Celeris provides an OpenAI‑compatible API that runs large language models at unprecedented speed, delivering responses in as little as 24 ms with a 15× performance boost over typical offerings. The service offers drop‑in compatibility—developers can point existing SDKs to api.celeris.ai and retain their code—while charging per token, so faster inference incurs no extra cost. It targets builders needing ultra‑low‑latency AI for real‑time applications.
Funding
Funding not disclosed
Founders
Product
Problem
Developers building real‑time AI applications often face high latency and costly inference when using existing large language model APIs, which can degrade user experience and increase operational expenses.
Solution
Celeris offers an OpenAI‑compatible API that delivers large language model responses in milliseconds, achieving up to 15× faster inference than leading models such as GPT‑5. Its novel inference architecture provides a median response time of 158 ms and streams results without buffering, enabling sub‑30 ms latency for individual tokens. The service can be integrated with just three lines of code, allowing developers to replace existing endpoints without code changes. Pricing is based solely on generated tokens, so faster performance does not incur additional fees. This combination of speed, compatibility, and token‑based billing supports high‑throughput, low‑latency text generation for interactive applications.
Target Audience
Primary customers are AI developers and product teams building chatbots, virtual assistants, or other real‑time language‑driven features that require low latency and high throughput.
Features
- OpenAI‑compatible API endpoint that works with existing SDKs and client libraries
- Millisecond‑scale inference with a p50 latency of 158 ms and streaming responses as fast as 24 ms
- Architecture delivering 1,664 output tokens per second (p50) and 13× speedup versus GPT‑5
- Token‑based pricing model that charges only for generated output, eliminating extra costs for speed
- Simple three‑line integration example for immediate migration from other LLM providers