Humanloop
Data labeling software
- Features
- Ease of use
- Ease of management
- Quality of support
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What is Humanloop
Humanloop is a platform for building and improving LLM-powered applications with human feedback workflows. It supports collecting and managing labeled examples (for prompts, model outputs, and evaluations), running experiments, and monitoring application behavior over time. Typical users include ML engineers and product teams who need a feedback loop to iterate on prompts, fine-tuning datasets, and evaluation sets. Compared with dataset-first labeling tools, it is oriented around LLM application development and evaluation rather than high-volume computer-vision annotation.
LLM feedback loop tooling
Humanloop centers labeling around LLM prompts, responses, and human preference signals. This fits teams that need to curate instruction data, rank outputs, and capture qualitative feedback for iterative improvement. The workflow aligns with common LLM iteration cycles (prompt changes, dataset updates, and evaluation reruns). It is less dependent on image/video annotation primitives than many labeling-focused platforms.
Experimentation and evaluation support
The product includes mechanisms to compare versions of prompts or models using consistent test sets and human review. This helps teams track whether changes improve quality and reduce regressions. Evaluation artifacts can be reused as labeled datasets for future iterations. The approach is suited to text-centric and conversational use cases.
Developer-oriented integration options
Humanloop is designed to integrate into application development workflows where model calls and feedback capture happen in the product. This supports collecting labels from internal reviewers or end users as part of normal usage. It can reduce the friction of moving data between separate labeling and experimentation systems. The emphasis is on operationalizing feedback rather than managing large annotation workforces.
Not optimized for CV labeling
Humanloop’s core workflows focus on LLM outputs and text-based evaluation rather than image, video, or sensor annotation. Teams needing bounding boxes, segmentation, or frame-level tooling may find the feature set insufficient. For those use cases, additional specialized annotation software is typically required. This can increase toolchain complexity for multimodal projects.
Limited workforce/managed labeling
The platform is primarily built for in-house feedback and review processes, not for sourcing large-scale external annotators. Organizations that require managed services, complex vendor operations, or large distributed labeling teams may need separate providers. This can affect scalability for high-volume labeling programs. Governance of third-party annotators may need to be handled outside the product.
Best fit for LLM apps
Humanloop’s value is strongest when the main problem is improving LLM application quality through iterative feedback and evaluation. If a team’s primary need is dataset management for traditional supervised learning pipelines, the platform may feel indirect. Some organizations may prefer a dataset-first system of record for labels. Adoption may require changes to existing ML ops processes.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Try for free | Free | 2 members; 50 eval runs; 10K logs per month (listed on vendor pricing page). |
| Enterprise | Custom pricing (contact sales) | SSO + SAML; Role-based access controls; Hands-on support with SLA; VPC deployment add-on; EU/US hosting and HIPAA options; dedicated account manager (listed on vendor pricing page). |
Seller details
Humanloop Ltd
London, UK (Unsure)
Private
https://humanloop.com
https://x.com/humanloop
https://www.linkedin.com/company/humanloop/
