WhyLabs website screenshot

WhyLabs

WhyLabs was an AI observability platform focused on data and model monitoring for both classical ML and LLM workloads. It built and maintained whylogs, an open-source data logging library that produces statistical profiles of tabular and unstructured data, and LangKit, an open-source toolkit for LLM telemetry covering relevance, toxicity, prompt injection signals, and quality metrics. WhyLabs, Inc. has announced it is discontinuing operations and has open-sourced its platform; the whylogs and LangKit projects remain available on GitHub for community use and research.

WhyLabs publishes 3 APIs on the APIs.io network. Tagged areas include AI Observability, ML Monitoring, LLM Monitoring, Open-Source, and whylogs.

25.2/100 emerging ▬ flat Agent 3/100 human only self hosted · Apache-2.0 Full breakdown ↓
scored 2026-09-14 · rubric v0.22.0
AccessFree
3 APIs 6 Features 4 Use Cases
AI ObservabilityML MonitoringLLM MonitoringOpen-SourcewhylogsLangKitDiscontinued

Kin Score

Kin Score Kin Score How this is scored →
scored 2026-09-14 · rubric v0.22.0
Open Source Surface applies to this provider. This product is open source and we read its repository directly, so Open Source Surface carries 10 points of the composite. It is scored from what the repository actually publishes — a security policy, a contribution guide, a release history, a code of conduct — read live from the provider rather than inferred from our own catalog pointers. This facet adds; nothing was taken away to make room for it. An open-source project is not excused from the commercial facets, because exemption would strip it of the points it does earn. If we have the wrong repository, or this product is not open source, say so on your provider repo and we will drop the facet rather than have you publish against it.
Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.
The six quality facets above are damped to 90 points between them, because the conditional facet above carries the other 10. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 90% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.
Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from github.com/api-evangelist/whylabs: open an issue to ask a question, or submit a pull request to add artifacts. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

APIs 3

Individual APIs this provider publishes, each with its own machine-readable definition.

whylogs

whylogs is an open-source data logging library that creates approximate statistical profiles of datasets, enabling drift detection, data quality monitoring, and bias analysis fo...

LangKit

LangKit is an open-source toolkit that extracts telemetry from LLM prompts and responses including relevance, sentiment, toxicity, prompt injection signals, jailbreak similarity...

WhyLabs Observability Platform

WhyLabs Observability is the historical commercial SaaS that ingested whylogs profiles and LangKit telemetry for dashboards, drift alerts, and constraint monitoring. WhyLabs, In...

Pricing Plans 1

Published pricing tiers and plan structures.

Whylabs Plans Pricing

1 plans

PLANS

Rate Limits 1

Documented rate limits and quota policies.

Whylabs Rate Limits

2 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals for API financial operations.

Features 6

Notable capabilities this provider offers.

whylogs Profiling

Privacy-preserving statistical profiles of tabular, text, image, and embedding data.

LangKit LLM Telemetry

Out-of-the-box metrics for relevance, toxicity, prompt injection signals, and refusal patterns.

Drift Detection

Compare profiles over time to detect data and concept drift.

Data Quality Monitoring

Constraint-based checks on schema, ranges, missingness, and distribution properties.

Bias and Fairness Analysis

Profile-driven analysis of model inputs and outputs across protected groups.

Open Source

Core libraries remain available under permissive licenses on GitHub.

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Whylabs Domain Security

TLSv1.3 · HSTS · DNSSEC · DMARC

SECURITY

Use Cases 4

What developers build with this provider.

ML Data Quality

Monitor training and inference datasets for schema drift and quality issues.

LLM Telemetry

Instrument LLM applications with LangKit metrics to track safety and quality over time.

Model Drift Monitoring

Detect distribution shifts in features and predictions for production ML models.

Privacy-Preserving Logging

Share statistical profiles between teams and environments without exposing raw data.

Integrations 6

Pre-built integrations with other platforms and tools.

pandas

Profile pandas DataFrames directly with whylogs.

Spark

Generate whylogs profiles from PySpark and Spark Scala jobs.

Snowflake

Profile Snowflake tables for drift and quality monitoring.

AWS S3

Read and write whylogs profiles to S3 for distributed pipelines.

MLflow

Log whylogs profiles alongside MLflow runs and models.

Hugging Face

Apply LangKit metrics to Hugging Face model outputs.

Resources

Documentation 2

Reference material describing how the API behaves

Build 4

SDKs, sample code, and the tooling you integrate with

Access & Security 1

Authentication, authorization, and security posture

Operate 3

Status, limits, changes, and where to get help

Commercial 1

Pricing, plans, and the legal terms of use

Company 2

The organization behind the API

Source (apis.yml)

