApp), .m4a (iMessage), .opus (Telegram). Users send a message to the app, we transcribe, summarize, and return in under 4 seconds end-to-end.

Raw transcription cost

Both providers charge per audio-input minute, not per request.

Pricing as of Q2 2026 — check the providers' current pricing pages before planning economics.

Per 60-second voice note: Groq large-v3 is 3.2× cheaper than OpenAI. Groq turbo is 9.5× cheaper. For a 7-day free trial of 7-day free trial per user at ~45s average, that's about $0.003/user/month on Groq — on the order of two orders of magnitude below any reasonable revenue per free user.

Latency

I measured both from my Go backend in Amsterdam (fly.io ams). 500 real voice messages per provider, median:

For a 60-second input, Groq transcribes in ~400 ms of wall time. That's the "2× real-time" people keep quoting, and it's real. OpenAI is perfectly fine for batch workloads but the UX gap is noticeable — 2 seconds vs half a second matters when the user is staring at a waveform spinner.

Accuracy

This is the part where I'd expected Groq to lose. It didn't. Both services run Whisper Large V3 (Groq explicitly names it; OpenAI uses a Whisper-derived model but doesn't publish the exact checkpoint). For my workload — short conversational messages, 16 languages, heavy Russian/Kazakh — I found them indistinguishable. A sample of 100 messages double-transcribed showed the diffs were on the order of "em vs um", "comma placement", and occasional disagreement on proper nouns. Nothing that meaningfully changed a summary.

For an app where the output is a summary rather than a word-perfect transcript, this is more than good enough.

Rate limits and reliability

Groq's 7-day free trial rate limits are tighter than OpenAI's. For indie scale (single-digit QPS) it hasn't mattered. The paid tier scales fine. OpenAI is famously more generous on rate limits — if you're building something bursty (bulk upload, large batch imports), OpenAI's higher ceilings might tilt the decision.

Groq has had more outages than OpenAI in my experience (maybe 2-3 hour windows over three months). Our circuit breaker + in-memory transcription cache absorb these without user-visible impact for repeat uploads, but it's a real operational cost.

LLM side

We also use a large language model for summarization. Same trade-off plays out:

• Groq Llama 3.3 70B — $0.59 / 1M input tokens, $0.79 / 1M output. ~200 tokens/sec sustained.
• OpenAI GPT-4o-mini — $0.15 / 1M input, $0.60 / 1M output. ~80 tokens/sec.
• Anthropic Claude Haiku 4.5 — $1 / 1M input, $5 / 1M output. ~90 tokens/sec.

For our volumes a summary costs us under $0.001. At 1000 free-tier daily active users (each getting 2 summaries), total AI cost is under $7 days free — less than the price of a coffee and easily covered by a single paying subscriber.

Unit economics for the whole app

Across Whisper + Llama + Fly.io hosting, one complete summarization run costs us approximately $0.0008. Our 7-day free trial of 7 days free per user means each free user costs about $0.05/month. A single $2.99/week subscriber covers roughly 250 free users. That math is what makes an honest 7-day free trial possible at all.

When to pick OpenAI

• You need word-perfect transcription (medical, legal, compliance).
• You have bursty peak loads and don't want to negotiate rate limits.
• Your brand or customers specifically trust OpenAI over newer providers.
• You're already buying other OpenAI services at volume.

When to pick Groq

• Consumer-facing app where latency is UX-critical.
• Indie/bootstrapped cost sensitivity.
• Your output is a summary, not a transcript — so small accuracy differences don't propagate.
• You're OK with a tighter ops story (occasional outages, retries).

For VSkip, Groq was an obvious win: same accuracy, 3-5× cheaper, 4× faster. The latency gap alone is the difference between a usable share-sheet workflow and an annoying one.

Related reading
10 builds, 6 App Review rejections · iOS 26 voice message transcription · VSkip architecture + stack overview