← Back to changelog

February 10, 2026

[Draft] Simplify for Scale

We've rebuilt Langfuse's infrastructure to address performance bottlenecks at scale.

After processing billions of events per month, we analyzed our query patterns and we introduce changes now to be able to scale with our customers.

The new architecture delivers faster charts and APIs through a simplified data model that eliminates expensive joins and reduces query complexity.

Changes

1. Observation-centric data model

The data model now centers on observations as the primary concept.

Context attributes (user_id, session_id, metadata) set at the trace level now propagate automatically to all child spans. This eliminates the need to repeatedly update trace metadata or patch outputs across observations.

Complex agentic workflows often involve multiple important steps within a single trace. The new observation-centric model lets you filter and analyze individual observations, moving beyond the limitation of viewing only trace-level inputs and outputs. Saved table filters let you persist filter presets across users in a project, making it easier for teams to share common views.

2. UI performance improvements

Chart rendering time has been significantly reduced. Queries that previously took seconds or minutes now complete in few seconds or milliseconds. Database operations have been optimized to reduce query complexity.

This applies to trace exploration, filtering, and analytics across large projects.

3. New observations and metrics API endpoints

We redesigned the observations and metrics APIs to test the new data model under the hood. The redesigned APIs offer significant performance improvements:

• Observations API v2: Optimized query execution with flexible filtering
• Metrics API v2: Faster aggregations across large projects

4. Faster LLM-as-a-judge evaluations

LLM-as-a-Judge evaluations on traces require a separate ClickHouse query, causing delays and backlogs at scale.

Observation-level evaluations execute in seconds instead of minutes:

• Eliminate evaluation delays: No database query overhead. Execution time is limited only by LLM API rate limits, not database performance
• Scale without performance impact: Run evaluations on every observation without degradation
• Faster iteration: See evaluation results immediately after ingestion, not minutes later
• Higher concurrency: Process more evaluations per minute

Upgrade existing trace-level evaluators to observation-level for immediate performance gains.

Technical changes

The rebuild is based on three core principles:

• Immutable observations: Observations are written once and never modified, eliminating deduplication operations.
• No joins: Trace-level attributes propagate to observations in the SDK. Queries run on a single table without joins.
• Observation-centric model: Observations are the primary data source for APIs and UI.

Max shared technical details on how we arrived at this architecture to keep product performance ahead of demand at ClickHouse Open House (recording) in Amsterdam.

How to unlock new performance

The new architecture is being rolled out gradually. Here’s how to access the performance improvements:

1. Enable beta UI experience

In the UI, a beta toggle will be available to opt into the new experience. Note that not all UI screens have been migrated to the new data model yet.

2. Upgrade SDKs

Upgrade to the latest major SDK versions to take advantage of the observation-centric data model and explore your data in real time:

• Python SDK: v4 (migration guide)
• JS/TS SDK: v5 (migration guide)

The key change: update_current_trace() / updateActiveTrace() is replaced by propagate_attributes(), which automatically flows attributes like user_id, session_id, metadata, and tags to all child observations.

3. Migrate LLM-as-a-judge evaluations

Upgrade your LLM-as-a-judge evaluations to run at the observation level instead of the trace level for significantly faster execution. Learn more in the LLM-as-a-judge migration guide.

4. Adopt new observations and metrics API endpoints

The Observations API v2 and Metrics API v2 deliver significant performance improvements through the new observation-centric data model:

• Selective field retrieval: Request only the field groups you need instead of full rows
• Cursor-based pagination: Consistent performance regardless of result set size
• Optimized querying: Built on the new immutable events table with no joins required

See the v2 APIs announcement for migration guidance and detailed feature comparison.

Get started