apis.yml Raw ↑
aid: whylabs
url: https://raw.githubusercontent.com/api-evangelist/whylabs/refs/heads/main/apis.yml
name: WhyLabs
type: Index
deliveryModel:
  model: self-hosted
  license: Apache-2.0
  open_source: true
  commercial: false
  callable_host: false
  label: Self-hosted open source · you run it yourself
  confidence: high
  source:
  - license
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: free
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  label: Free
  confidence: medium
  source:
  - plans
  generated: '2026-07-22'
  method: derived
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/whylabs.png
tags:
- AI Observability
- ML Monitoring
- LLM Monitoring
- Open-Source
- whylogs
- LangKit
- Discontinued
tags_raw:
- AI Observability
- ML Monitoring
- LLM Monitoring
- Open Source
- whylogs
- LangKit
- Discontinued
description: WhyLabs was an AI observability platform focused on data and model monitoring for both classical ML and LLM workloads.
  It built and maintained whylogs, an open-source data logging library that produces statistical profiles of tabular and unstructured
  data, and LangKit, an open-source toolkit for LLM telemetry covering relevance, toxicity, prompt injection signals, and
  quality metrics. WhyLabs, Inc. has announced it is discontinuing operations and has open-sourced its platform; the whylogs
  and LangKit projects remain available on GitHub for community use and research.
created: '2026-05-23'
modified: '2026-05-23'
specificationVersion: '0.23'
apis:
- aid: whylabs:whylogs
  name: whylogs
  tags:
  - Open-Source
  - Data Logging
  - Profiling
  - Monitoring
  tags_raw:
  - Open Source
  - Data Logging
  - Profiling
  - Monitoring
  humanURL: https://whylogs.readthedocs.io/
  properties:
  - url: https://whylogs.readthedocs.io/
    type: Documentation
  - url: https://github.com/whylabs/whylogs
    type: SourceCode
  - url: https://pypi.org/project/whylogs/
    type: SDKs
  description: whylogs is an open-source data logging library that creates approximate statistical profiles of datasets, enabling
    drift detection, data quality monitoring, and bias analysis for ML pipelines. Supports tabular, text, image, and embedding
    data and produces privacy-preserving profiles that can be shared and compared without exposing raw data.
- aid: whylabs:langkit
  name: LangKit
  tags:
  - Open-Source
  - LLM Monitoring
  - Telemetry
  - Safety
  tags_raw:
  - Open Source
  - LLM Monitoring
  - Telemetry
  - Safety
  humanURL: https://github.com/whylabs/langkit
  properties:
  - url: https://github.com/whylabs/langkit
    type: SourceCode
  - url: https://pypi.org/project/langkit/
    type: SDKs
  description: LangKit is an open-source toolkit that extracts telemetry from LLM prompts and responses including relevance,
    sentiment, toxicity, prompt injection signals, jailbreak similarity, refusal patterns, and quality metrics. Designed to
    plug into whylogs profiles for end-to-end LLM observability.
- aid: whylabs:whylabs-observability
  name: WhyLabs Observability Platform
  tags:
  - Software-as-a-Service
  - Observability
  - Discontinued
  tags_raw:
  - SaaS
  - Observability
  - Discontinued
  humanURL: https://whylabs.ai/
  properties:
  - url: https://whylabs.ai/
    type: Documentation
  description: WhyLabs Observability is the historical commercial SaaS that ingested whylogs profiles and LangKit telemetry
    for dashboards, drift alerts, and constraint monitoring. WhyLabs, Inc. has announced it is discontinuing operations and
    open-sourced the platform; commercial availability of the hosted service should be re-verified directly with the company.
common:
- type: IssueTracker
  url: https://github.com/whylabs/whylogs/issues
- type: Releases
  url: https://github.com/whylabs/whylogs/releases
- type: CodeOfConduct
  url: https://github.com/whylabs/whylogs/blob/mainline/.github/CODE_OF_CONDUCT.md
- type: ContributionGuide
  url: https://github.com/whylabs/whylogs/blob/mainline/.github/CONTRIBUTING.md
- type: License
  name: Apache-2.0
  url: https://github.com/whylabs/whylogs/blob/mainline/LICENSE
- type: DomainSecurity
  url: security/whylabs-domain-security.yml
- type: Website
  url: https://whylabs.ai/
- type: GitHubOrganization
  url: https://github.com/whylabs
- type: GitHubRepository
  url: https://github.com/whylabs/whylogs
- type: GitHubRepository
  url: https://github.com/whylabs/langkit
- type: WhylogsDocumentation
  url: https://whylogs.readthedocs.io/
- type: LinkedIn
  url: https://www.linkedin.com/company/whylabs/
- type: CompanyStatus
  url: https://whylabs.ai/
- type: Features
  data:
  - name: whylogs Profiling
    description: Privacy-preserving statistical profiles of tabular, text, image, and embedding data.
  - name: LangKit LLM Telemetry
    description: Out-of-the-box metrics for relevance, toxicity, prompt injection signals, and refusal patterns.
  - name: Drift Detection
    description: Compare profiles over time to detect data and concept drift.
  - name: Data Quality Monitoring
    description: Constraint-based checks on schema, ranges, missingness, and distribution properties.
  - name: Bias and Fairness Analysis
    description: Profile-driven analysis of model inputs and outputs across protected groups.
  - name: Open Source
    description: Core libraries remain available under permissive licenses on GitHub.
- type: UseCases
  data:
  - name: ML Data Quality
    description: Monitor training and inference datasets for schema drift and quality issues.
  - name: LLM Telemetry
    description: Instrument LLM applications with LangKit metrics to track safety and quality over time.
  - name: Model Drift Monitoring
    description: Detect distribution shifts in features and predictions for production ML models.
  - name: Privacy-Preserving Logging
    description: Share statistical profiles between teams and environments without exposing raw data.
- type: Integrations
  data:
  - name: pandas
    description: Profile pandas DataFrames directly with whylogs.
  - name: Spark
    description: Generate whylogs profiles from PySpark and Spark Scala jobs.
  - name: Snowflake
    description: Profile Snowflake tables for drift and quality monitoring.
  - name: AWS S3
    description: Read and write whylogs profiles to S3 for distributed pipelines.
  - name: MLflow
    description: Log whylogs profiles alongside MLflow runs and models.
  - name: Hugging Face
    description: Apply LangKit metrics to Hugging Face model outputs.
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com

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