Groq vs DeepInfra (2026): Latency Floor vs Cheapest Breadth

Who Wins Per Workload

The choice is rarely "which provider is better." It is "which provider wins for the request I am about to send." This is that table.

Quick Comparison

Groq sells speed and determinism on a fixed menu. DeepInfra sells price, context, and deployment flexibility. Morph is in the third column for honesty, not as a general-serving alternative: it does code apply, not chat.

Cost on a Real Workload

Take a concrete case: serving Llama 3.3 70B at 50M output tokens per day. Using only the list prices on this page (computed from list prices, early 2026), the arithmetic is something you can redo by hand.

Groq has no self-serve dedicated rate to compare here. Its on-demand premium ($1,185/mo versus DeepInfra's $480/mo) buys the latency floor, not lower cost. If a user is waiting on the output, that premium can be worth it. If the work is batched, it is not.

Architecture: Deterministic LPU vs Flexible GPU Fleet

The hardware is the whole story here. Groq runs its own chip; DeepInfra runs commodity GPUs well.

Groq's LPU stores model weights in hundreds of megabytes of on-chip SRAM rather than treating SRAM as a cache over DRAM. A purpose-built compiler statically schedules the entire execution graph down to individual clock cycles, including inter-chip communication. There is no cache hierarchy, no prefetch logic, and no runtime speculation, so latency is deterministic: every forward pass executes the same operations in the same order. That is why Groq's token rate stays flat under load instead of degrading the way a contended GPU does.

DeepInfra runs A100, H100, and B200 GPUs with regional autoscaling and FP8 quantization. It does not chase a single-stream speed record. Its advantage is that any model that runs on a GPU runs on DeepInfra, including models you bring yourself, and it scales instances from zero to many based on load.

The practical split: Groq wins when you need one answer back fast and predictably. DeepInfra wins when you need a specific or private model served cheaply, even if each token arrives a little slower.

Speed: Groq Wins Single-Stream by a Wide Margin

For latency-sensitive workloads, Groq is the faster provider, full stop.

Artificial Analysis has benchmarked Groq's Llama 3.3 70B as the fastest of all tracked providers. The speculative-decoding build (a small draft model predicts tokens, the 70B verifies them in one batched pass) jumped from about 250 tok/s to over 1,600 on the same hardware.

DeepInfra's fleet averages roughly 42 tok/s across 28 benchmarked models, with the fastest small FP8 models near 316 tok/s and a mean time-to-first-token around 885 ms. That is fine for batch and async work, but it is not in the same class as Groq for interactive, token-by-token streaming.

Pricing: DeepInfra Undercuts on Most Models

At full on-demand rates, DeepInfra is the cheaper provider for nearly every shared open model.

On Llama 3.3 70B, DeepInfra is about 6x cheaper on input and 2.5x cheaper on output. Groq narrows that with a 50% Batch API discount and 50% prompt caching, which stack to roughly 25% of on-demand pricing for cacheable, async workloads. If your traffic is real-time and uncached, DeepInfra still wins on raw token cost.

Models & Flexibility: Curated Menu vs Open Catalog

Groq gives you a short, fast list. DeepInfra gives you a large catalog plus your own models.

Groq serves a curated set: Llama 3.x and 4, GPT-OSS 20B and 120B, Qwen3 32B, and a handful of others, each hand-tuned for the LPU. You get speed and consistency, but you cannot run a model Groq has not onboarded. There is no embeddings endpoint and no image generation.

DeepInfra hosts a much larger catalog (Llama 4 Maverick at 1M context, DeepSeek V4, Qwen3-235B, and more) plus embeddings ($0.005 to $0.01 per 1M tokens) and FLUX image generation. The catalog tracks new open releases quickly, and anything missing you can deploy yourself.

Dedicated & Custom Deployments: DeepInfra Only

This is the clearest dividing line. DeepInfra supports private and custom deployments; Groq does not.

DeepInfra deploys custom models and LoRA adapters on A100, H100, H200, B200, or B300 GPUs with autoscaling and tenant isolation. The private endpoint speaks the same OpenAI-compatible API as the shared one, so moving from multi-tenant to dedicated is a base-URL change, not a rewrite. Groq's dedicated capacity is available only through enterprise sales, and there is no self-serve custom-model path.

