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Online Learning for Cost-Efficient LLM Routing

Kedar Thakkar <https://www.linkedin.com/in/kedar-thakkar-22b4a4119>· · 0 reactions · 0 comments · 2 views
Online Learning for Cost-Efficient LLM Routing

How our LLM gateway uses Thompson sampling over lognormal latency posteriors to pick the fastest, most reliable model route per request

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Hacker News (AI / LLM) files mainly under ai. We currently carry 2,495 of its stories.

Original article
Ramp Builders · Kedar Thakkar <https://www.linkedin.com/in/kedar-thakkar-22b4a4119>
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Record

Original publisherRamp Builders
Canonical URLhttps://builders.ramp.com/post/thompson-sampling-model-routing
Publication timeMon, 27 Jul 2026 19:35:50 +0000
Retrieval time2026-07-27T19:53:58.939Z
Last seen2026-07-27T19:53:58.939Z
Headline sourcePublisher (no WeSearch rewrite)
Excerpt sourcepublisher description
Excerpt methodPublisher-supplied description / RSS summary field.
SummaryNone yet
Summary source textdescription
Citation coverageNo WeSearch summary has been generated for this story yet.
Cluster1sdUn52-I0VI · 1 stories
Cluster logicGrouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison.
Ranking reasonStory pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking.
Publisher visitYes — open original
Substitutes article?No — link-out required for full text

Rights status (four layers)

Publisher-declared
No publisher-confirmed rights record for this source yet.
Machine-readable
No source-specific machine-readable restriction detected beyond the public feed.
WeSearch interpretation
WeSearch declared handling (basis: Derived from the published RSS/Atom feed). This is WeSearch policy, not a legal grant on the publisher's behalf.
Unknown
Retrieval and training permissions are not asserted unless the publisher confirms them.

WeSearch handling by dimension

Indexing May the item be indexed (stored, ranked, made findable)? Allowed
Snippet May a short excerpt of the publisher's text be shown? Allowed
AI summary May WeSearch generate its own short summary of the article? Limited
Retrieval / RAG May the content be exposed for third-party retrieval-augmented generation? Not asserted
Model training May the content be used to train AI models? Not asserted
Commercial reuse May the content be reused commercially? Not permitted

Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.

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