Observability for Generative AI
Track costs, usage, and latency for GPT applications with one line of code.
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import {Configuration, OpenAIApi} from "openai";const configuration = new Configuration({apiKey: process.env.OPENAI_API_KEY,// Add a basePath to the ConfigurationbasePath: "https://oai.hconeai.com/v1",baseOptions: {headers: {// Add your Helicone API Key"Helicone-Auth": "Bearer <API_KEY>",},}});const openai = new OpenAIApi(configuration);
“Keeping costs under control was a huge issue 2-3 weeks ago but we are now profitable per user. We leveraged a mix of caching, model-swapping, fine-tuning, and product updates to get here @helicone_ai has been a godsend for LLM cost analytics, especially cost/user”
“My favourite of the new AI apps? @helicone_ai - Observability for @OpenAI is pretty bad. Hard to track bills and specific usage with native tools. I see Helicone as the next @datadoghq”
“I'm now using Helicone and it's a major QoL improvement while deving on LLMs Add one line to your python/JS OpenAI project and get - input/output logging - user-level metrics - caching (soon) Also OSS 👏”
“As an early-stage startup, speed is everything at Trelent. Helicone helps us quickly understand user behaviour when we're iterating with OpenAI, shorten our testing cycles.”
Stay on top of your costs, usage, and latency
Integrate models like GPT-4 with Helicone to track API requests and visualize results.
Tools built for savvy developers
Cache requests to save on latency and money, proactively track errors in your application, handle rate limits and reliability concerns with Helicone.
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