Fireworks AI vs Together AI: which open-weight inference platform wins in 2026?
Fireworks AI wins on low-latency LLM serving and fine-tune-and-serve. Together AI wins on model catalog breadth and aggressive scale pricing. Pick Fireworks for latency-critical, Together for breadth.
At a glance
| Dimension | Fireworks AI | Together AI |
|---|---|---|
| Inference latency | Best in class for LLM servingWIN | Fast — GPU-based |
| Open-weight catalog | Curated | Best in the categoryWIN |
| Fine-tune + serve workflow | First class | First class |
| Custom model deployment | Yes — bring your own | Yes — bring your own |
| Multimodal serving | Strong | Strong |
| Free tier | Limited | DecentWIN |
| Pricing per 1M tokens | Competitive | Aggressive at scaleWIN |
| Best for | Production LLM serving, fine-tuned models | Multi-model serving, scale pricing |
Verdict
Fireworks AI is the right pick for production LLM serving where latency matters and you want a clean fine-tune-and-serve story. Together AI is the right pick for broad open-weight catalog access, custom model deployment, and aggressive pricing at scale. For most teams in 2026 it's a close call — pick by primary workload (low-latency serving vs broad multi-model serving) and by team familiarity.
When to pick which
Pick Fireworks AI
Latency-critical LLM serving, fine-tune + serve, production-grade endpoints.
Pick Together AI
Multi-model serving, broadest open-weight catalog, scale pricing.
FAQ
Fireworks or Together AI in 2026?
Fireworks for latency-critical production; Together for breadth and scale pricing.
Cheapest at scale?
Together AI tends to be cheaper at billion-token scale; Fireworks is competitive at low-medium scale.
Best for fine-tuned models?
Both — Fireworks has slightly faster serving; Together has more model variety to fine-tune.
Last updated: 2026-06-01.