WhyLabs
Open-source AI observability tools for self-hosted monitoring
Solid open-source tools for privacy-preserving logging and LLM monitoring, but only if you're willing to self-host and self-maintain. No support, no dashboards, no guarantees—good for research, risky for production.
- Researchers exploring open-source AI observability
- Teams needing privacy-preserving logging for sensitive data
- Developers building custom LLM monitoring solutions
- Organizations capable of self-hosting open-source tools
- Teams requiring a supported, production-ready observability platform
- Enterprises needing SLAs and managed cloud service
- Users who rely on dashboards and alerting out of the box
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Skip WhyLabs if you need a supported, production-ready observability platform with SLAs, managed hosting, or pre-built dashboards.
Self-hosting requires you to cover infrastructure costs (compute, storage, networking) that a managed service would include.
WhyLabs is free (open source) but requires self-hosting. Cheaper than SaaS alternatives like Arize AI or Evidently AI if you have DevOps resources; more expensive in hidden labor if you don't.
In short
WhyLabs — Open-source AI observability tools for self-hosted monitoring. Best for Researchers exploring open-source AI observability, Teams needing privacy-preserving logging for sensitive data, Developers building custom LLM monitoring solutions. Free to use.
What's new in WhyLabs
Checked 9 days agoAcross the latest 1 update: 1 news mention.
Viability Score
How likely is WhyLabs to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Key Features
- Privacy-preserving data logging via whylogs
- LLM monitoring and security via langkit
- Data drift detection from statistical profiles
- Prompt injection detection
- LLM output safety evaluation
- Open-source self-hosted platform
- No raw data storage
- Community-driven best practices
- Open standard for data logging
- Complete platform code open-sourced
About WhyLabs
WhyLabs, once an AI observability company, has shut down and open-sourced its entire platform. The core surviving projects are whylogs, a privacy-preserving data logging library, and langkit, a toolkit for monitoring and securing LLMs. These tools let teams log model inputs and outputs, detect data drift, flag prompt injections, and evaluate output safety—all without storing raw data. With no managed service, paid tiers, or support, users self-host and rely on community maintenance. WhyLabs is technically solid for research and privacy-sensitive environments but lacks production guarantees compared to actively supported alternatives like Arize AI or Evidently AI.
Behind the Verdict
WhyLabs' open-source tools are technically sound for privacy-preserving logging and LLM monitoring. whylogs provides a great way to log data and detect drift without storing raw data, and langkit offers useful guardrails for prompt injection and output safety. However, after the company's shutdown, there is no vendor support, no hosted service, and no active development from the original team. If you're a researcher or a team with strong DevOps capabilities, these can be a solid foundation. But for production systems where reliability and uptime matter, the lack of SLAs and community maintenance is a real risk. Compared to Arize AI or Evidently AI, which offer active support and managed cloud services, WhyLabs is only for those who can afford to go it alone.
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Real-world workflow fit
Concrete scenarios for the personas WhyLabs actually fits — and what changes day-one when you adopt it.
You want to log model inputs/outputs for reproducibility without storing raw sensitive data.
Outcome: Install whylogs, profile your data, and detect drift across model versions—all while preserving privacy.
You need to detect prompt injections and evaluate output safety for a chatbot.
Outcome: Integrate langkit, monitor inputs/outputs in real time, and flag malicious prompts or toxic responses.
Use Cases
- Log ML model inputs and outputs without storing raw data using whylogs
- Detect prompt injections in LLM applications with langkit
- Monitor model drift across data segments using statistical profiles
- Evaluate LLM outputs for toxicity, accuracy, and safety
- Build a custom privacy-preserving monitoring pipeline
- Create alerts for data distribution changes over time
Limitations
- WhyLabs has discontinued operations as a company; the platform is now fully open-source.
- Self-hosting is required for all features, including dashboards and alerting.
- Documentation and updates rely on community maintenance.
- Deployment and management require DevOps skills.
as of 2026-06-28
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published WhyLabs tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free (self-hosted open source)
$0
Where the pricing makes sense
The company stage and team size where WhyLabs's pricing actually pencils out — and where peers do it cheaper.
WhyLabs is free (open source) but requires self-hosting. Cheaper than SaaS alternatives like Arize AI or Evidently AI if you have DevOps resources; more expensive in hidden labor if you don't.
Setup time & first value
How long it actually takes to get something useful out of WhyLabs — broken out by persona, not the marketing-page minute.
For a developer experienced with Python and DevOps, setting up whylogs and langkit locally can take a few hours. Deploying a self-hosted monitoring stack with dashboards may take days to weeks depending on infrastructure.
Switching to or from WhyLabs
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From no observability: install whylogs via pip and start logging profiles immediately.
- ↗To Arize AI: export whylogs profiles and import into Arize's SDK for managed observability.
- ↗To Evidently AI: use their open-source library with similar profiling concepts.
Resources & Guides
Official links
Tools that pair well with WhyLabs
Common stack mates teams adopt alongside WhyLabs, with the specific reason each pairing earns its keep.
Alternatives to WhyLabs
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