Humanloop is a large language model (LLM) development and deployment platform that provides large enterprises with a range of tools to develop, evaluate, and deploy their own LLMs safely and efficiently.
The platform was founded by former engineers from Google, Amazon, Microsoft, and leading UK Universities (University College London & University of Cambridge), and has received significant venture capital funding ($2.6 million Seed Round).
However, due to its relatively small size and limited publicly available review data, it is scored at 8 out of 10.
Humanloop was founded on March 3, 2020, and is based on the experience of its founding members who were all former machine learning engineers at Google, Amazon, Microsoft, and leading UK Universities (University College London & University of Cambridge).
Cloud-based enterprise platform
Focuses on LLM evaluation, prompt engineering, active learning, and human-in-the-loop systems for safe AI deployment
Inferred from product category (MLOps/ML Observability) and descriptions; specific stack not publicly detailed
| Service | Cost | Details | Source |
|---|---|---|---|
| Free | $0 | 2 members, 50 eval runs, 10K logs/month | — |
| Enterprise | Custom quote | SSO + SAML, role-based access controls, hands-on support w/ SLA, VPC deployment add-on, all features | — |
| Startup Program | Free (application required) | For early stage VC backed startups, access to platform tools to help scale | — |
| Feature | Humanloop | Braintrust | Weights & Biases | LangSmith |
|---|---|---|---|---|
| LLM Observability | Yes | Yes | Yes | Yes |
| Prompt Engineering | Yes (collaborative workspace, versioning) | Yes | Partial | Yes |
| Evaluation Suite | Yes (LLM-as-judge, human review) | Yes | Yes | Yes |
| CI/CD Integration | Yes | Yes | Yes | Yes |
| Starting Price | Free tier | $39/mo | $50/user/mo | Free tier |
| Free Tier | Yes (50 eval runs) | Yes (5K traces) | No | Yes |
| Enterprise SSO | Yes | Yes | Yes | Yes |
| API Access | Yes | Yes | Yes | Yes |
| Integration Count | Multi-LLM support | High | High | LangChain focus |
| Support Options | Enterprise SLA | Email/Slack | Priority tiers | Enterprise support |
Human Loop is designed to be used by Product Managers and Engineers using a collaborative user interface first approach along with coding first approaches to evaluate while BrainTrust is designed around a developer centric tracing methodology. HumanLoop has stronger enterprise grade security than BrainTrust (SOC2, HIPAA) and uses custom pricing models versus Brain Trust’s tiered pricing model.
HumanLoop is best suited for cross functional teams while BrainTrust is best for development focused observability.
While W&B has a large market share in the space of traditional ML experiment tracking, HumanLoop is native to LLM and includes feature sets such as prompt management and evaluation suite tools built for production LLMs. As well, W&B has additional configuration to provide LLM specific monitoring compared to HumanLoop.
W&B is best suited for comprehensive ML pipeline tracking while HumanLoop is best for LLM specific observability.
LangSmith excels in providing LangChain ecosystem integrations however does not include the same level of collaborative workspaces and enterprise grade security as HumanLoop. Although both products offer free tiers, HumanLoop allows for independent support from any LLM provider.
LangSmith is best for LangChain users while HumanLoop is best for teams utilizing multiple providers for their LLM.
While Phoenix offers open source LLM tracing, HumanLoop provides a full enterprise platform including evaluation, monitoring, and compliance. Phoenix is a good option for cost sensitive teams however it lacks HumanLoops’ ability for cross-functional team UI collaboration and its security certifications.
Phoenix is best for open source experimentation while HumanLoop is best for production enterprise use cases.
Evaluate Subjective Metrics (Tone, Helpful) Using Customizable LLM Evaluators
Integrate Human Feedback At Key Decision Points to Improve Models & Datasets
Full Version Control Capabilities for All Prompts Outside of Code Base
Embed Evaluations Into Dev Pipelines So That You Can Catch Regressions Early
Average Evaluator Scores Over Time to Spot Performance Trends
Organize & Manage Evaluation Datasets to Turn Production Issues into Test Cases
Mitigate Bias in Model Outputs to Ensure Fairness & Alignment
Flag Harmful Outputs (Hallucination, Toxic Language)
Use data monitoring tools to track whether or not the data being used for training and fine-tuning is current and relevant.
Be aware of your underlying infrastructure and resources so you can identify potential bottlenecks before they happen.
Collect user feedback about how users are interacting with the system in order to make the model easier to use.
Anomalies such as cost spikes and data drift will trigger instant slack and email notifications for immediate response.
Perform a deep dive into issues by displaying all input and output data and metadata for every step in both your RAG pipeline and agent systems.
Create custom thresholds and corresponding actions (such as routing a flagging response to a human reviewer) for your anomaly detection systems
| Organization Type | Primary Use Case | Key Benefit | Example Users |
|---|---|---|---|
| AI Product Teams | LLM Evaluation & Optimization | Evaluate and iterate on model behavior before production deployment | Gusto, Vanta, Duolingo |
| RAG Development | RAG Pipeline Optimization | Simplify evaluation cycles for retrieval-augmented generation systems | Enterprise search and knowledge systems |
| Fine-Tuning Teams | Reward Model Development | Integrate human feedback to refine and improve model behavior | Custom model adaptation |
| Production ML | Real-Time Monitoring & Alerts | Detect harmful outputs, performance degradation, and data drift | Enterprise AI applications |
| Regulated Industries | Compliance & Accountability | Maintain audit trails and prove regulatory compliance | Financial services, healthcare |
| Cross-Functional Teams | Collaborative Development | Align technical and non-technical teams on AI product quality | Product, engineering, data teams |
| Capability | Humanloop | Langfuse | Arize | Vellum |
|---|---|---|---|---|
| LLM Evaluation Framework | ✓ Advanced | ✓ Advanced | ⚠ Basic | ✓ Advanced |
| Human-in-the-Loop Feedback | ✓ Native | ⚠ Limited | ✓ Supported | ✓ Native |
| RAG Pipeline Optimization | ✓ Best-in-class | ✓ Supported | ⚠ Limited | ✓ Strong |
| Real-Time Guardrails | ✓ Yes | ✓ Yes | ✓ Yes | ✓ Yes |
| Bias & Fairness Detection | ✓ Yes | ⚠ Limited | ✓ Advanced | ⚠ Limited |
| CI/CD Integration | ✓ Complete | ✓ Complete | ⚠ Basic | ✓ Complete |
| Prompt Version Control | ✓ Yes | ✓ Yes | ✗ No | ✓ Yes |
| Self-Hosting | ✓ Yes | ✓ Yes | ⚠ Limited | ✗ No |
| SOC 2 Type II | ✓ Yes | ⚠ In Progress | ✓ Yes | ⚠ Limited |
