Groq, Together AI, and Fireworks AI are the three most important fast inference platforms for open source LLMs. Groq is the fastest raw inference platform available (700-800 tokens/second on Llama models using custom LPU chips), Together AI has the broadest model selection and good pricing for fine-tuning, and Fireworks specializes in production-ready inference with strong function calling support. All three are significantly cheaper than running GPT-4o or Claude Sonnet, making them worth evaluating for any high-volume workload where an open source model is good enough.
Why These Platforms Exist
Running large language models on GPU clusters is operationally complex and capital-intensive. Groq, Together AI, and Fireworks AI have all built specialized inference infrastructure that lets developers access open source models through a simple API, without managing any infrastructure.
The value proposition is threefold:
- Cost: Open source model inference is 5-50x cheaper than GPT-4o or Claude Sonnet for equivalent output quality on many tasks.
- Speed: Dedicated inference infrastructure often delivers faster responses than OpenAI and Anthropic, especially under load.
- Model flexibility: Access to hundreds of open source models, including domain-specific fine-tunes that outperform general models on specific tasks.
Groq: The Speed Leader
Groq uses custom Language Processing Units (LPUs) designed specifically for transformer inference. The result is inference speeds of 700-900 tokens per second on Llama 3 8B, and 200-400 tokens per second on Llama 3 70B. For comparison, GPT-4o typically delivers 50-100 tokens per second.
This speed advantage is meaningful for latency-sensitive applications: real-time voice interfaces, interactive coding tools, applications where users experience the model typing character by character.
Groq pricing (May 2026):
- Llama 3.1 8B: $0.05/1M input, $0.08/1M output
- Llama 3.1 70B: $0.59/1M input, $0.79/1M output
- Llama 3.1 405B: $2.00/1M input, $2.00/1M output
- Gemma 2 9B: $0.20/1M input, $0.20/1M output
Free tier: Groq offers a free tier with rate limits — useful for prototyping. Check console.groq.com for current limits.
Groq limitations: Model selection is narrower than Together AI or Fireworks. Groq focuses on a curated set of top-performing open source models rather than offering everything available. No fine-tuned model hosting. Context window on some models is more limited than native offerings.
Best for: Any application where low latency matters, voice interfaces, interactive tools, development and prototyping.
Together AI: The Model Catalog
Together AI offers one of the largest selections of open source models available through a single API. As of May 2026, they host 100+ models including fine-tuned variants, code-specific models, and specialized domain models.
Together AI pricing (May 2026):
- Llama 3.1 8B Instruct: $0.18/1M input, $0.18/1M output
- Llama 3.1 70B Instruct: $0.88/1M input, $0.88/1M output
- Llama 3.1 405B: $3.50/1M input, $3.50/1M output
- CodeLlama 34B Instruct: $0.78/1M input, $0.78/1M output
- Mistral 7B Instruct: $0.20/1M input, $0.20/1M output
Together AI strengths:
- Fine-tuning API: you can fine-tune models on their infrastructure and deploy them
- Broad model selection including specialized models for code, math, and specific languages
- Serverless and dedicated deployment options
- Competitive pricing on mid-size models
Best for: Workloads where you need a specific model that other platforms do not offer, teams exploring fine-tuning, applications where the best open source model for your domain is not in Groq's limited catalog.
Fireworks AI: Production-Ready Inference
Fireworks AI positions itself as the production-focused inference platform. It has strong support for function calling, JSON mode, and structured outputs — features that are essential for agentic applications and structured data extraction.
Fireworks AI pricing (May 2026):
- Llama 3.1 8B: $0.20/1M tokens (blended)
- Llama 3.1 70B: $0.90/1M tokens (blended)
- Llama 3.1 405B: $3.00/1M tokens (blended)
- Mixtral 8x7B: $0.50/1M tokens (blended)
Fireworks AI strengths:
- Best-in-class function calling support on open source models
- Compound AI systems (running multiple models in sequence or parallel)
- SLA guarantees available on paid plans
- Low latency optimized for production workloads
Best for: Production applications using agentic patterns, function calling, structured outputs. Teams that need an SLA and have shipped beyond early-stage.
Pricing Comparison Table
| Model | Groq | Together AI | Fireworks AI | OpenAI Equivalent | |-------|------|-------------|--------------|-------------------| | 8B class | $0.05-0.08 | $0.18 | $0.20 | GPT-4o-mini: $0.15-0.60 | | 70B class | $0.59-0.79 | $0.88 | $0.90 | GPT-4o: $2.50-10.00 | | 405B class | $2.00 | $3.50 | $3.00 | GPT-4o: $2.50-10.00 |
A 70B class open source model (Llama 3.1 70B) on any of these platforms is roughly 3-10x cheaper than GPT-4o and comparable to GPT-4o on many tasks. For teams currently spending significant amounts on GPT-4o, evaluating Llama 3.1 70B on Groq or Together AI should be on your cost optimization roadmap.
When to Use Open Source Platforms vs. Direct Providers
Use Groq, Together AI, or Fireworks when:
- You have evaluated an open source model and it performs adequately for your task
- Latency is a primary concern (Groq especially)
- You are running high-volume workloads where 5-10x cost reduction matters
- You need a fine-tuned or specialized model
Stick with OpenAI or Anthropic direct when:
- The latest frontier models (GPT-4o, Claude Sonnet) genuinely outperform available open source options for your task
- You need the absolute latest model capabilities immediately at release
- Compliance requirements mandate tier-1 provider contracts
Keep Reading
- Local LLM vs. API Cost Comparison — When self-hosting beats all three of these platforms.
- Model Routing Guide — Use these platforms as the cheap tier in a routing strategy.
- LLM API Pricing Comparison 2026 — Full pricing comparison across all major providers.
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