Developer Features: Both Cover the Basics

Both are OpenAI-compatible and support tool calling and JSON output. Groq goes deeper on structured outputs; DeepInfra goes wider on modalities.

Groq's strict structured outputs use constrained decoding to guarantee 100% schema adherence, never returning invalid JSON. That matters for agent pipelines that parse every response. DeepInfra ships scoped JWTs that limit a key to a specific model and spend cap, plus async webhook workflows, which help when you are running many keys across a team or product.

Compliance & Limits

DeepInfra publishes the broader compliance posture; Groq publishes the more generous free tier.

If you need HIPAA or ISO 27001 on paper without an enterprise contract, DeepInfra states SOC 2, ISO 27001, GDPR, and HIPAA compliance with a zero-data-retention policy. Groq routes compliance and dedicated capacity through enterprise sales. For free experimentation, Groq's 30 RPM and 14,400 requests-per-day allowance is one of the more generous free tiers around.

When to Use Groq

• Real-time, latency-bound apps. ~394 tok/s on Llama 3.3 70B and 1,600+ with speculative decoding. Voice agents, live chat, and interactive UIs feel instant.
• Deterministic performance under load. The LPU's static scheduling keeps token rate flat instead of degrading when traffic spikes.
• Strict JSON pipelines. Constrained-decoding structured outputs guarantee 100% schema adherence, so downstream parsers never break.
• You are happy on the curated menu. If Llama, GPT-OSS, or Qwen3 covers your need, you get top speed without managing infrastructure.
• Generous free experimentation. 14,400 requests per day on the free tier is enough to prototype seriously before paying.

When to Use DeepInfra

• Lowest per-token price. $0.10/$0.32 on Llama 3.3 70B is roughly 6x cheaper than Groq on input. For batch and offline work, the savings compound.
• Custom or private models. Deploy any Hugging Face model or LoRA adapter on dedicated A100/H100/B200 GPUs at $0.89 to $2.79 per hour, autoscaling from zero.
• Broad modality coverage. Embeddings at $0.005 to $0.01 per 1M tokens and FLUX image generation alongside the LLM catalog.
• Compliance out of the box. SOC 2, ISO 27001, GDPR, and HIPAA with zero data retention and scoped JWT keys.
• Long context. Llama 4 Maverick serves up to 1M tokens of context, well beyond Groq's 128k-131k window.

Neither is built for the coding-agent apply loop; if applying model-generated code edits is the bottleneck, that is a different tool (Morph Fast Apply, ~10,500 tok/s, with published benchmarks).

Frequently Asked Questions

Is Groq or DeepInfra faster?

Groq is faster for single-stream latency. Its LPU runs Llama 3.3 70B at about 394 tok/s on-demand, and the speculative-decoding build exceeds 1,600 tok/s. DeepInfra averages around 42 tok/s across its benchmarked models, with the fastest near 316. If time-to-last-token matters, Groq wins; if price-per-token matters, DeepInfra usually wins.

Is Groq or DeepInfra cheaper?

DeepInfra is cheaper per token for most open models. Llama 3.3 70B Turbo is $0.10/$0.32 per 1M tokens on DeepInfra versus $0.59/$0.79 on Groq, roughly 6x cheaper on input. Groq closes the gap with a 50% Batch API discount and 50% prompt caching that stack to about 25% of on-demand pricing, but at full on-demand rates DeepInfra is the budget pick.

Can I deploy my own fine-tuned model?

Only on DeepInfra. It supports custom Hugging Face models and LoRA adapters on dedicated A100, H100, H200, B200, or B300 GPUs with autoscaling from zero, at $0.89 to $2.79 per hour. Groq serves a curated model list only and does not support custom or LoRA uploads.

Does Groq or DeepInfra support longer context?

DeepInfra. It serves Llama 4 Maverick at up to 1M tokens of context, well beyond Groq's typical 128k to 131k window. If your workload feeds large documents, codebases, or long histories into a single call, DeepInfra is the provider with the headroom. Groq's curated menu trades context length for its deterministic latency floor.

Are Groq and DeepInfra OpenAI-compatible?

Yes. Both expose an OpenAI-compatible chat completions API and support function calling and JSON-mode structured outputs. Groq adds strict structured outputs with constrained decoding (100% schema adherence) and a Responses API. DeepInfra also covers embeddings and FLUX image generation, which Groq does not.