Fireworks AI vs Together AI vs Groq

TL;DR

Groq, Fireworks AI, and Together AI all run open-source models faster and cheaper than OpenAI — but they optimize for different things. Groq (LPU chips) is the fastest raw inference provider, with Llama 3.3 70B at 500+ tokens/second. Fireworks is the most developer-friendly with production features (structured output, fine-tuning, function calling). Together AI has the widest model selection and the best fine-tuning pipeline. If you're choosing between them: Groq for latency-critical apps, Fireworks for production SaaS, Together for research and fine-tuning.

Key Takeaways

• Groq: 500+ tokens/sec on Llama 70B — fastest available, LPU hardware advantage
• Fireworks: best structured output + function calling for open models, production-grade
• Together AI: 200+ models, best fine-tuning workflow, multimodal support
• Cost: all three are 5-20x cheaper than GPT-4o for comparable quality open models
• OpenAI-compatible: all three use the same /chat/completions API format
• When to use: Groq for chatbots, Fireworks for API products, Together for model research

---

The Case for Third-Party Inference

Why not just use OpenAI?

OpenAI GPT-4o:
  Cost:    $5/M input, $15/M output
  Speed:   ~80 tokens/sec
  Models:  OpenAI only (closed source)

Llama 3.3 70B on Groq:
  Cost:    $0.59/M input, $0.79/M output
  Speed:   500+ tokens/sec
  Models:  Open source, no vendor lock-in

For many tasks (code, Q&A, summarization):
Llama 70B quality ≈ GPT-4o quality
Cost: 15-20x cheaper
Speed: 6x faster

---

Groq: The Speed King

Best for: real-time applications, latency-sensitive chatbots, anything needing sub-second response

// Groq is OpenAI API-compatible — drop-in replacement:
import Groq from 'groq-sdk';

const groq = new Groq({ apiKey: process.env.GROQ_API_KEY });

const completion = await groq.chat.completions.create({
  messages: [{ role: 'user', content: 'Explain quantum computing in 3 sentences.' }],
  model: 'llama-3.3-70b-versatile',
  temperature: 0.7,
  max_tokens: 500,
  stream: true,   // Streaming works the same as OpenAI
});

for await (const chunk of completion) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? '');
}

// Or with the raw OpenAI SDK (just change baseURL):
import OpenAI from 'openai';

const groqClient = new OpenAI({
  apiKey: process.env.GROQ_API_KEY,
  baseURL: 'https://api.groq.com/openai/v1',
});

// Exact same API as OpenAI from here

Groq Models (2026)

Model	Tokens/sec	Context	Input $/M	Output $/M
llama-3.3-70b-versatile	500+	128K	$0.59	$0.79
llama-3.1-8b-instant	750+	128K	$0.05	$0.08
mixtral-8x7b-32768	500+	32K	$0.24	$0.24
gemma2-9b-it	600+	8K	$0.20	$0.20
llama-3.2-90b-vision	300+	128K	$0.90	$0.90

How Groq achieves this: LPU (Language Processing Unit) — custom silicon designed specifically for sequential token generation. Unlike GPUs (optimized for parallelism), LPUs excel at the autoregressive nature of LLM decoding.

Groq Limitations

❌ No fine-tuning (fixed public models only)
❌ Limited model selection vs Together/Fireworks
❌ No persistent storage or embeddings
❌ Rate limits more aggressive than competitors
✅ Best latency
✅ OpenAI-compatible API
✅ Predictable performance

---

Fireworks AI: Production-Grade Open Models

Best for: production API products, structured output requirements, function calling with open models

import OpenAI from 'openai';

const fireworks = new OpenAI({
  apiKey: process.env.FIREWORKS_API_KEY,
  baseURL: 'https://api.fireworks.ai/inference/v1',
});

// Structured output (Fireworks FireFunction-v2 is best for this):
const response = await fireworks.chat.completions.create({
  model: 'accounts/fireworks/models/firefunction-v2',
  messages: [
    { role: 'user', content: 'Extract the name and email from: "John Smith, john@example.com"' },
  ],
  response_format: { type: 'json_object' },  // Guaranteed JSON
  tools: [
    {
      type: 'function',
      function: {
        name: 'extract_contact',
        description: 'Extract contact information',
        parameters: {
          type: 'object',
          properties: {
            name: { type: 'string' },
            email: { type: 'string', format: 'email' },
          },
          required: ['name', 'email'],
        },
      },
    },
  ],
  tool_choice: { type: 'function', function: { name: 'extract_contact' } },
});

