How we run Gemini at scale across billions of posts
Modash utilizes large language models (LLMs) to process billions of social media posts efficiently. The company has developed production pipelines that enhance data quality by understanding content rather than relying on traditional pattern-matching methods. This approach helps reduce false positives and improves the overall value of the data provided to customers.
- ▪Modash's dataset grows by millions of posts daily, necessitating the use of LLMs for data processing.
- ▪The company employs a multi-cloud setup to manage operational complexity and costs associated with LLM inference.
- ▪LLMs are used to extract structured meaning from messy, multilingual social content, improving data quality.
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Modash |
| Canonical URL | https://www.modash.io/engineering/how-we-run-gemini-at-scale-across-billions-of-posts |
| Publication time | Sat, 30 May 2026 08:31:41 +0000 |
| Retrieval time | 2026-05-30T08:42:09.002Z |
| Last seen | 2026-05-30T08:42:09.002Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | vePLX65oiTud |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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.
Opening excerpt (first ~120 words) tap to expand
Using LLMs with billions of inputs in a multi-cloud setupAt Modash we sit on top of a creator-discovery dataset that grows by millions of posts every day. A growing slice of that pipeline now runs through LLMs.This massive volume of inference adds up on our cloud bills and our operational complexity. In this article you will learn how we actually run an LLM against billions of inputs without going broke.Why We Use LLMsIs the AI hype worth it? Do LLMs have any real use beyond being a 24/7 chatbot? We think so, and over the last year we’ve shipped several production pipelines where LLMs are visibly improving the data we deliver to our customers.Several of those pipelines exist to extract structured meaning from messy, multilingual, multimodal social content.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Modash.
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