Savings per merged PR
Overall AI spend saved
Tokens/second available
Set up in minutes with our CLI
Auto-select the optimal model
Set budgets and measure savings
Admins see every user's spend, limit, and override. Team members see their own and can request an increase before reaching their limit. Set the budget in Settings or through the REST API.
Engineers maintain their tools and flow state with Nexus. Set up in minutes. Create more token headroom with auto, your model autopilot, or pin your own model choices.
Nexus auto scores every request and takes the most cost-efficient path between open and closed models when available. For open models only, developers can set the provider to Fireworks.
“Frontier models took us to product-market fit; now we're driving signal-to-noise further — more real bugs, fewer false positives. At our scale that's a training problem, and Fireworks makes it easy.”
"Fireworks has been a fantastic partner in building AI dev tools at Sourcegraph. Their fast, reliable model inference lets us focus on fine-tuning, AI-powered code search, and deep code context, making Cody the best AI coding assistant. They are responsive and ship at an amazing pace."
"Tokenmaxxing had a good run. But the teams that win with AI from here will be the ones getting more done for less, not the ones spending the most…After optimizing our harness for open weight models, we secretly swapped one of our most used internal agents from Opus 4.8 to GLM-5.2, and no one at the company noticed. We are now seeing cost savings of up to 72%."
“Frontier models took us to product-market fit; now we're driving signal-to-noise further — more real bugs, fewer false positives. At our scale that's a training problem, and Fireworks makes it easy.”
"Fireworks has been a fantastic partner in building AI dev tools at Sourcegraph. Their fast, reliable model inference lets us focus on fine-tuning, AI-powered code search, and deep code context, making Cody the best AI coding assistant. They are responsive and ship at an amazing pace."
"Tokenmaxxing had a good run. But the teams that win with AI from here will be the ones getting more done for less, not the ones spending the most…After optimizing our harness for open weight models, we secretly swapped one of our most used internal agents from Opus 4.8 to GLM-5.2, and no one at the company noticed. We are now seeing cost savings of up to 72%."
“Frontier models took us to product-market fit; now we're driving signal-to-noise further — more real bugs, fewer false positives. At our scale that's a training problem, and Fireworks makes it easy.”
Fireworks has built a fully disaggregated inference engine optimized at every layer, from custom kernels to memory management to adaptive caching. The platform now serves 40T+ tokens daily on a global scale with zero data retention, US-only hosting options, and SOC 2, ISO 27001, and ISO 42001 certifications, plus HIPAA compliance. We maintain commercial agreements for leading frontier open models and offer zero-day access with optimized performance.
While it's true you can configure a router in a weekend, a badly built ladder performs worse than no routing at all. Research by Arize proved naive escalation across ten models performed worse than every single model tested on its own. The judgment is the stack. For example, Fireworks holds a 90%+ cache hit rate on routine coding traffic, and cached input runs at half price. Nexus auto factors hit rate into every orchestration decision.
Have a gateway? Keep it. Nexus has a documented LiteLLM Proxy integration. Use it for policy, fan-out, and fallbacks, and let Nexus auto own the cost-versus-quality call on tasks inside it.
