Fireworks AI API Review 2026: Fast Inference, Fine-Tuning & 100+ Models

Introduction

Fireworks AI is a San Francisco-based inference platform that differentiates itself from competitors like Groq, DeepInfra, and Together AI through three core strengths: production-grade fine-tuning, Firefunction-v2 (the highest-accuracy function calling model on the market), and a broad model catalog spanning Meta Llama, Alibaba Qwen, Mistral, DeepSeek, and Google Gemma.

Unlike Groq's specialized LPU hardware or Cerebras's wafer-scale chips, Fireworks AI runs on standard NVIDIA H100 GPU clusters optimized with their proprietary FireCompiler and FireInfer engines. This means broader model support, lower cold-start latency, and the ability to fine-tune models at a fraction of the cost of self-hosted solutions.

In this comprehensive review, we cover Fireworks AI's pricing structure, model catalog, Firefunction-v2 capabilities, fine-tuning workflow, free tier, China access, and head-to-head comparisons with Groq, DeepInfra, and Together AI.

Supported Models

Fireworks AI hosts over 100 models across multiple categories. Here are the most significant ones:

Pricing

Fireworks AI uses a per-token pricing model that varies by model tier. The platform does not charge for cached input tokens — automatic prompt caching is included at no extra cost, making it extremely cost-effective for workloads with repeated system prompts.

LLM Pricing (per 1M tokens)

Fine-Tuning Pricing

Note: fine-tuning training costs are one-time. After training, the model is hosted on Fireworks' infrastructure with free inference for the first 5M tokens per month, then standard inference pricing applies.

Image Generation Pricing

Firefunction-v2: The Industry's Best Function Calling

Fireworks AI's flagship model, Firefunction-v2, is a fine-tuned variant based on Llama 3.3 that achieves 97.1% accuracy on the Berkeley Function Calling Leaderboard (BFCL v2). To put this in perspective:

• Firefunction-v2: 97.1%
• GPT-4o: 94.8%
• Claude 3.5 Sonnet: 93.2%
• DeepSeek V3: 91.5%
• Gemini 2.5 Pro: 90.8%

Firefunction-v2 uses a novel dual-output format: it can produce both JSON function calls AND natural language responses in a single API call. This means an agent can call a function AND explain the result to the user in one round-trip, dramatically reducing latency for chatbot-with-tools architectures.

The model also supports parallel function calling (up to 10 simultaneous tool calls), nested function schemas, and streaming function calls — where partial function arguments are streamed before the call completes.

import openai

client = openai.OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="YOUR_FIREWORKS_API_KEY"
)

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a city",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="accounts/fireworks/models/firefunction-v2",
    messages=[{"role": "user", "content": "What is the weather in Tokyo?"}],
    tools=tools,
    tool_choice="auto"
)

# Firefunction-v2 returns both tool calls and assistant response
print(response.choices[0].message.tool_calls)
print(response.choices[0].message.content)

Managed Fine-Tuning

Fireworks AI offers one of the most developer-friendly fine-tuning pipelines on the market. Unlike Together AI (which requires CLI tools) or DeepInfra (which doesn't support fine-tuning at all), Fireworks provides a fully managed web UI and API for fine-tuning.

Fine-Tuning Workflow

1. Prepare data: Format your training data as JSONL with messages-completions format (same as OpenAI) or chat format
2. Upload: Upload via the Fireworks dashboard or the files.create API endpoint
3. Train: Select a base model, training epochs, and learning rate. Training typically completes in 15-60 minutes depending on model size
4. Deploy: The fine-tuned model is automatically hosted on Fireworks infrastructure with zero cold-start

import openai

client = openai.OpenAI(
    base_url="https://api.fireworks.ai/inference/v1",
    api_key="YOUR_FIREWORKS_API_KEY"
)

# 1. Upload training file
training_file = client.files.create(
    file=open("training_data.jsonl", "rb"),
    purpose="fine-tune"
)

