Life & fun · alternatives · 2026
24 alternatives to Midas, a Streaming Anomaly Detector. Now Implemented in Go
Go implementation of MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams - steve0hh/midas
Midas is an anomaly detection tool for streaming data, implemented in Go. It identifies unusual patterns in continuous data streams, making it useful for monitoring systems, networks, or any application that needs to… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Midas, a Streaming Anomaly Detector. Now Implemented in Go launched in 2020; newer entries below may have overtaken it.
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Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies. - Stream-AD/MIDAS
2019 · github.com · its alternatives →
- 2ID
Abstract page for arXiv paper 1911.04464: MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
2020 · arxiv.org · its alternatives →
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Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies. - Stream-AD/MIDAS
2020 · github.com · its alternatives →
- 4
Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies. - Stream-AD/MIDAS
2020 · github.com · its alternatives →
- 5
Edge stream anomaly detection for Ruby. Contribute to ankane/midas-ruby development by creating an account on GitHub.
2020 · github.com · its alternatives →
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MemStream: Memory-Based Streaming Anomaly Detection - Stream-AD/MemStream
2021 · github.com · its alternatives →
- 7
Middleware▲633Observability copilot to resolve production issues instantly
2023 · middleware.io · its alternatives →
- 8
Middleware▲257Open-source DORA metrics for software engineering teams
2024 · github.com · its alternatives →
- 9
The NUS School of Computing is recognised among the top global universities for computer science; and exemplifies academic excellence and innovative research.
2020 · comp.nus.edu.sg · its alternatives →
- 10
Sketch-Based Anomaly Detection in Streaming Graphs - Stream-AD/AnoGraph
2021 · github.com · its alternatives →
- 11
An alternative to stack traces for your Go errors. Contribute to bracesdev/errtrace development by creating an account on GitHub.
2023 · github.com · its alternatives →
- 12
Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: https://docs.ragas.io/en/stable/, docs: https://docs.ragas.io/. Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year. Problems Ragas can solve - How do you choose the best components for your RAG, such as the retriever, reranker, and LLM? - How do you formulate a test dataset…
2024 · github.com · its alternatives →
- 13AR
2013 · getanomalous.com · its alternatives →
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A Python toolkit for rule-based/unsupervised anomaly detection in time series - arundo/adtk
2019 · github.com · its alternatives →
- 16
Cuckoo Filter implementation in Go, better than Bloom Filters (unmaintained) - irfansharif/cfilter
2016 · github.com · its alternatives →
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Best no-code anomaly detection engine for Google Analytics
2022 · baresquare.com · its alternatives →
- 19
Hi everyone! I wanted to share my analytics app with you. This project came from requirements to track certain very frequent events. I found that the cost to do it on a regular analytics product was much more than i was willing to pay. Secondly, I also wanted to use as few resources as possible. So I thought it may be a good idea to create something that may be useful for myself (and hopefully others). I have been able to track a great number of events with this using ~20 MB of storage and memory which is incredible. I have been really impressed by golang as a language and as an ecosystem…
2025 · github.com · its alternatives →
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I've created a powerful tool to help Go developers uncover the hidden giants in their compiled binaries. Go Size Analyzer is like an X-ray machine for your Go executables, revealing: Which dependencies are eating up your binary size Unexpected bloat from standard library or vendor packages Size changes between binary versions with a visual diff Key features that set it apart: Interactive treemap visualizations (check out the demo: https://gsa.zxilly.dev) Slick terminal UI for deep diving into package hierarchies Cross-platform support (works on Linux, macOS, and Windows binaries)…
2024 · github.com · its alternatives →
- 21
A library for object detection and seamless YOLO model integration in Elixir - poeticoding/yolo_elixir
2024 · github.com · its alternatives →
- 22
We built Gonzo to make log analysis faster and friendlier in the terminal. Think of it like k9s for logs — a TUI that can ingest JSON, text, or OpenTelemetry (OTLP) logs, highlight and boil up patterns, and even run AI models locally or via API to summarize logs. We’re still iterating, so ideas and contributions are welcome!
2025 · github.com · its alternatives →
- 23
Probabilistic data structures for processing continuous, unbounded streams. - tylertreat/BoomFilters
2015 · github.com · its alternatives →
- 24
A Python library for detecting patterns and anomalies in massive datasets using the Matrix Profile - target/matrixprofile-ts
2019 · github.com · its alternatives →
Also compare
- Midas, Microcluster-Based Detector of Anomalies in Edge Streams alternatives
- Intrusion Detection in Real-time alternatives
- Midas, a Streaming Anomaly Detector alternatives
- Fast Real-Time Anomaly Detection in Dynamic Graphs alternatives
- Anomaly Detection in Ruby alternatives
- Memory-Based Anomaly Detection in Multi-Aspect Streams alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →