Isoquant

Teams building on LLMs today often rely on general-purpose frontier models or default inference providers, which can mean higher per-token costs, slower time-to-first-token, and lower throughput than necessary for a given workload. The page benchmarks several existing inference providers (Together, CoreWeave, Baseten, Fireworks, Z.ai) showing meaningfully slower latency and lower throughput than Isoquant's own API. For companies with narrow, repeatable tasks like intent classification or policy decisions, using large general models can mean paying for capability the task does not need, at latency the workflow cannot afford.

Isoquant offers an API for running open models like GLM-5.3-Flash with published per-token pricing, automatic prompt caching, and no top-up fees, built to be a drop-in replacement for existing OpenAI-style integrations by just changing the base URL and key. Under the hood it describes architecture-aware model optimization, GPU kernel and memory engineering, speculative decoding, and workload-aware serving with KV caching and load balancing to cut latency and raise throughput. For companies, Isoquant runs a continuous pipeline: connect a workload with its tasks and success criteria, evaluate and improve using evals, configs, and trained specialist models, then deploy the best-fit model. Case studies describe this producing smaller fine-tuned models (e.g., an 8B banking intent specialist, a Qwen3.6-35B-A3B model for order resolution) that match or beat larger frontier models on accuracy while cutting inference cost substantially.