Isoquant
Isoquant provides an optimized inference API for GLM-5.3-Flash, engineered for low-latency, high-throughput LLM workloads in coding, reasoning, and agent workflows. The service achieves a P50 time-to-first-token of 452 ms and output throughput of 158.9 tokens per second, with automatic prompt caching and no top-up fees. Integration requires only changing the API key and base URL.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Raised to date
$500KRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Founder details are not available yet.
Product
Problem
Developers and enterprises running LLM-powered coding, reasoning, and agent workflows face high inference costs and slow response times. Generic API providers often lack model-specific optimizations, leading to latency bottlenecks and throughput limitations that degrade user experience and increase operational expenses.
Solution
Isoquant provides an optimized inference API for GLM-5.3-Flash, engineered specifically for low-latency, high-throughput performance. The service leverages architecture-aware optimization, GPU kernel and memory engineering, speculative decoding, and workload-aware serving with KV cache and load balancing. It delivers a P50 time-to-first-token of 452 ms and output throughput of 158.9 tokens per second, outperforming competitors like Together, CoreWeave, and Fireworks. Automatic prompt caching reduces costs for repeated inputs, and the API requires no top-up fees, simplifying billing. Integration is straightforward: keep existing code and simply update the base URL and API key. Isoquant also offers configuration optimization services that have demonstrated dramatic quality improvements on agentic workflows, such as raising task success from 15.8% to 98.7%.
Target Audience
Primary customers are developers and enterprises building coding assistants, reasoning systems, and autonomous agent workflows that require fast, cost-efficient LLM inference. The case study demonstrates particular value for teams running high-volume, tool-using agent workloads that need consistent execution quality.
Features
- Pricing at $0.09 per million input tokens, $0.03 per million cached input tokens, and $0.35 per million output tokens
- P50 time-to-first-token of 452 ms and P50 output throughput of 158.9 tokens per second, benchmarked against leading providers
- Automatic prompt caching with no top-up fees or minimum commitments
- Architecture-aware optimization tuned per model, including mixed precision execution
- GPU performance engineering with kernel and memory optimization for higher compute efficiency
- Speculative decoding and decode optimization to accelerate token generation
- Workload-aware orchestration with KV cache management and load balancing for long context and concurrent traffic
- Drop-in integration requiring only a new API key and base URL change