Groq vs Fireworks AI: pricing per million tokens

When you’re squeezing inference cost out of a production LLM pipeline, the Groq vs Fireworks AI pricing per token decision directly dictates your margin. Both platforms serve open-weight models through OpenAI-compatible endpoints, but they diverge hard on hardware, model catalog, and rate limit behavior.

Capabilities

Groq runs inference on custom LPUs (Language Processing Units). The catalog is deliberately narrow: Llama 3 (8B, 70B), Mixtral 8x7B, Gemma 7B, and a few others. You get chat completions, streaming, function calling on supported models, and JSON mode. You do not get hosted fine-tuning or proprietary model blends.

Model strings and versioning

Groq identifies models by short tags: llama3-70b-8192, mixtral-8x7b-32768. The numeric suffix is the context window. Fireworks uses paths like accounts/fireworks/models/llama-v3-70b-instruct and suffixes -finetuned for custom jobs. The verbose names complicate config files but make multi-tenant routing explicit.

Fireworks AI casts a wider net. Beyond the standard Llama and Mixtral derivatives, they ship FireLLM (their own tuned mixes), DeepSeek variants, and multi-modal image models. Fine-tuning is a first-class feature—upload a dataset, train a LoRA or full adapter, and serve it on the same endpoint. If your product needs a custom weight or image generation alongside text, Fireworks is the only one of the two that clears the bar.

# Groq does not support fine-tune serving; Fireworks does:
from openai import OpenAI

fw = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key="FW_KEY")
resp = fw.chat.completions.create(
    model="accounts/fireworks/models/llama-v3-70b-finetuned",
    messages=[{"role": "user", "content": "Summarize this ticket"}]
)

Price/cost model

Both providers bill strictly per token with no monthly minimum. The difference is in the per-million rates and how they treat cached prompts.

Groq’s public rates for Llama 3 70B sit at $0.59 per million input tokens and $0.79 per million output tokens. The 8B variant drops to $0.05 input / $0.10 output. These numbers are aggressively low because the LPU amortizes compute differently than GPUs.

Fireworks prices the same Llama 3 70B at roughly $0.90 per million input and $0.90 per million output, with 8B around $0.20 / $0.20. Fireworks applies prompt caching discounts—repeated prefixes are cheaper after the first call. Groq has begun honoring cache-control hints on some models but does not yet publish a separate cached-token rate.

The Groq vs Fireworks AI pricing per token gap widens at scale: a 70B app processing 1B input tokens/month pays ~$590 on Groq vs ~$900 on Fireworks before caching. Output tokens usually dominate cost because generation is longer than prompts in chat; Groq’s $0.79 vs $0.90 output is a smaller delta than input but still measurable.

{
  "groq_llama3_70b": {"input_per_m": 0.59, "output_per_m": 0.79},
  "fireworks_llama3_70b": {"input_per_m": 0.90, "output_per_m": 0.90}
}

Cache mechanics

Fireworks reads cache_control in the message list and bills cached input at a reduced rate (often 50%). Groq’s support is partial: set "cache_control": {"type": "ephemeral"} on the system message and the platform may reuse KV caches across requests, but the invoice line item is not separated. For high-repeat RAG prefixes, Fireworks gives predictable savings.

Latency/throughput

Groq’s LPUs are built for token velocity. Measured community benchmarks show Llama 3 70B generating 200–300 tokens/sec per stream. Fireworks on A100/H100 clusters delivers 80–120 tokens/sec for the same model. For interactive chat, both feel instant; for bulk extraction over millions of documents, Groq’s throughput cuts wall-clock time by 2–3x.

Queueing and batching

Groq’s free tier frequently returns 429s during peak because LPU capacity is finite. Paid accounts get priority queues. Fireworks scales horizontally on GPU pools, so sustained batch jobs see fewer hard throttles but higher per-token cost. If you submit async batches of 1000 requests, Fireworks’ throughput per dollar may beat Groq once you factor in retry overhead.

