An all-in-one platform for building, evaluating, and deploying AI apps powered by large language models.
Klu.ai is an LLM application platform built for AI engineers and product teams. It combines a no-code builder with pro-code SDKs (Python, React) so you can design prompts, connect data sources for retrieval-augmented generation (RAG), A/B test models from OpenAI, Anthropic, Google, and others, then deploy to production. Built-in analytics, evaluation workflows, and collaborative prompt engineering help teams iterate fast and ship reliable AI features without stitching together multiple tools.
Klu.ai is an LLM application platform that helps AI engineers and product teams design, deploy, and optimize apps powered by large language models. It provides tools for prompt engineering, model evaluation, retrieval-augmented generation (RAG), and production deployment in a single workspace.
Klu.ai supports models from OpenAI (GPT-4, GPT-4 Turbo), Anthropic (Claude), Google Vertex AI, AWS Bedrock, Together AI (Llama, Mistral), and self-hosted models. You connect your own API keys, so billing stays with your model provider and your data remains private.
Yes. A free Starter plan is available for experimentation and learning. It gives access to basic prompt workflows, shared evaluations, and community support.
Paid plans start with the Team tier at around $99/month per seat. Enterprise plans offer custom pricing, private deployments, and additional governance features.
Not necessarily. Klu.ai offers a no-code visual builder for designing and deploying AI features without writing code. For deeper customization, Python and React SDKs are available. Both approaches support production-grade deployments.
Klu.ai integrates with Slack, Microsoft Teams, Google Drive, Notion, GitHub, Salesforce, Zendesk, Intercom, Asana, Airtable, Confluence, and databases like PostgreSQL, MySQL, Snowflake, and Redis. It also supports file uploads including PDF, DOCX, CSV, HTML, Markdown, MP3, and MP4.
Klu.ai handles the infrastructure work: embedding pipelines, model routing, evaluation frameworks, and deployment tooling. This lets your team focus on prompt design and user experience rather than building and maintaining custom LLMOps infrastructure. Teams report cutting evaluation time in half compared to stitching together separate tools.
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