Traceway

OpenTelemetry-native observability for LLM applications with evals and GPU monitoring

OpenLIT is an open-source AI engineering platform that provides OpenTelemetry-native observability for LLM applications. It combines distributed tracing, evaluation, prompt management, a secrets vault, and GPU telemetry in a single self-hostable stack. With 50+ integrations across LLM providers and frameworks, it lets teams monitor AI applications using their existing observability backends like Grafana, Datadog, or Jaeger.

All-in-one open-source observability — logs, metrics, traces, RUM

OpenObserve is an open-source observability platform that unifies logs, metrics, traces, and real user monitoring in a single binary. It claims 140x lower storage costs than Elasticsearch through columnar storage and compression, with native OpenTelemetry support, a built-in query UI, dashboards, and alerts. Designed for AI and cloud-native workloads at petabyte scale. Over 15,000 GitHub stars.

Observability platform purpose-built for Python and Pydantic AI apps

Pydantic Logfire is an observability platform built by the Pydantic team specifically for Python AI applications. It provides structured logging, distributed tracing, and metrics with native understanding of Pydantic models, FastAPI, and AI framework data types. Auto-instruments OpenAI, Anthropic, LangChain, and other LLM providers. Built on OpenTelemetry for vendor-neutral data export. Offers a managed cloud dashboard with a generous free tier for development and small-scale production use.

Open-source GPU control plane for scalable AI model serving

Open-source GPU cluster manager that configures vLLM, SGLang, TensorRT-LLM or custom engines, serves models through compatible APIs, and provisions SSH-accessible GPU instances across on-premises, Kubernetes and cloud environments.

Disaggregated KV cache storage and transfer for LLM serving

Open-source infrastructure for disaggregated LLM serving that pools KV caches across prefill and decode workers, with high-performance transfer, distributed storage and integrations for vLLM and SGLang.

Build custom AI inference servers in pure Python

Open-source, FastAPI-based serving engine from Lightning AI for building custom inference APIs — models, agents, RAG, and pipelines — with built-in batching, streaming, and multi-GPU autoscaling.

Private, self-hosted research notebooks with flexible AI models, source chat, and podcasts

Open Notebook is an MIT-licensed, self-hosted alternative to NotebookLM for collecting sources, chatting over research, generating reusable transformations, and producing multi-speaker podcasts. Its Docker stack keeps notebook data under the user's control while supporting 18-plus model providers, including local Ollama and LM Studio workflows.

Hugging Face's open-source inference server for embeddings, rerankers, and classifiers

Text Embeddings Inference is Hugging Face's Apache-2.0 server for high-throughput embedding, reranking, and sequence-classification models. TEI packages token-based dynamic batching, optimized Transformers kernels, Safetensors loading, OpenAI-compatible embedding endpoints, Prometheus metrics, and configurable OpenTelemetry tracing in deployable CPU and GPU images.

Open-source toolkit for quantizing, deploying, and serving LLMs and vision-language models

LMDeploy is an Apache-2.0 toolkit for self-hosting LLM and vision-language model inference with TurboMind and PyTorch engines. It combines continuous batching, blocked KV cache, tensor parallelism, AWQ and KV-cache quantization with OpenAI-compatible APIs, multi-GPU distribution, offline pipelines, and production metrics.