Fireworks AI API Pricing & Model Rates
Fireworks AI delivers state-of-the-art inference speeds powered by their proprietary FireAttention engine. Explore token pricing, serverless LoRA deployment, multimodal APIs, and enterprise SLAs.
Fireworks AI Serverless Compute Simulator
Calculate monthly bills across Llama 3.3 70B, DeepSeek-V3, and specialized multimodal models.
Production Pricing & Model Tier Rates
Verified rates, context capacities, typical inference latency, and recommended enterprise workloads.
| Model / Tier | Input Rate (1M) | Output Rate (1M) | Context Window | Latency / Throughput | Recommended Workload |
|---|---|---|---|---|---|
| Llama 3.3 70B Instruct | $0.900 | $0.900 | 128,000 | 140–180 tok/s | High-throughput reasoning, production agents, code gen |
| Llama 3.1 8B Instruct | $0.200 | $0.200 | 128,000 | 220–280 tok/s | Sub-150ms classification, routing, and autocomplete |
| DeepSeek-V3 (671B MoE) | $0.900 | $0.900 | 64,000 | 60–90 tok/s | High-volume coding, analysis (cheapest hosted V3) |
| DeepSeek-R1 (Reasoning) | $1.200 | $2.400 | 64,000 | 40–60 tok/s | Complex math, olympiad reasoning, formal logic |
| FireFunction-v2 | $0.900 | $0.900 | 32,768 | 140–180 tok/s | GPT-4 level function calling on open-source weights |
| Flux.1 Schnell (Image) | $0.003 / image | N/A | N/A | Sub-1s | Ultra-fast photorealistic text-to-image generation |
⚡ Architectural Deep Dive & Unit Economics Analysis
Fireworks AI differentiates itself from standard vLLM clouds through custom CUDA kernel engineering. Built by the ex-Meta PyTorch and compiler core teams, Fireworks developed FireAttention, a proprietary attention engine that delivers up to 4x throughput and reduces Time-To-First-Token (TTFT) by 25–35% during high concurrency.
For developers building compound AI systems, Fireworks offers FireFunction-v2—a custom fine-tuned model that matches GPT-4 on structured tool execution and multi-turn function calling at a fraction of the cost. Additionally, Fireworks allows developers to deploy custom LoRA adapters with zero infrastructure overhead or hosting surcharges, hot-swapping adapters seamlessly across base model clusters.
Production Workloads & Monthly Cost Modeling
Evaluating practical unit economics across real-world application architectures.
Low-Latency Customer Voice Assistant
Streaming multi-turn dialogue where TTFT under 200ms is essential for user experience. Fireworks AI FireAttention delivers superior responsiveness.
High-Volume DeepSeek-V3 Production Pipeline
Running 100M tokens monthly of deep analysis. Fireworks AI offers DeepSeek-V3 at $0.90/M versus Together AI's $1.25/M, saving $35/mo per 100M.
Autonomous Agent Tool Calling with FireFunction
Calling external APIs with strict JSON arguments. FireFunction-v2 delivers 98%+ schema adherence at $0.90/M tokens.
import os
from openai import OpenAI
# Fireworks AI is 100% compatible with the OpenAI Python SDK
client = OpenAI(
api_key=os.environ.get("FIREWORKS_API_KEY"),
base_url="https://api.fireworks.ai/inference/v1"
)
def run_firefunction_agent(prompt: str, tools: list):
"""Execute structured tool calling on open weights with FireFunction-v2."""
response = client.chat.completions.create(
model="accounts/fireworks/models/firefunction-v2",
messages=[{"role": "user", "content": prompt}],
tools=tools,
temperature=0.1
)
return response.choices[0].messageOperational Gotchas & Failure Modes
Engineering realities, throttling thresholds, and hidden surcharge traps to monitor.
Related Models, Showdowns & Benchmarks
Cross-reference provider rates against hosted models, direct head-to-head battles, and calculators.
