Contact Sales
Platform

Label Studio Enterprise

Train, Benchmark and Evaluate AI

Label Studio sits at the center of your workflow, connecting data, models, and human judgment into a continuous improvement loop.

Evaluation Across the Entire AI Lifecycle

01

Build novel AI solutions on your data

Combine proprietary data with human expertise to create AI systems competitors can’t replicate off-the-shelf.

02

Know before you deploy with model benchmarks

Score outputs across model versions, compare tradeoffs between frontier models, and predict real-world performance on the tasks your users actually care about.

03

Continually improve AI from real-world usage

Most teams collect evaluation traces but never act on them. Label Studio turns edge cases into labeled training data so models keep getting better after launch.

04 · Customize

The most adaptable annotation & evaluation platform on the market.

Every team's label schema, review flow, and evaluation rubric is different. Label Studio gives you advanced tools to create interfaces perfectly aligned to your data and use case.

New

Prompt to create advanced interfaces quickly

The new interface agent in Label Studio Enterprise will quickly build any interface shaped to your data and evaluation criteria.

Dozens of prebuilt templates you can modify and extend

Quickly configure annotation interfaces with pre-built tags for all common data scenarios, or build advanced interfaces in React.

Explore Templates
Plan

One platform, every modality

Agentic Traces
Computer Vision
Document and NLP
Audio and Speech
Time Series
def push_tasks(tasks: list[dict]) -> list[int]:
    """Import tasks into the project; returns the created task IDs."""
    result = ls.projects.import_tasks(id=PROJECT_ID, request=tasks)
    print(f"Imported {len(tasks)} tasks into project {PROJECT_ID}")
    return result.task_ids if hasattr(result, "task_ids") else []
from braintrust import init_project

project = init_project(name="customer-support")
experiment = project.experiments.fetch("v3-eval")
records = experiment.fetch_all()
scored = [r for r in records if r.scores.get("accuracy")]
print(f"Retrieved {len(scored)} scored eval records from Braintrust")
from databricks import sql

with sql.connect(server_hostname=HOST, http_path=PATH, access_token=TOKEN) as conn:
    cur = conn.cursor()
    cur.execute("SELECT id, transcript FROM main.support.tickets LIMIT 1000")
    rows = cur.fetchall()
print(f"Retrieved {len(rows)} support transcripts from Databricks")

Integrates with any data source and ML pipeline

API, Python SDK, and webhooks let you create projects, stream predictions, and trigger training, active learning, and evaluation workflows in real time.

Sync data from any storage and connect any model to power AI-assisted labeling, benchmarking, and continuous model evaluation.

LABEL STUDIO ENTERPRISE

A COMPREHENSIVE PLATFORM

Make the highest use of your unique expertise and novel datasets as you train, benchmark, and evaluate AI in one common environment.

Label Studio UI showing audio annotation interface