PyFlare

Open-Source AI/ML Observability

Stop paying 30% of your infrastructure costs on observability. Understand why your models fail, drift, or hallucinate not just that they did.

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ML Teams Are Flying Blind in Production

Current observability solutions are failing ML teams in three critical ways:

Prohibitive Costs

Observability spend frequently reaches 30% of infrastructure costs. Some enterprises spend $50M+ annually on platforms like Datadog.

Vendor Lock-In

Proprietary agents, storage formats, and query languages make migration nearly impossible. Once you're in, you're trapped.

Surface-Level Insights

Most tools tell you that something broke, not why. Teams need root cause analysis, not just red dashboards.

What is PyFlare?

PyFlare is an open-source, OpenTelemetry-native observability platform purpose-built for AI/ML workloads. It provides deep visibility into model behavior across traditional ML, deep learning, and LLM applications.

Deep Introspection

Understand why models make decisions, not just what decisions they made

Multi-Model Coverage

Unified observability for ML, deep learning, and LLM applications

Production-Ready

Built for scale — handle millions of inferences per second

Zero Lock-In

OpenTelemetry native with standard formats and full data portability

Core Capabilities

Intelligent Drift Detection

Go beyond simple statistical metrics with semantic drift analysis. PyFlare tracks embedding drift, feature drift, concept drift, and prediction drift; catching model degradation before it impacts your users.

Hallucination & Failure Detection

Purpose-built evaluators for LLM applications: hallucination scoring with LLM-as-judge, RAG quality analysis, toxicity detection, and prompt injection identification. Know when your models are generating unreliable outputs.

Root Cause Analysis

PyFlare's differentiator: automated analysis that tells you why something broke. Anomaly clustering identifies systematic issues, slice analysis surfaces underperforming data segments, and counterfactual explanations show what would have changed the outcome.

Unified Tracing

End-to-end visibility across complex ML pipelines. Follow agent workflows from input to response, trace RAG pipelines through every stage, and identify latency bottlenecks at each step of inference.

Cost Intelligence

Granular cost tracking with per-request attribution by user, feature, or model version. Detailed token economics, budget alerts, and AI-driven optimization recommendations help you reduce spend without sacrificing quality.

Part of the PyFlame Ecosystem

PyFlare extends OA Quantum Labs' mission to break vendor lock-in across the entire AI development lifecycle.

PyFlame

Train models on Cerebras without CUDA lock-in

PyFlameRT

Optimized inference compilation

FlameVision

Computer vision acceleration

FlameAudio

Audio signal processing

PyFlare

Production observability & debugging

Built on Proven Technology

OpenTelemetry Native

Industry-standard instrumentation prevents vendor lock-in and integrates with your existing observability stack.

ClickHouse Storage

Columnar OLAP database delivers 10-100x better compression and query performance than traditional alternatives.

Kafka Streaming

High-throughput message transport decouples collection from processing for horizontal scaling.

Vector Search

Qdrant integration enables semantic analysis and embedding drift detection at scale.

Works With Your Stack

Deep Learning: PyFlame, PyTorch, TensorFlow, JAX

LLM Frameworks: LangChain, LlamaIndex, DSPy, Haystack

LLM Providers: OpenAI, Anthropic, Google Gemini, Mistral, AWS Bedrock

Traditional ML: scikit-learn, XGBoost, LightGBM, CatBoost

Orchestration: Airflow, Prefect, Dagster, Kubeflow

Support Open Source AI Infrastructure

PyFlare represents thousands of hours of research, engineering, and testing; all released freely to the community. But maintaining and expanding an open-source project of this scope requires ongoing resources.

Your donation helps us:

✓

Maintain and improve the codebase with bug fixes, optimizations, and new features

✓

Expand integrations as new ML frameworks and LLM providers emerge

✓

Create documentation including tutorials, guides, and example projects

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Provide community support through forums, issue tracking, and direct assistance

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Keep PyFlare independent and free from commercial pressures

Every contribution, regardless of size, directly supports the engineers and researchers who make this work possible.

Donate to Support PyFlare

PyFlare is released under the Apache License 2.0 — fully open source with no restrictions on commercial use.

Built by OA Quantum Labs · Breaking vendor lock-in for AI infrastructure