Together AI vs Fireworks AI Showdown
Detailed comparison of inference speed, LoRA support, and pricing.
Together AI Pricing Breakdown
Explore dedicated instance pricing and full-parameter fine-tuning.
Cheapest LLM APIs of 2026
Rankings of all low-cost token providers on the market.
LLM Fine-Tuning ROI Calculator
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Frequently Asked Questions
What is Fireworks AI's pricing for Llama 3.3 70B?
Fireworks AI charges a flat $0.90 per million input tokens and $0.90 per million output tokens for Llama 3.3 70B.
What makes Fireworks AI faster than other GPU clouds?
Fireworks AI built FireAttention, an optimized CUDA inference kernel that minimizes memory bandwidth bottlenecks, reducing Time-To-First-Token by roughly 25–35% during high concurrency.
What is FireFunction-v2?
FireFunction-v2 is Fireworks AI's custom fine-tuned model trained specifically for function calling, structured JSON output, and multi-turn agent tool routing, rivaling GPT-4 at open-source token pricing.
Can I host custom LoRA adapters on Fireworks AI?
Yes! You can upload Hugging Face LoRA weights and call them on serverless base models without paying dedicated GPU fees or hosting surcharges.
Does Fireworks AI support DeepSeek-V3 and R1?
Yes! Fireworks AI hosts DeepSeek-V3 for $0.90/M tokens (cheaper than Together AI's $1.25/M) and DeepSeek-R1 for $1.20 in / $2.40 out.
Is Fireworks AI compatible with the OpenAI SDK?
Yes! Setting base_url to 'https://api.fireworks.ai/inference/v1' makes Fireworks AI a drop-in replacement with the official OpenAI library.
Fireworks AI Serverless Architecture: Speculative Decoding & Function Calling
Fireworks AI is recognized for ultra-low latency inference, pioneering production speculative decoding engines (FireAttention) that deliver up to 140 tokens per second on Llama 3.3 70B and Qwen 2.5. Beyond raw LLM generation ($0.20/M in, $0.90/M out on 70B models), Fireworks provides serverless Function Calling models fine-tuned specifically for tool-calling agents, alongside hosted image generation (Flux.1 Schnell at $0.003 per image) with sub-second execution times.
Fireworks AI Rate Card & Performance Benchmarks
| Model Architecture | Input ($/M) | Output ($/M) | Throughput | Context Window |
|---|---|---|---|---|
| Llama 3.3 70B Instruct | $0.20 | $0.90 | 140 tok/s | 128,000 tokens |
| Qwen 2.5 Coder 32B | $0.15 | $0.60 | 160 tok/s | 32,000 tokens |
| Llama 3.1 8B Instruct | $0.05 | $0.20 | 220 tok/s | 128,000 tokens |
| Flux.1 Schnell (Image) | $0.003 / image | N/A | 0.8s / generation | 1024 × 1024 resolution |
Structured JSON Mode Client (Python)
from openai import OpenAI
client = OpenAI(
base_url="https://api.fireworks.ai/inference/v1",
api_key=os.environ["FIREWORKS_API_KEY"]
)
# Constrain model decoding to strict JSON schema with zero output token hallucination
response = client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p3-70b-instruct",
messages=[{"role": "user", "content": "Extract user profile entities from document."}],
response_format={"type": "json_object"}
)Engineering Q&A: Fireworks AI
How does FireAttention speculative decoding increase generation speed?
FireAttention uses a compact draft model to predict multiple future tokens in parallel, which the larger 70B target model verifies in a single forward pass. This reduces memory bandwidth bottlenecks, boosting generation speed by 2x to 3x with zero loss in mathematical precision.
Can developers deploy custom LoRA adapters on Fireworks AI?
Yes. Fireworks supports dynamic serverless LoRA switching. You can upload fine-tuned weight adapters to Fireworks storage and invoke them on base foundation models with zero server cold start latency or dedicated GPU reservation fees.