Fastino is a developer-first AI company building Task-Specific Language Models (TLMs), a new class of compact models engineered for accuracy, speed, and cost-predictability on targeted tasks rather than general-purpose chat. Where large frontier models are powerful but expensive and slow, Fastino's TLMs are optimized for the specific jobs that power production AI applications and agents, things like extraction, classification, summarization, and structured reasoning, delivering results far faster and cheaper.
The company says its TLMs achieve up to 99x faster inference than traditional LLMs, and notably were trained on less than $100,000 of low-end gaming GPUs, a deliberate counterpoint to the prevailing wisdom that capability requires massive compute budgets. By specializing models to tasks, Fastino aims to give developers predictable latency and cost, which matters enormously when agents call models repeatedly in tight loops and need to be both reliable and economical at scale.
Fastino's team was assembled from AI researchers at Google DeepMind, Stanford, Carnegie Mellon, and Apple Intelligence, and the company is based in Palo Alto. Its positioning, fast, accurate, cost-predictable models for specific tasks, speaks directly to a pain point in agentic systems, where general LLM calls can be too slow and too costly to support real-time, high-volume agent workflows.
Fastino raised a $17.5 million seed round led by Khosla Ventures, bringing total funding to $25 million. The round included pre-seed lead Insight Partners as well as Valor Equity Partners and notable angels including former Docker CEO Scott Johnston and Weights & Biases CEO Lukas Biewald.
For developers building agents and AI products that need to run fast and cheap without sacrificing accuracy, Fastino offers task-specialized models as a practical alternative to over-provisioning expensive general-purpose LLMs.