Latest articles
Explore practical routing guides, API performance notes, and product updates from the Fastrouter team.
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Prompt caching can cut repeated context costs by up to 90%. Here is how it works across major providers and why most teams are not using it yet
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We fine-tuned Gemma 3 4B on 3,000 synthetic browser trajectories and benchmarked it against GPT-5.1, Claude 4.5 Sonnet, and six other models.
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5 practical levers engineering teams are using to reduce LLM spend right now — model routing, prompt caching, Flex Processing, and Batch
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How I Cut My LLM Bill 79% in 15 Minutes Without Changing Application Code
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Enterprise AI spend is past the adoption phase. Here is what the first wave of LLM investment is teaching engineering leaders about cost accountability.
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Under the Hood: Building a Hybrid AI Agent with FastRouter BYOK | Fastrouter Blog
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Stop routing every agent task to a frontier model. The Architect-Editor pipeline cuts costs 55% by matching model capability to task complexity.
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Stop guessing at prompt quality. GEPA evolves your system prompts automatically — real production data, multi-metric scoring, full iteration audit.
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Route your own provider credentials and fine-tuned models through FastRouter — unified observability, fallback chains, and governance included.
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Add fine-tuned and custom model endpoints to FastRouter. Route them like any standard model — with full observability, cost tracking, and governance.
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Cut LLM costs on repeated context with Prompt Caching on FastRouter. Automatic for OpenAI, DeepSeek, and Gemini. One field for Anthropic Claude.
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Cut batch processing costs ~50% by appending :flex to your model ID. No code refactors, no migration — just cheaper inference.
