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Elastic Workflows takes a plain-text prompt and generates YAML you can inspect, version and run against your Elasticsearch data. Now GA, with human-in-the-loop workflows in Slack, parallel execution, and 10 new connectors.
Your Agent Builder agents already log every LLM call as an OTel trace, and that agent tracing data can power token cost dashboards and budget alerts before one runaway conversation quietly wrecks your month.
Automatic calibration at merge time picks vector quantization parameters for each segment by predicting recall from a small sample. Here's how we built it into Elasticsearch's merge path.
Jina Reranker 3.5 beats v3 by 50%+ on case law, closes the gap with models 7x its size on legal, medical, and financial benchmarks, and beats them outright on structured data. It's a drop-in replacement for v3, with no API changes.
ES|QL's CHANGE_POINT command finds structural shifts, variance changes and spikes in any metric in ~1ms, without tuning anything per series.
AutoOps introduces clearer severity, updated page layouts, and simpler issue triage for Elastic Cloud Hosted deployments and Cloud Connect clusters.
All 28 Jina AI models, including rerankers, as ready-to-deploy Docker containers, with zero telemetry and no license server. Drop-in compatible with OpenAI, Cohere, Voyage AI and Elastic Inference Service APIs.
Add custom attributes to OpenTelemetry spans and run six ES|QL queries that reveal your top searches, zero-result rate and slowest queries.
Learn the geometric model that lets Elasticsearch predict recall with R² > 0.98 accuracy and auto-select vector quantization parameters from a small data sample.
Set up NVIDIA hosted models in Elasticsearch with one API key and a model ID. No custom integration code needed.
AI shopping agents that guess at your vocabulary make expensive mistakes. Pre-computed catalog context stops the guessing before the first tool call.
We benchmarked two embedding models on 5,000 real products and found that combining image and text beats either alone by up to 50%. Here's the data and the model that won.
Use AutoOps, the Profile API and ES Rally together to find cluster hotspots, slow queries and index bottlenecks, with real benchmarks showing a 98.9% latency cut and 4x indexing gain.
On the same hardware, Elasticsearch and Qdrant land in the same range at 56 QPS. The io_uring disk scorer and memory claims turned out to be the two things that mattered least.
A hands-on test comparing BBQ and float32 vector indices in Elasticsearch, measuring memory, disk and recall@10 across five languages.
Elasticsearch is becoming a first-class columnar database. Columnar Mode ships in 9.5, storing data once alongside the existing modes and cutting storage footprints while speeding up analytical queries.
How to instrument your search application to use modern Open Telemetry standard to drive insights in to your search and users.
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