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AI Tool Comparison
Fireworks AI vs Together AI
A side-by-side breakdown to help you pick the right tool for your workflow.
Review Score assesses product quality; Fit Score is specific to a use case. Limited reviews do not establish overall product quality. How reviews work.
Fireworks AI
Run Llama, Mixtral, and 50+ open-source models at production speed: model-specific token billing with the same SDK you're already using.
Models
paid
Together AI
Run, fine-tune, and scale open-source models: start on cheap shared inference and graduate to dedicated GPUs (including on-demand B200s) as traffic grows.
Developer Tools
freemium
Bottom Line
Catalog updated: August 2026
Fireworks AI (Models) and Together AI (Developer Tools) come from different corners of the market, so this usually comes down to which job you're actually hiring a tool for, not a head-to-head on the same task. Fireworks AI runs on a paid-only plan while Together AI runs on a freemium model, which alone may settle it if budget or a free tier is a hard requirement.
Choose Fireworks AI if…
Best for developers who need fast, cheap inference on open-source models without sacrificing production reliability, and its edge is latency that consistently beats cloud providers, with model- and serving-tier-specific token pricing. A genuinely strong value pick for open-source inference, the free credit is too small to test real production load. Lean toward Together AI instead if a catalog of 200+ open models across major families, all accessible through one OpenAI-compatible interface matters more for your use case.
Choose Together AI if…
Best for developers who want to run, fine-tune, or deploy open-source models via API without managing GPU infrastructure, and its edge is a catalog of 200+ open models across major families, all accessible through one OpenAI-compatible interface. Competitive pricing and real production scale, usage costs are worth modeling before committing to heavy workloads. Lean toward Fireworks AI instead if latency that consistently beats cloud providers, with model- and serving-tier-specific token pricing matters more for your use case.
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| Attribute | Fireworks AI | Together AI |
|---|---|---|
| Category | Models | Developer Tools |
| Pricing | paid | freemium |
| Pricing Detail | GPT OSS 120B Serverless Standard (USD) / $0.15/1M uncached input tokens / $0.015/1M cached-input tokens / $0.60/1M output tokens | Pay-as-you-go from $1.04/M tokens / dedicated GPUs from $6.49/hr |
| TWF Review Score | Not yet reviewed | Not yet reviewed |
Key Features
Fireworks AI
- OpenAI-compatible API for instant drop-in replacement
- 50+ open-source models including Llama, Mixtral, and Gemma
- Compound AI system deployment (multiple models in one call)
- Function calling and JSON mode across all supported models
- Fine-tuning API for custom model specialization
- Sub-100ms time-to-first-token on most models
Together AI
- Inference for 200+ open models
- Fine-tuning and training
- OpenAI-compatible API
- Dedicated endpoints
Pros
Fireworks AI
- •Best-in-class latency for open-source model inference
- •Separate input, cached-input, and output token rates support cost estimation
- •OpenAI-compatible API means zero migration effort
Together AI
- •Broad open-model catalog
- •Scales for production
- •Competitive pricing
Cons
Fireworks AI
- Smaller model selection than OpenRouter
- Fine-tuning has limited base model options vs dedicated platforms
- Free credit is small, production workloads require billing setup
Together AI
- Usage costs add up
- Less consumer-facing