# 2. Create fine-tune job
job = client.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="accounts/fireworks/models/llama-v3p3-70b-instruct",
    hyperparameters={
        "n_epochs": 3,
        "batch_size": 8,
        "learning_rate_multiplier": 0.002
    }
)

print(f"Fine-tune job created: {job.id}")
# Job typically completes in 20-40 minutes for 70B models

Free Tier

Fireworks AI offers a generous $25 in free credits for new accounts, which is more than Groq's rate-limited free tier ($0 free credits, just rate limits) but less than DeepInfra's $50 credit offer. The $25 credit is enough for:

• ~25M tokens of Llama 3.3 8B inference (input + output)
• ~14,000 images with FLUX.1-Dev
• One fine-tuning run on Llama 3.3 70B with ~8K training tokens

Free-tier accounts are rate-limited to 10 requests per minute and 50,000 tokens per minute (Rate Limit 1). Paid accounts get higher limits and priority queue access. The free credits never expire, making Fireworks an excellent platform for long-term prototyping.

Performance & Speed

Fireworks AI uses a proprietary inference engine called FireInfer that runs on NVIDIA H100 GPUs. Here's how it compares to competitors on key metrics:

China Access

Like most US-based AI API providers, Fireworks AI is blocked in mainland China. The API endpoint api.fireworks.ai is subject to GFW restrictions. Chinese developers have three options:

• Proxy/VPN: Route traffic through a Hong Kong or Singapore proxy. Fireworks' API is OpenAI-compatible, so standard proxy configurations for OpenAI work identically
• China-direct alternatives: SiliconFlow (硅基流动) offers 100+ open-source models at ¥0.4/M tokens with direct China access. FreeModel is another option with DeepSeek official partnership
• Aggregator routing: Use OpenRouter with a proxy-capable backend to route to Fireworks when available, falling back to China-direct providers

Pros and Cons

• ✅ Firefunction-v2: Best function calling model on the market (97.1% BFCL)
• ✅ Fully managed fine-tuning with auto-deployment — no GPU management
• ✅ Free prompt caching — automatic, no configuration needed
• ✅ $25 free credits for new users — never expire
• ✅ 100+ models including Llama, Qwen, DeepSeek, image gen, and embedding
• ✅ OpenAI-compatible API — drop-in replacement for existing code
• ⚠️ More expensive than DeepInfra on base models (2-5x on some)
• ⚠️ Slower than Groq on raw inference speed (400-600 vs 1,200+ tok/s)
• ⚠️ Blocked in China — requires proxy or alternative
• ⚠️ No dedicated SLA for free-tier accounts

Use Cases

Comparison: Fireworks AI vs Groq vs DeepInfra vs Together AI

Choosing between these four providers depends on your priorities:

• Choose Fireworks AI if you need function calling (Firefunction-v2), managed fine-tuning, or a broad model catalog with free prompt caching. Best for AI agents and custom model training.
• Choose Groq if raw inference speed is your top priority — 1,200+ tok/s on Llama 3.3 70B with virtually zero cold-start latency. Limited model catalog, no fine-tuning.
• Choose DeepInfra if you want the lowest possible price on open-source models — 30-50% cheaper than competitors on most models. No fine-tuning, basic features.
• Choose Together AI if you need fine-tuning AND want the widest model selection (200+ models). CLI-based fine-tuning with more configuration options than Fireworks.

Conclusion

Fireworks AI occupies a unique position in the 2026 AI inference landscape. It is not the cheapest (DeepInfra wins on price), not the fastest (Groq wins on speed), and not the widest (Together AI has more models). But Fireworks AI is the best platform for developers building AI agents with function calling, thanks to Firefunction-v2's industry-leading accuracy and its dual-response format.

The managed fine-tuning pipeline is a close second in value — being able to fine-tune Llama 3.3 70B with a single API call and get auto-hosted inference is a game-changer for teams without dedicated ML infrastructure.

For Chinese developers, the GFW restriction is a significant limitation, but for international teams building production AI applications, Fireworks AI deserves serious consideration alongside Groq and DeepInfra.

Best for: AI agent developers, teams needing custom fine-tuning without GPU management, function-calling-heavy workloads, and developers who want a single API for LLMs, images, and embeddings.