Fireworks AI vs Groq (2026): Groq Runs Llama 70B at 394 tok/s for $0.59/$0.79, Fireworks Hosts 100+ Models You Can Fine-Tune

Who Wins Per Workload

The hardware split decides most of these. Groq owns the latency floor and stock-model simplicity; Fireworks owns anything that needs flexibility.

Quick Comparison

Groq wins on raw speed and headline price for the models it hosts. Fireworks wins on model count, fine-tuning, and enterprise deployment options.

Hardware: LPU vs GPU

The whole comparison flows from one decision: Groq replaced the GPU, Fireworks optimized it.

Groq's Language Processing Unit is a deterministic dataflow chip. Each clock cycle runs the same operations in the same order, with no cache hierarchy, no prefetch logic, and no speculative execution. It uses about 230MB of on-chip SRAM instead of HBM, which removes the memory-bandwidth wall that caps GPU token generation. The cost of that design: a single LPU cannot hold a 70B model, so Groq links hundreds of chips across racks to serve one large model. That is GroqCloud's job to manage, not yours.

Fireworks runs Nvidia H100, H200, B200, and B300 GPUs and squeezes them with a proprietary inference stack rather than off-the-shelf vLLM or TensorRT-LLM. FireAttention CUDA kernels, speculative decoding, continuous batching, and hardware-specific quantization are how it competes on speed without custom silicon. The trade is that GPU throughput varies with batch size and deployment shape, so latency is not as flat as an LPU's, but the same flexibility lets Fireworks host 100+ open models and let you fine-tune them.

The practical difference: Groq's determinism means latency does not vary with batch size up to capacity, which is excellent for predictable real-time apps. Fireworks' GPU flexibility means it can host almost any open model and let you fine-tune it, which an LPU pipeline is not built to do.

Speed: Groq Leads Single-Stream

Groq is the fastest provider for the models it hosts. Independent benchmarks consistently put it at the top of single-stream throughput.

Groq publishes the per-model speeds above directly on its pricing page, all at 128k or 131k context. Fireworks does not publish a fixed tok/s per model because GPU throughput varies with batch size and deployment shape. For single-stream workloads where one user waits on one stream, Groq's LPU is hard to beat. For a tuned model or one Groq does not host, Fireworks' flexibility matters more than headline tok/s.

For a chatbot or voice agent where latency is the product, Groq's determinism keeps response times flat regardless of batch size up to capacity. For a batch pipeline, a model Groq does not host, or a model you fine-tuned, Fireworks is the better fit.

Pricing: Groq Cheaper Per Token, Fireworks Cheaper at Scale

The two catalogs only overlap on a few models, so most rows are not a head-to-head. The clean shared comparison is GPT-OSS 120B, where both charge $0.15 input / $0.60 output per million tokens and Groq adds a published 500 tok/s. On the curated models Groq hosts, Groq is cheap and fast. For DeepSeek, GLM, Qwen, Kimi, and MiniMax, Fireworks is where you go.

Both providers discount cached input and batch jobs. Fireworks bills cached input at a 50% discount and runs batch inference at 50% of serverless pricing. Groq's prompt caching halves input cost on supported models, and its Batch API runs at 50% lower cost on a 24-hour to 7-day window. Fireworks gives new accounts $1 in free credits; Groq has a free tier at console.groq.com.

Rate Limits and Spending Tiers

Limits decide whether you can ship at scale, not just whether the demo works. The two providers gate differently: Fireworks gates monthly budget by spending tier, Groq gates requests-per-minute by plan and model.

Fireworks spending tiers step up automatically: Tier 1 is $50/mo with a valid card, Tier 2 is $500/mo once you have spent or added $50, Tier 3 is $5,000/mo, and Tier 4 is $50,000/mo. Groq's free plan caps each model individually, for example llama-3.1-8b-instant at 30 RPM, 14,400 requests/day, 6,000 tokens/minute, and 500,000 tokens/day; the Developer plan lifts those and adds Batch and Flex processing.

Cost on a Real Workload

Model Selection: Fireworks by a Wide Margin

Fireworks hosts far more models. Groq trades breadth for speed.

Fireworks runs 100+ open models across text, vision, audio, image generation, and embeddings, with named serverless tiers for DeepSeek V4 Pro ($1.74/$3.48) and V4 Flash ($0.14/$0.28), GLM 5.1 ($1.40/$4.40), Qwen 3.6 Plus ($0.50/$3.00), Kimi K2.6 ($0.95/$4.00), and MiniMax 2.7 ($0.30/$1.20). Anything not pre-listed can run on a dedicated deployment. It also serves embeddings from $0.008 per million input tokens (Qwen3 8B embeddings at $0.10/M), which Groq does not offer.

Groq curates a smaller set tuned to its LPU: Llama variants, GPT-OSS 20B and 120B, Qwen3 32B, Kimi K2, Llama 4 Scout, plus Whisper for speech. If your model is on Groq, it is fast. If it is not, you wait until Groq adds it. Across the open weights both providers carry, the per-token spread is real: Kimi K2.6 runs $0.75/$3.50 on DeepInfra, $0.95/$4.00 on Fireworks, and $1.20/$4.50 on Together, so if a model is the whole job, the cheapest host is worth a check.

For DeepSeek specifically, the host you pick changes the output, not just the price. Most serverless providers quantize activations to fp8 to cut cost, which degrades quality. Morph serves DeepSeek with 16-bit (bf16) activations and never quantizes activations to fp8, so output matches the reference weights. morph-dsv4flash (DeepSeek V4 Flash) is $0.139 input and $0.278 output per million tokens. For coding agents, Morph adds codegen-specific speculative decoding plus custom low-level inference kernels built for code generation, making it the fastest and highest-fidelity place to run DeepSeek when the workload is code. See pricing.