Fireworks counters with consistent batch throughput and multi-modal endpoints that Groq lacks. If you need to caption 10k images, Groq won’t help.

Ergonomics

Both expose an OpenAI-compatible REST surface, so the openai Python client works with a base_url swap. Groq originally shipped a dedicated groq SDK, but the OpenAI shim is now the documented path. Fireworks uses the same pattern.

from openai import OpenAI

groq = OpenAI(base_url="https://api.groq.com/openai/v1", api_key="GROQ_KEY")
groq.chat.completions.create(
    model="llama3-70b-8192",
    messages=[{"role": "user", "content": "Ping"}]
)

fw = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key="FW_KEY")
fw.chat.completions.create(
    model="llama-v3-70b-instruct",
    messages=[{"role": "user", "content": "Ping"}]
)

Header differences are minimal. Fireworks requires Authorization: Bearer; Groq uses the same. Fireworks supports x-fireworks-* headers for routing custom models; Groq is simpler with just model name strings.

When you route through a gateway such as n4n.ai, you collapse both into one OpenAI-compatible endpoint that addresses 240+ models, with automatic fallback when a provider is rate-limited and per-token metering on the unified bill.

Error shapes

Both return OpenAI-style error objects. Groq’s 429 includes x-ratelimit-reset seconds; Fireworks returns retry-after header. Wrap calls in a ten-line backoff:

import time
def complete(client, **kwargs):
    for i in range(4):
        try:
            return client.chat.completions.create(**kwargs)
        except Exception as e:
            if e.status_code == 429:
                time.sleep(2 ** i)
    raise

Ecosystem

Groq’s ecosystem is lean: API, a playground, and community weights. It integrates with LangChain and LlamaIndex via the OpenAI adapter. There’s no model marketplace.

Fireworks ships a model registry, fine-tune job manager, evaluation harness, and SDKs for JS/Python. Their image API mirrors the chat API shape. For teams that want to iterate on weights without standing up training infra, Fireworks removes a lot of glue code.

LangChain example

from langchain_openai import ChatOpenAI

groq_llm = ChatOpenAI(base_url="https://api.groq.com/openai/v1",
                      api_key="GROQ_KEY", model="llama3-70b-8192")
fw_llm = ChatOpenAI(base_url="https://api.fireworks.ai/inference/v1",
                    api_key="FW_KEY", model="llama-v3-70b-instruct")

The same abstraction runs both; the cost difference is invisible until the invoice.

Limits

Groq enforces tight per-minute token quotas on free tier (e.g., 30k/min). Paid tier raises limits but you still hit global LPU capacity during peaks. Context windows max at 8k–128k depending on model.

Fireworks rate limits scale with spend; default 100 req/min on paid. They support up to 128k context on selected models and allow concurrent fine-tune jobs.

Neither offers private VPC isolation at the entry tier. If compliance demands dedicated hardware, you’ll need enterprise contracts with either.

Head-to-head table

Which to choose

High-volume text inference on standard open models. Pick Groq. The Groq vs Fireworks AI pricing per token advantage is real, and the LPU throughput shrinks batch jobs. If you’re serving Llama 3 70B to millions of users, the $0.30/M output gap compounds.

Custom weights or image+text pipelines. Fireworks wins by default. Groq can’t fine-tune or caption images. The extra $0.11/M input is cheap compared to running your own GPU cluster.

Latency-sensitive but variable traffic. Use Groq as primary with Fireworks as overflow. A gateway that honors client routing directives can send cacheable prefixes to Fireworks and burst to Groq when Fireworks throttles.

Prototype to production with one codebase. Fireworks’ registry and eval tools keep you inside their console. Groq forces you to bolt on external tooling.

Strict per-token budgeting across many providers. Route both through a unified meter. The earlier n4n.ai note stands: one endpoint, fallback, and cache-control forwarding removes the comparison from your app code entirely.

The Groq vs Fireworks AI pricing per token story is not complicated—Groq is the cheap fast text specialist; Fireworks is the broader platform. Choose based on whether you need only tokens or a model workshop.