Fireworks Key Differentiators

1. FireFunction-v2 — open-source model fine-tuned specifically for function calling:

// FireFunction-v2 matches GPT-4 on function calling benchmarks
// At $0.90/M tokens vs GPT-4o's $15/M output
model: 'accounts/fireworks/models/firefunction-v2'

2. Structured Output with any model:

// Fireworks supports structured JSON output on most models
// via response_format or grammar-based sampling:
const response = await fireworks.chat.completions.create({
  model: 'accounts/fireworks/models/llama-v3p3-70b-instruct',
  response_format: {
    type: 'json_schema',
    json_schema: {
      name: 'product_review',
      schema: {
        type: 'object',
        properties: {
          sentiment: { type: 'string', enum: ['positive', 'negative', 'neutral'] },
          score: { type: 'number', minimum: 1, maximum: 5 },
          summary: { type: 'string' },
        },
        required: ['sentiment', 'score', 'summary'],
      },
    },
  },
  messages: [{ role: 'user', content: `Review: "${reviewText}"` }],
});

3. Fine-tuning pipeline:

// Upload training data:
const formData = new FormData();
formData.append('file', fs.createReadStream('training.jsonl'));
await fetch('https://api.fireworks.ai/v1/files', {
  method: 'POST',
  headers: { Authorization: `Bearer ${process.env.FIREWORKS_API_KEY}` },
  body: formData,
});

// Create fine-tuning job:
await fetch('https://api.fireworks.ai/v1/fine_tuning/jobs', {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${process.env.FIREWORKS_API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'llama-v3p1-8b-instruct',
    training_file: 'file-abc123',
    hyperparameters: { n_epochs: 3, learning_rate_multiplier: 1.0 },
  }),
});

Fireworks Models

Model	Context	Input $/M	Notes
llama-v3p3-70b-instruct	131K	$0.90	Best general purpose
firefunction-v2	8K	$0.90	Best function calling
llama-v3p2-11b-vision	131K	$0.20	Vision + text
phi-3-vision-128k	128K	$0.20	Lightweight vision
mixtral-8x22b-instruct	65K	$0.90	Complex reasoning
llama-v3p1-405b-instruct	131K	$3.00	Most capable open model

---

Together AI: The Model Research Platform

Best for: trying many open-source models, fine-tuning experiments, teams researching model capabilities

import OpenAI from 'openai';

const together = new OpenAI({
  apiKey: process.env.TOGETHER_API_KEY,
  baseURL: 'https://api.together.xyz/v1',
});

const response = await together.chat.completions.create({
  model: 'meta-llama/Llama-3.3-70B-Instruct-Turbo',
  messages: [{ role: 'user', content: 'What is 17 * 23?' }],
  max_tokens: 100,
  temperature: 0.1,
});

Together's 200+ Model Selection

AI21 Labs:
  jamba-1.5-large, jamba-1.5-mini

Alibaba:
  Qwen/Qwen2.5-72B-Instruct, Qwen/QwQ-32B-Preview

Deepseek:
  deepseek-ai/DeepSeek-V3, deepseek-ai/DeepSeek-R1

Google:
  google/gemma-2-27b-it, google/gemma-2-9b-it

Meta:
  meta-llama/Llama-3.3-70B-Instruct-Turbo
  meta-llama/Llama-3.1-405B-Instruct-Turbo
  meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo

Mistral:
  mistralai/Mixtral-8x22B-Instruct-v0.1
  mistralai/Mistral-7B-Instruct-v0.3

NovaSky:
  NovaSky-AI/Sky-T1-32B-Preview

Nvidia:
  nvidia/Llama-3.1-Nemotron-70B-Instruct-HF

Together's Fine-Tuning (Most Complete)

Together has the most complete fine-tuning pipeline among the three:

