AI Observability

End-to-end observability for your AI-powered applications.

Accelerating business transformation with AI Observability

Get answers about the behavior, performance, and cost of AI models and services and end-to-end operational view for your AI applications.

Estimate and optimize costs

Gain visibility into every layer of your application's AI stack, so you can optimize end-to-end customer experience and tie AI cost to business value and sustainability.
Improve service quality

Investigate prompt engineering possibilities and create better designed retrieval augmented generation (RAG) pipelines, so you can reduce hallucination and detect model drift.
Ensure service reliability

Run AI models at scale, observe resource consumption, and detect emerging degradation in system performance issues, so they can be remediated before leading to expensive outages.

Seamlessly integrate with cloud services and custom models such as Amazon Elastic Inference, Google Tensor Processing Unit, and NVIDIA GPU.
Monitor infrastructure data, including temperature, memory utilization, and process usage to ultimately support carbon-reduction initiatives.

Connect and monitor cloud services, custom and foundational models, including large language models (LLMs) such as OpenAI GPT3/4, Amazon Translate, Amazon Textract, Azure Computer Vision, and Azure Custom Vision Prediction.
Get visibility into service-level performance metrics like token consumption, latency, availability, response time, and error count for production models.

Seamlessly integrate with your semantic caches and vector databases like Milvus, Weaviate, Chroma, and Qdrant.
Understand the effectiveness of your retrieval-augmented generation (RAG) architecture from both retrieval and generation aspects, and detect model drift in embedding computations with the help of semanic cache hit rates.

Integrate with orchestration frameworks like LangChain to simplify tracing distributed requests.
Get detailed workflow analysis, resource allocation insights, and end-to-end execution insights from prompt to response.
Improve your GenAI application by designing better RAG pipelines. Reduce response times and hallucinations with detailed workflow analysis, resource allocation insights, and end-to-end execution insights into each step of your AI agent's task.