Fine-Tuning & Customization: Fireworks Only

Fireworks is a training-and-serving platform. Groq is a serving platform.

Fireworks ships full LoRA supervised fine-tuning, DPO, and reinforcement fine-tuning. The price scales with model size, billed per million training tokens.

Groq does not offer public fine-tuning. You bring open weights and Groq serves them fast; it is not where you train. If your production model is a fine-tune, Fireworks is the only option of the two, and its rates undercut Together AI's LoRA SFT ($0.48 up to 16B, $1.50 for 17B to 69B) only at the smallest size, so price the exact model size before committing.

Dedicated & Enterprise

Fireworks exposes more of the deployment stack to you; Groq keeps it managed.

Both are SOC 2 Type II compliant. Fireworks is also HIPAA compliant and runs a Zero Data Retention policy that does not log or store prompts or generations for open models without explicit opt-in, with TLS 1.2+ in transit and AES-256 at rest. Groq can process PHI under a BAA on certain GroqCloud services, but preview and beta features and its compound AI systems are excluded from the BAA. For a regulated workload that needs a clean BAA across the whole surface, Fireworks has the more explicit public posture.

When to Use Fireworks AI

• You need a model Groq does not host. DeepSeek V4 Pro/Flash, GLM 5.1, Qwen 3.6 Plus, Kimi K2.6, MiniMax 2.7, or any of 100+ open models, including vision, image, audio, and embeddings.
• You want to fine-tune. LoRA SFT from $0.50 per million training tokens, plus Full SFT, DPO, and reinforcement fine-tuning that scale with model size.
• You run steady high volume. Dedicated H100/H200 at $7/hr converts a per-token bill into a fixed rate.
• You need higher request throughput. A 6,000 RPM ceiling with a payment method, gated only by spending tier.
• You are in a regulated industry. SOC 2 Type II, HIPAA, and Zero Data Retention on open models with no opt-in required.

When to Use Groq

• You need the lowest latency. 394 tok/s on Llama 3.3 70B, 840 tok/s on Llama 3.1 8B, and 1,000 tok/s on GPT-OSS 20B, all published on the pricing page.
• You want predictable response times. Deterministic LPU execution keeps latency flat regardless of batch size, ideal for real-time voice and chat.
• Your model is on the menu. Llama, GPT-OSS 20B/120B, Qwen3 32B, Kimi K2, Llama 4 Scout, and Whisper all run fast.
• You want a cheap Llama 70B. $0.59/$0.79 on Llama 3.3 70B and $0.05/$0.08 on Llama 3.1 8B, models Fireworks does not run on serverless.
• You want zero infrastructure and a free tier. No GPU sizing, just an OpenAI-compatible endpoint that is fast by default, with a free plan at console.groq.com.

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 faster than Fireworks AI?

For single-stream token generation on the models Groq hosts, yes. Groq publishes 394 tok/s on Llama 3.3 70B, 840 tok/s on Llama 3.1 8B, 1,000 tok/s on GPT-OSS 20B, 662 tok/s on Qwen3 32B, and 500 tok/s on GPT-OSS 120B. Fireworks runs Nvidia GPUs and does not publish a fixed tok/s per model because throughput varies with batch size. Groq leads on raw single-stream latency for its menu.

How much does Llama 3.3 70B cost on Groq?

Groq prices Llama 3.3 70B Versatile at $0.59 input and $0.79 output per million tokens, at 394 tok/s with 128k context. Llama 3.1 8B Instant is $0.05/$0.08 at 840 tok/s. Fireworks does not host Llama 3.3 70B on serverless, so a same-model price comparison is not possible. On the shared model GPT-OSS 120B, both charge $0.15 input / $0.60 output.

What are Fireworks AI's rate limits?

Fireworks allows 10 requests per minute without a payment method, and a fixed 6,000 RPM ceiling once you add a valid card. Spending tiers gate monthly budget: Tier 1 $50/mo, Tier 2 $500/mo after $50 spent, Tier 3 $5,000/mo, Tier 4 $50,000/mo. New accounts get $1 in free credits. Groq's free plan caps individual models, for example llama-3.1-8b-instant at 30 RPM and 14,400 requests per day.

Can I fine-tune models on Fireworks AI and Groq?

Fireworks has full LoRA SFT, Full SFT, DPO, and reinforcement fine-tuning. LoRA SFT runs $0.50 per million training tokens up to 16B, $3.00 for 16.1B to 80B, $6.00 for 80B to 300B, and $10.00 above 300B; DPO is 2x the SFT rate. Groq does not offer public fine-tuning; it serves existing open weights rather than training new ones.

Which provider has more open models?

Fireworks, by a wide margin. It hosts 100+ open text, vision, audio, image, and embedding models, plus per-model serverless tiers for DeepSeek V4, GLM 5.1, Qwen 3.6, Kimi K2.6, and MiniMax 2.7. Groq runs a curated set tuned for its LPU and optimized for speed over breadth.

Are Fireworks AI and Groq SOC 2 and HIPAA compliant?

Both are SOC 2 Type II compliant. Fireworks is also HIPAA compliant with a Zero Data Retention policy on open models, TLS 1.2+ in transit, and AES-256 at rest. Groq can process PHI under a BAA on certain GroqCloud services, but preview and beta features and its compound AI systems are excluded from the BAA.