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New top story on Hacker News: Benchmarks and comparison of LLM AI models and API hosting providers
Benchmarks and comparison of LLM AI models and API hosting providers
30 by Gcam | 7 comments on Hacker News.
Hi HN, ArtificialAnalysis.ai provides objective benchmarks and analysis of LLM AI models and API hosting providers so you can compare which to use in your next (or current) project. The site consolidates different quality benchmarks, pricing information and our own technical benchmarking data. Technical benchmarking (throughput, latency) is conducted through sending API requests every 3 hours. Check out the site at https://ift.tt/AwBXeys , and our twitter at https://twitter.com/ArtificialAnlys Twitter thread with initial insights: https://twitter.com/ArtificialAnlys/status/17472648324397343... All feedback is welcome and happy to discuss methodology, etc.
30 by Gcam | 7 comments on Hacker News.
Hi HN, ArtificialAnalysis.ai provides objective benchmarks and analysis of LLM AI models and API hosting providers so you can compare which to use in your next (or current) project. The site consolidates different quality benchmarks, pricing information and our own technical benchmarking data. Technical benchmarking (throughput, latency) is conducted through sending API requests every 3 hours. Check out the site at https://ift.tt/AwBXeys , and our twitter at https://twitter.com/ArtificialAnlys Twitter thread with initial insights: https://twitter.com/ArtificialAnlys/status/17472648324397343... All feedback is welcome and happy to discuss methodology, etc.
Monday, January 15, 2024
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New top story on Hacker News: Show HN: Quickwit – OSS Alternative to Elasticsearch, Splunk, Datadog
Show HN: Quickwit – OSS Alternative to Elasticsearch, Splunk, Datadog
9 by francoismassot | 6 comments on Hacker News.
Hi folks, Quickwit cofounder here. We started Quickwit 3 years ago with a POC, "Searching the web for under $1000/month" (see HN discussions [0]), with the goal of making a robust OSS alternative to Elasticsearch / Splunk / Datadog. We have reached a significant milestone with our latest release (0.7) [1], as we have witnessed users of the nightly version of Quickwit deploy clusters with hundreds of nodes, ingest hundreds of terabytes of data daily, and enjoy considerable cost savings. To give you a concrete example, one company is ingesting hundreds of terabytes of logs daily and migrating from Elasticsearch to Quickwit. They divided their compute costs by 5x and storage costs by 2x while increasing retention from 3 to 30 days. They also increased their durability, accuracy with exactly-once semantics thanks to the native Kafka support, and elasticity. The 0.7 release also brings better integrations with the Observability ecosystem: improvements of the Elasticsearch-compatible API and better support of OpenTelemetry standards, Grafana, and Jaeger. Of course, we still have a lot of work to be a fully-fledged observability engine, and we would love to get some feedback or suggestions. To give you a glance at our 2024 roadmap, we planned to focus on Kibana/OpenDashboard integration, metrics support, and pipe-based query language. [0] Searching the web for under $1000/month: https://ift.tt/jiag3H9 [1] Release blog post: https://ift.tt/1o468Yd [2] Open Source Repo: https://ift.tt/Upl0Okw [3] Home Page: https://quickwit.io
9 by francoismassot | 6 comments on Hacker News.
Hi folks, Quickwit cofounder here. We started Quickwit 3 years ago with a POC, "Searching the web for under $1000/month" (see HN discussions [0]), with the goal of making a robust OSS alternative to Elasticsearch / Splunk / Datadog. We have reached a significant milestone with our latest release (0.7) [1], as we have witnessed users of the nightly version of Quickwit deploy clusters with hundreds of nodes, ingest hundreds of terabytes of data daily, and enjoy considerable cost savings. To give you a concrete example, one company is ingesting hundreds of terabytes of logs daily and migrating from Elasticsearch to Quickwit. They divided their compute costs by 5x and storage costs by 2x while increasing retention from 3 to 30 days. They also increased their durability, accuracy with exactly-once semantics thanks to the native Kafka support, and elasticity. The 0.7 release also brings better integrations with the Observability ecosystem: improvements of the Elasticsearch-compatible API and better support of OpenTelemetry standards, Grafana, and Jaeger. Of course, we still have a lot of work to be a fully-fledged observability engine, and we would love to get some feedback or suggestions. To give you a glance at our 2024 roadmap, we planned to focus on Kibana/OpenDashboard integration, metrics support, and pipe-based query language. [0] Searching the web for under $1000/month: https://ift.tt/jiag3H9 [1] Release blog post: https://ift.tt/1o468Yd [2] Open Source Repo: https://ift.tt/Upl0Okw [3] Home Page: https://quickwit.io
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