Alternatives
Products that do what DriftWatch does
Catch ML model drift before it silently kill your prediction
- 1DA
Hey HN! I’ve written a bunch of WebSocket servers over the years to do simple things like state synchronization, WebRTC signaling, and notifying a client when a backend job was run. I realized that if I had a simple way to create a private, temporary, mini-redis that the client could talk to directly, it would save a lot of time. So we created DriftDB. In addition to the open source server that you can run yourself, we also provide https://jamsocket.live where you can use an instance we host on Cloudflare’s edge (~13ms round trip latency from my home in NY). You may have seen my…
2023 · driftdb.com
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- 7UO
A couple of months ago, we left our jobs to build UpTrain AI, an open-source machine learning observability and refinement tool which helps users understand the performance of their models in production and improves them over time by identifying problematic data-points for retraining. Data drift, Distribution shifts, Model degradation, Edge cases - we have personally faced these problems in our previous organizations and have built a lot of tooling to solve them. We are building UpTrain so that others don’t need to build them and can solely focus on improving their ML models while we…
2023 · github.com
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- 11AS
This little project came about because I kept running into the same problem: cleanly differentiating sensor data before doing analysis. There are a ton of ways to solve this problem, I've always personally been a fan of using kalman filters for the job as its easy to get the double whammy of resampling/upsampling to a fixed consistent rate and also smoothing/outlier rejection. I wrote a little numpy only bayesian filtering/smoothing library recently (https://github.com/hugohadfield/bayesfilter/) so this felt like a fun and very useful first thing to…
2024 · github.com
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2021 · github.com
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Trace LLM requests + costs with OpenTelemetry monitoring
Oct 2025
- 18YD
If you've built any web-based app in the last 15 years, you probably used something like Datadog, New Relic, Sentry, etc. to monitor and trace your app, right? Why should it be different when the app you're building happens to be using LLMs? So today we're open-sourcing OpenLLMetry-JS. It's an open protocol and SDK, based on OpenTelemetry, that provides traces and metrics for LLM JS/TS applications and can be connected to any of the 15+ tools that already support OpenTelemetry. Here's the repo: https://github.com/traceloop/openllmetry-js A few months ago we launched…
2024 · github.com
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- 20UD
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
2024
- 21TD
Hey HN, I'm Marcel from Tusk. We’re launching Tusk Drift, an open source tool that generates a full API test suite by recording and replaying live traffic. How it works: 1. Records traces from live traffic (what gets captured) 2. Replays traces as API tests with mocked responses (how replay works) 3. Detects deviations between actual vs. expected output (what you get) Unlike traditional mocking libraries, which require you to manually emulate how dependencies behave, Tusk Drift automatically records what these dependencies respond with based on actual user behavior and maintains recordings…
Nov 2025 · github.com
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