// Together fine-tuning with LoRA:
const fineTuneJob = await fetch('https://api.together.xyz/v1/fine-tunes', {
  method: 'POST',
  headers: {
    Authorization: `Bearer ${process.env.TOGETHER_API_KEY}`,
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    model: 'meta-llama/Meta-Llama-3-8B-Instruct-Reference',
    training_file: 'file-abc123',
    n_epochs: 3,
    learning_rate: 1e-5,
    batch_size: 16,
    lora: true,         // LoRA fine-tuning (cheaper, faster)
    lora_rank: 8,
    lora_alpha: 16,
    lora_dropout: 0.05,
  }),
}).then((r) => r.json());

// After fine-tuning, deploy as dedicated endpoint:
// model: 'your-org/your-fine-tuned-model'

Together Embeddings

Together is the only provider with competitive embeddings:

const embedding = await together.embeddings.create({
  model: 'togethercomputer/m2-bert-80M-8k-retrieval',
  input: 'text to embed',
});
// 768-dimension embeddings at $0.008/M tokens

---

Speed Benchmark (2026)

Real-world tokens/second on Llama 3.3 70B:

Provider	Tokens/sec (output)	First token latency
Groq	500-700	~80ms
Fireworks	100-150	~200ms
Together	80-120	~250ms
OpenAI GPT-4o	80-100	~400ms
Anthropic Claude 3.5	60-80	~500ms

Groq's LPU advantage is real and significant — 3-5x faster than GPU-based providers.

---

Cost Comparison at Scale

For a chatbot handling 1M queries/month (avg 500 input + 200 output tokens):

Provider + Model	Monthly Cost	Notes
Groq Llama 3.3 70B	$445	$0.59+$0.79 /M
Fireworks Llama 70B	$630	$0.90 /M both
Together Llama 70B	$445	Similar to Groq
OpenAI GPT-4o-mini	$750	$0.15+$0.60 /M
OpenAI GPT-4o	$7,500	$5+$15 /M

All three open model providers are 10-17x cheaper than GPT-4o for the same volume.

---

Decision Framework

Use GROQ if:
  → Chatbot or voice app requiring <200ms response
  → User-facing real-time streaming
  → You don't need fine-tuning

Use FIREWORKS if:
  → You need reliable structured output (JSON schemas)
  → Function calling with open models
  → Production API product where schema compliance matters
  → Fine-tuning with production deployment

Use TOGETHER if:
  → You need to try 10+ different models
  → Research or model comparison
  → Fine-tuning with LoRA and custom hyperparameters
  → Embeddings alongside completions
  → You want DeepSeek R1 or other newer models first

Use all three (via abstraction):
  → Vercel AI SDK or LiteLLM can route to any provider
  → A/B test models across providers
  → Failover: if Groq rate limits, fall back to Together

---

Multi-Provider Abstraction

// Route to fastest available provider:
import OpenAI from 'openai';

type Provider = 'groq' | 'fireworks' | 'together';

const providers: Record<Provider, OpenAI> = {
  groq: new OpenAI({
    apiKey: process.env.GROQ_API_KEY,
    baseURL: 'https://api.groq.com/openai/v1',
  }),
  fireworks: new OpenAI({
    apiKey: process.env.FIREWORKS_API_KEY,
    baseURL: 'https://api.fireworks.ai/inference/v1',
  }),
  together: new OpenAI({
    apiKey: process.env.TOGETHER_API_KEY,
    baseURL: 'https://api.together.xyz/v1',
  }),
};

const modelMap: Record<Provider, string> = {
  groq: 'llama-3.3-70b-versatile',
  fireworks: 'accounts/fireworks/models/llama-v3p3-70b-instruct',
  together: 'meta-llama/Llama-3.3-70B-Instruct-Turbo',
};

async function inferWithFallback(prompt: string, preferredProvider: Provider = 'groq') {
  const order: Provider[] = [
    preferredProvider,
    ...(['groq', 'fireworks', 'together'] as Provider[]).filter((p) => p !== preferredProvider),
  ];

  for (const provider of order) {
    try {
      const response = await providers[provider].chat.completions.create({
        model: modelMap[provider],
        messages: [{ role: 'user', content: prompt }],
      });
      return response.choices[0].message.content;
    } catch (err) {
      console.error(`${provider} failed, trying next...`, err);
    }
  }
  throw new Error('All providers failed');
}

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