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.
Groq vs. Together AI vs. Fireworks AI: Fast LLM Inference Compared
Three fast, cheap inference platforms for open source LLMs. Groq is the fastest, Together AI has the broadest model selection, Fireworks specializes in production-grade function calling.
Mahmudul Haque Qudrati
CEO & ML Engineer
May 17, 2026
5 min read
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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.
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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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Frequently Asked Questions
What is Groq vs. Together AI vs. Fireworks AI: Fast LLM Inference Compared?
This comparison evaluates three leading platforms for running open-source LLMs at high speed and low cost. Groq uses custom LPU hardware for the fastest raw inference (700+ tokens/sec). Together AI offers the largest model catalog (100+ models) and fine-tuning capabilities. Fireworks AI focuses on production-ready features like function calling and structured outputs. All three provide API access to models like Llama 3, Mistral, and Mixtral at a fraction of the cost of proprietary APIs.
How does Groq vs. Together AI vs. Fireworks AI: Fast LLM Inference Compared work?
Each platform has optimized its infrastructure for LLM inference. Groq's custom LPU chips process transformer models with deterministic low latency, achieving 700-900 tokens/sec on 8B models. Together AI uses GPU clusters with optimized batching and offers serverless endpoints plus dedicated deployments. Fireworks AI employs a proprietary inference engine with advanced features like function calling and JSON mode, and supports compound AI systems that chain multiple models. All three expose REST APIs compatible with OpenAI's SDK.
What are the best practices for Groq vs. Together AI vs. Fireworks AI: Fast LLM Inference Compared?
Best practices include: 1) Benchmark your specific task on each platform using a representative dataset, as performance varies by model and workload. 2) Start with Groq for latency-sensitive apps like real-time chat or voice. 3) Use Together AI when you need a niche fine-tuned model or plan to fine-tune your own. 4) Choose Fireworks for production apps requiring reliable function calling or structured outputs. 5) Implement a model routing strategy to send simple queries to cheaper 8B models and complex ones to 70B+ models. 6) Monitor costs per token and set up alerts.
How much does Groq vs. Together AI vs. Fireworks AI: Fast LLM Inference Compared cost?
Pricing varies by model. For Llama 3.1 8B, Groq charges $0.05-0.08 per million tokens, Together AI $0.18, Fireworks $0.20. For 70B models, Groq is $0.59-0.79, Together AI $0.88, Fireworks $0.90. For 405B, Groq is $2.00, Together AI $3.50, Fireworks $3.00. All are significantly cheaper than GPT-4o ($2.50-10.00 per million tokens). Groq offers a free tier with rate limits. Together AI and Fireworks have usage-based pricing with volume discounts available.
Is Groq vs. Together AI vs. Fireworks AI: Fast LLM Inference Compared worth it in 2026?
Yes, for teams that have validated an open-source model meets their quality bar. The cost savings (5-50x vs GPT-4o) are substantial for high-volume workloads. Groq is unmatched for latency-critical apps. Together AI is ideal for model diversity and fine-tuning. Fireworks excels in production agentic systems. The main drawback is model capability: frontier models like GPT-4o still outperform open-source on complex reasoning, creative writing, and nuanced instruction following. Evaluate on your specific tasks before committing.
Which platform has the best function calling support?
Fireworks AI has the best function calling support among the three. It offers native JSON mode, structured outputs, and reliable function calling on open-source models like Llama 3 and Mixtral. Together AI also supports function calling but with less consistency. Groq does not currently offer function calling as a first-class feature. For agentic workflows requiring tool use, Fireworks is the recommended choice.
Can I fine-tune models on these platforms?
Only Together AI offers a built-in fine-tuning API that allows you to fine-tune models on their infrastructure and deploy them as custom endpoints. Groq and Fireworks do not currently provide fine-tuning services. If fine-tuning is a requirement, Together AI is the platform to use. Alternatively, you can fine-tune locally or on other services and then deploy the model on Fireworks or Together AI.
How do these platforms compare to running models locally?
Running models locally (e.g., with Ollama or vLLM) eliminates API costs and latency but requires upfront GPU investment and operational expertise. For low-volume or experimental use, local inference can be cheaper. For production at scale, these platforms often win on total cost of ownership due to economies of scale and no idle GPU costs. Groq's LPUs are not available for local deployment. A hybrid approach (local for dev, API for prod) is common.
#groq#together-ai#fireworks-ai#llm-inference-platforms
Mahmudul Haque Qudrati
CEO & ML Engineer
Visionary leader with extensive experience in machine learning and software development. Drives strategic innovation and business growth.
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