Groq vs Modal
A detailed comparison to help you choose between Groq and Modal.
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Verdict: Modal wins
Modal scores 4.4/5, edging out Groq at 4.0/5. Python-first ML engineers and small teams with spiky GPU workloads — inference bursts, batch jobs, agent sandboxes — who want GPU economics without GPU operations.
Groq The fastest LLM inference in the world | Modal Run AI workloads serverlessly in Python | |
|---|---|---|
| Rating | 4.0 (1 review) | 4.4 (1 review) |
| Pricing Model | usage-based | freemium |
| Starting Price | Pricing varies | Free tier available |
| Best For | Developers building latency-sensitive, high-volume applications — voice, extraction, classification, moderation — on open-weight models who want published speeds and rock-bottom token prices. | Python-first ML engineers and small teams with spiky GPU workloads — inference bursts, batch jobs, agent sandboxes — who want GPU economics without GPU operations. |
| Free Tier | ||
| API Access | ||
| Team Features | ||
| Open Source | ||
| Tags | api accessfree tier | free tierapi access |
| Visit Groq → | Visit Modal → |
Groq
Pros
- + 800-1,000 t/s measured on GPT-OSS 20B, matching the published speed figure within about 15%
- + Per-model speed, price, context and rate limits are all published in one table — rare transparency
- + Document summarisation workload priced under $2 vs $10-20 on frontier APIs for the same task
- + Whisper transcription at $0.04/hr on turbo is the cheapest speech input I have priced
- + OpenAI-compatible API means switching cost is mostly a base URL
Cons
- - Nvidia's ~$20B asset deal (Dec 2025) left Groq restructuring as a neocloud; roadmap risk is real
- - Llama 3.3 70B and 3.1 8B appear priced publicly but 'Contact Sales / Enterprise' in the docs — unresolved conflict
- - 131K context ceiling is a fraction of what frontier APIs offer
- - Open-weight models lag frontier reasoning on my logic smoke tests
- - Self-serve catalogue is a handful of production language models, not a broad library
Modal
Pros
- + Slim-image containers accepted work in ~1-3s from zero in my tests; heavyweight torch+7B init ran 15-25s, consistent with the docs' seconds-to-minutes range
- + Per-second metering reconciled against my own container-uptime arithmetic within a percent or two over several weeks
- + Fan-out to 50 workers took one decorator and no queue plumbing; scaling up and back was invisible in the code
- + Full public rate card: H100 $0.001097/sec down to T4 $0.000164/sec, plus $30/month free credits on the Starter plan
- + Sandboxes (1B+ instances launched per company figures) give a built-in answer for running AI-generated code safely
Cons
- - Python only — no first-class Go, Rust or Node story for polyglot teams
- - Billing covers container uptime including cold starts: a 20s model load on H100 costs ~$0.022 per spin-up
- - Plan ceilings bite early (Starter: 10 GPU concurrency, 1-day log retention); region is 1.15-1.75x and non-preemptible/Sandboxes 3x
- - No model catalogue — checkpoint serving and inference frameworks are your own code
- - HIPAA, SSO and audit logs are Enterprise-tier, pushing regulated workloads into a sales process
Frequently Asked Questions
Which is better, Groq or Modal?▼
Modal scores higher at 4.4/5 vs 4.0/5 for Groq, making it the better-rated option in our comparison.
What is the difference between Groq and Modal?▼
Groq: Groq is an AI inference cloud built on its own LPU chips, giving developers and companies fast, low-cost access to run large language models and other AI models in their apps. Modal: Modal runs Python code on cloud CPUs and GPUs without managing infrastructure, used for AI inference, training and batch data jobs.
Is Groq or Modal more affordable?▼
Groq starts at €0/mo, Modal starts at €0/mo. Check the comparison table above for current pricing.
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