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Inference comparison

Groq vs Modal

Managed inference API against serverless gpu. Editorial fit scores help shortlist; live pricing stays on the provider sites.

Groq product facts checked October 2, 2026Groq editorial scores reviewed September 3, 2026Modal product facts checked October 2, 2026Modal editorial scores reviewed September 3, 2026

Managed inference API

Groq

GroqCloud model-specific token, audio-hour or character pricing; eligible asynchronous Batch requests receive a 50% discount. Dedicated platform terms differ.

vs

Serverless GPU

Modal

Functions: per-second CPU, memory and GPU resources; Shared Endpoints: per-token. Storage and network egress have separate allowances and rates.

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Groq vs Modal GPU inference comparison
DimensionGroqModal
CategoryManaged inference APIServerless GPU
BillingGroqCloud model-specific token, audio-hour or character pricing; eligible asynchronous Batch requests receive a 50% discount. Dedicated platform terms differ.Functions: per-second CPU, memory and GPU resources; Shared Endpoints: per-token. Storage and network egress have separate allowances and rates.
GPU choiceGroqMetal dedicated LPU infrastructure; GroqCore adds an inference stack and GroqAssured enterprise controls. These are separate from the shared GroqCloud API.Choose supported GPU types through the function GPU setting, including T4, L4, A10, L40S, A100, RTX PRO 6000, H100, H200, B200 and B300; allocation and multi-GPU limits vary.
Cold startGroqCloud is a shared API; no universal first-request latency is established by the captured documentation. Dedicated capacity has its own deployment terms.Can be noticeable on cold containers; benchmark the real image and model.
Model accessGroqCloud production and preview catalogs are separate. Current docs list GPT-OSS text and Whisper audio models; some Llama and MiniMax routes require Enterprise sales. Preview models are for evaluation and may be discontinued at short notice.Package models in images or pull at runtime
Best forLatency-sensitive chat, agents, and batch inference on Groq’s supported model setDevelopers who want GPU code as functions with autoscaling
Watch outConfirm the exact served model, endpoint and current quota before committing architecture. Groq’s first-party announcement describes a non-exclusive NVIDIA technology licensing agreement. Its August 17, 2026 release confirms a $350M Series A; neither announcement establishes a fixed throughput or capacity guarantee for your workload.You can choose a specific supported GPU type, but containers and scaling remain managed. Warm idle containers consume billable resources. Starter compute credits do not cover Shared Endpoints tokens.
GPU choice (1–10)2/10Winner: 6/10
Time-to-serving (editorial) (1–10)Winner: 10/107/10
Price clarity (1–10)6/10Winner: 7/10
Production ops (1–10)8/108/10
Open-model breadth (1–10)6/10Winner: 7/10

Chart

Fit scores on the decision axes

Editorial 1–10 ratings for this pair only — not live pricing or latency benchmarks.

GPU choice

Pick exact GPUs

Time-to-serving (editorial)

Warm-path fit

Price clarity

Easy to forecast

Production ops

Less DIY ops

Open-model breadth

Catalog depth

Scores are editorial planning ratings (1–10) for product shape — not published $/hour quotes or vendor SLAs. Verify current pricing on each provider site.

How to decide

Prefer Groq when latency-sensitive chat, agents, and batch inference on groq’s supported model set. Prefer Modal when developers who want gpu code as functions with autoscaling. If those statements both feel true, rent a GPU for control and keep a managed API for peak traffic — do not force one product to do both jobs.

Common questions

Groq vs Modal

Answered from the verified figures on this page rather than general guidance.

How are Groq and Modal billed?
Groq uses groqcloud model-specific token, audio-hour or character pricing; eligible asynchronous batch requests receive a 50% discount. dedicated platform terms differ.; Modal uses functions: per-second cpu, memory and gpu resources; shared endpoints: per-token. storage and network egress have separate allowances and rates.. Marketplace hourly spend tracks GPU uptime; serverless and managed APIs bill for what you invoke — different failure modes if you forget to shut things down.
Which gives more control over the GPU, Groq or Modal?
Groq: GroqMetal dedicated LPU infrastructure; GroqCore adds an inference stack and GroqAssured enterprise controls. These are separate from the shared GroqCloud API.. Modal: Choose supported GPU types through the function GPU setting, including T4, L4, A10, L40S, A100, RTX PRO 6000, H100, H200, B200 and B300; allocation and multi-GPU limits vary.. Modal scores higher for GPU choice (6/10 vs 2/10). Pick a marketplace when you need a specific SKU; pick serverless or managed APIs when you want the platform to handle hardware.
Which has faster cold starts, Groq or Modal?
Groq: GroqCloud is a shared API; no universal first-request latency is established by the captured documentation. Dedicated capacity has its own deployment terms.. Modal: Can be noticeable on cold containers; benchmark the real image and model.. Our editorial ratings favour Groq for warm API speed (10/10 vs 7/10), but real latency depends on model size, region, and whether endpoints are kept warm. Those 1–10 scores are editorial rankings, not measured milliseconds — record your own TTFT/p95 on a warm endpoint in the target region before you buy on latency.
Should I use Groq or Modal?
Choose Groq when latency-sensitive chat, agents, and batch inference on groq’s supported model set. Choose Modal when developers who want gpu code as functions with autoscaling. Watch out: Confirm the exact served model, endpoint and current quota before committing architecture. Groq’s first-party announcement describes a non-exclusive NVIDIA technology licensing agreement. Its August 17, 2026 release confirms a $350M Series A; neither announcement establishes a fixed throughput or capacity guarantee for your workload. You can choose a specific supported GPU type, but containers and scaling remain managed. Warm idle containers consume billable resources. Starter compute credits do not cover Shared Endpoints tokens.
When should I use a managed API like Groq instead of renting a GPU?
Prefer a managed inference API when you want tokens quickly without CUDA ops and the model is already on the catalog. Prefer GPU rental when you need a specific SKU, custom serving stack, or sustained utilization that beats token pricing — size VRAM on /gpus first.