If you're looking for a Kolank alternative, there are several options that could be a good fit. One top contender is AIML API, which aggregates more than 100 AI models through a single API. This service is built for high scale and reliability, with serverless inference and a pay-for-use pricing model based on tokens consumed. It's compatible with OpenAI and has straightforward integration and documentation, making it a good option for sophisticated machine learning projects.
Another strong contender is Keywords AI, a unified DevOps platform for building, deploying and monitoring AI applications based on large language models. It provides a single API endpoint for several LLM models, supports high concurrency without lag, and integrates directly with OpenAI APIs. The service includes features like keyboard shortcuts for fast development, a playground for testing and iterating on models, and pre-built dashboards for monitoring performance.
Unify is another strong contender, offering dynamic routing services that optimize LLM applications by sending prompts to the best available endpoint across multiple providers. It includes custom routing based on cost, latency and output speed constraints, and uses real-time benchmarks to select the fastest provider. The service is designed to improve accuracy and flexibility while minimizing resource usage and development time.
For those who want to fine-tune and serve large language models, Predibase is a good option. It lets developers fine-tune open-source LLMs for their own specific tasks and offers a serving foundation with free serverless inference. The service supports a variety of models and uses a pay-as-you-go pricing model, making it a good option for developers who want to squeeze the best performance out of their models.