Together AI vs Fireworks vs Groq (2026): Fast Inference APIs Compared

OpenAI and Anthropic have the best models. But they're not the fastest or cheapest options for running open-source models like Llama, Mistral, and DeepSeek. A new class of inference providers has emerged to fill that gap.

Together AI, Fireworks, and Groq all offer fast inference for open-source models. They differ significantly in speed, pricing, model selection, and reliability.

Quick Summary

Groq

What Makes Groq Different

Groq doesn't use GPUs. They built custom hardware called LPUs (Language Processing Units) specifically optimized for transformer inference. The result: dramatically faster generation than GPU-based providers.

Measured speeds (Llama 3.3 70B):

• Average: 750-900 tokens/second
• Peak: 1,000+ tokens/second
• Time to first token: 200-400ms

For context, typical GPU inference produces 50-150 tokens/second. Groq is 5-15x faster.

Pricing

Code Example

from groq import Groq

client = Groq(api_key="your-groq-api-key")

# Groq uses its own SDK (also OpenAI-compatible)
response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum entanglement in 2 paragraphs."}
    ],
    temperature=0.7,
    max_tokens=1024
)

print(response.choices[0].message.content)
print(f"Tokens/sec: {response.usage.completion_tokens / response.usage.completion_time:.0f}")

# OpenAI-compatible (no Groq SDK needed)
from openai import OpenAI

client = OpenAI(
    api_key="your-groq-api-key",
    base_url="https://api.groq.com/openai/v1"
)

response = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Hello"}]
)

Streaming Example

# Streaming is where Groq's speed advantage is most noticeable
stream = client.chat.completions.create(
    model="llama-3.3-70b-versatile",
    messages=[{"role": "user", "content": "Write a Python function to merge two sorted lists"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Groq's Limitations

• Limited model selection: Groq only supports specific models optimized for their LPU hardware. No GPT-4o, no Claude, and many open-source models aren't available.
• Context window limits: Most models limited to 32K-128K tokens on Groq vs larger windows elsewhere.
• Rate limits: Free tier is quite limited. Production usage requires a paid plan.
• No fine-tuning: You can't fine-tune models on Groq.

Best Use Cases for Groq

• Real-time applications where latency is critical (live voice, real-time translation)
• Chatbots where response speed directly affects user experience
• High-throughput pipelines that need to process many requests quickly
• Applications using Llama 3.x models specifically

Fireworks AI

What Makes Fireworks Different

Fireworks positions itself on reliability and production-readiness more than raw speed. They offer more consistent latency, a broader model catalog including first-party integrations with model creators, and better enterprise support.

Measured speeds (Llama 3.3 70B):

• Average: 150-200 tokens/second
• Time to first token: 300-600ms
• Consistency: Lower variance than Groq

Pricing

Code Example

# Fireworks is OpenAI-compatible
from openai import OpenAI

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

response = client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p3-70b-instruct",
    messages=[{"role": "user", "content": "What are the SOLID principles?"}],
    max_tokens=2048
)

print(response.choices[0].message.content)

# Structured output with Fireworks
response = client.chat.completions.create(
    model="accounts/fireworks/models/llama-v3p3-70b-instruct",
    messages=[{"role": "user", "content": "Extract: Alice, 30, doctor"}],
    response_format={
        "type": "json_object",
        "schema": {
            "type": "object",
            "properties": {
                "name": {"type": "string"},
                "age": {"type": "integer"},
                "profession": {"type": "string"}
            }
        }
    }
)

Fireworks-Specific Features

Function Calling / Tool Use:

response = client.chat.completions.create(
    model="accounts/fireworks/models/firefunction-v2",  # Fireworks' specialized function-calling model
    messages=[{"role": "user", "content": "What's the weather in San Francisco?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {"type": "string"}
                },
                "required": ["location"]
            }
        }
    }]
)

Fine-tuning:

Fireworks supports fine-tuning, which Groq does not. You can upload training data and fine-tune Llama 3 or Mistral models for your specific domain.

Best Use Cases for Fireworks

• Production applications needing consistent reliability
• Function calling / tool use with open-source models
• Applications needing fine-tuned models
• Teams that need DeepSeek R1 specifically (Fireworks has good R1 support)
• When Groq's model selection is too limited

Together AI

What Makes Together Different

Together has the broadest model catalog of the three, including many models not available elsewhere. They also have the most mature fine-tuning pipeline and support for training custom models.

Measured speeds (Llama 3.3 70B):

• Average: 100-150 tokens/second
• Time to first token: 400-800ms

Together is the slowest of the three, but compensates with model breadth and features.

Pricing

Code Example

# Together is OpenAI-compatible
from openai import OpenAI

client = OpenAI(
    api_key="your-together-api-key",
    base_url="https://api.together.xyz/v1"
)

response = client.chat.completions.create(
    model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
    messages=[{"role": "user", "content": "Explain attention mechanisms."}],
    max_tokens=2048,
    temperature=0.7
)

print(response.choices[0].message.content)

Fine-Tuning on Together

# Together's fine-tuning API
import together

client = together.Together(api_key="your-together-api-key")

# Upload training data
response = client.files.upload(
    file="training_data.jsonl",
    purpose="fine-tune"
)
file_id = response.id

# Start fine-tuning job
job = client.fine_tuning.create(
    training_file=file_id,
    model="meta-llama/Llama-3.1-8B-Instruct",
    n_epochs=3,
    batch_size=4,
    learning_rate=1e-5
)

print(f"Fine-tune job: {job.id}")

Best Use Cases for Together

• When you need a model not available on Groq or Fireworks
• Fine-tuning custom models at scale
• Research and experimentation across many model families
• DeepSeek R1 inference at competitive pricing
• Teams wanting the most model options

Reliability and SLA Comparison

All three providers have had notable outages. For production applications, use an LLM gateway with fallback chains across multiple providers.

Cost Comparison for a Real Workload

Assume: 10,000 requests/day, 500 input tokens + 300 output tokens each = 8M tokens/day total.

All three open-source inference providers are 3-5x cheaper than GPT-4o for comparable (though not equal) quality.

The Verdict

Groq: Best for latency. Use it when speed is the primary constraint. Accept the limited model selection.

Fireworks: Best balance of speed, reliability, and features. Use it for production applications that need consistent performance and function calling.

Together: Best model selection and fine-tuning. Use it when you need a specific model or want to fine-tune.

Practical recommendation: Use Groq as your primary provider for latency-sensitive paths with Fireworks as fallback. Use Together when you need models Groq and Fireworks don't carry.

Methodology

All benchmarks, pricing, and performance figures cited in this article are sourced from publicly available data: provider pricing pages (verified 2026-04-16), LMSYS Chatbot Arena ELO leaderboard, MTEB retrieval benchmark, and independent API tests. Costs are listed as per-million-token input/output unless noted. Rankings reflect the publication date and change as models update.