Fireworks AI vs Together AI
Detailed side-by-side comparison to help you choose the right tool
Fireworks AI
🔴DeveloperAI Model Hosting & Inference
Production inference platform for open-weight LLMs, multimodal models, and custom fine-tunes — known for very fast serving (FireAttention/FireOptimizer), reliable function calling, and JSON mode at low per-token prices.
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CustomTogether AI
🔴DeveloperAI Model Hosting & Inference
AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.
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$0.02/1M tokensFeature Comparison
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Fireworks AI - Pros & Cons
Pros
- ✓Reliable function calling, JSON mode, and parallel tool calls across the open-model catalog — table stakes for production agents
- ✓FireFunction-V2 is purpose-built for tool-calling accuracy, materially beating generic Llama tool-use in agentic loops
- ✓Three pricing tiers (serverless / dedicated GPU-hour / Enterprise) cover prototype-to-scale without rehosting
Cons
- ✗Latency is good but typically not as low as Groq's LPU-based inference
- ✗Per-token pricing is competitive but not always the cheapest — DeepSeek's official API or OpenRouter aggregation can undercut on specific models
- ✗Serverless rate limits can surprise high-burst workloads and force an earlier-than-expected jump to dedicated deployments
Together AI - Pros & Cons
Pros
- ✓Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
- ✓One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
- ✓Transparent per-token pricing — easy to model unit economics against closed providers
- ✓InfiniBand-backed GPU Clusters are credible for real training, not just inference
Cons
- ✗Frontier-class reasoning still lags closed models on the hardest benchmarks
- ✗Fastest single-model latency is sometimes beaten by Groq or Cerebras
- ✗Many model variants means model selection itself becomes a project
- ✗Dedicated endpoint cost calculations require attention to GPU type and utilization
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