Humanloop

Humanloop was an AI development and evaluation platform designed to help teams build, test, monitor, and improve applications powered by large language models. The company initially focused on making it easier for developers to build LLM applications, later expanding into a broader platform for prompt management, evaluation, observability, and collaborative AI development.

Humanloop opened access to developers in October 2022, at a time when LLM application development was still an emerging field. Its original goal was to address the gap between creating an impressive LLM prototype and operating a reliable AI product in production. The platform provided tools for prompt engineering, model experimentation, evaluation, deployment, and monitoring.

A major part of Humanloop was its evaluation platform. Teams could create evaluation datasets, define evaluation criteria, run automated tests, compare model or prompt versions, and use evaluation results to determine whether an AI application was improving. Humanloop supported both code-based and AI-based evaluators, as well as human evaluation workflows.

Humanloop’s evaluation approach was designed around continuous testing rather than evaluating an AI application only at the end of development. Teams could integrate evaluations into development and CI/CD processes, allowing changes to prompts, models, and AI workflows to be tested before deployment.

The platform also provided prompt management. Teams could create and edit prompts in a collaborative environment, maintain versions, test different models, and deploy tagged prompt versions. This helped separate prompt development from application code and allowed product managers, engineers, and subject-matter experts to collaborate on AI behavior.

Humanloop supported a multi-model environment rather than requiring teams to use a single AI provider. Its documentation listed support for providers including OpenAI, Anthropic, Google, Azure, Cohere, AWS Bedrock, Llama, and Groq. It also allowed teams to use their own models and self-hosted models through the API.

Another important capability was observability. Humanloop could capture logs from AI applications, including inputs, outputs, metadata, feedback, and evaluator judgments. This gave development teams visibility into how their AI applications behaved in production.

The platform’s observability capabilities included online monitoring, tracing, alerting, end-user feedback, and logging. These features were intended to help teams identify problems in production and understand where an AI application was performing well or poorly.

Humanloop also supported human feedback. Its Human Evaluator feature allowed domain experts and product teams to review AI outputs and provide judgments. Human evaluations could be combined with automated code-based and AI-based evaluators to provide a broader picture of application quality.

Humanloop introduced Tools to connect LLMs with APIs and private data sources. This allowed models to access additional resources and perform actions beyond simple text generation. The feature was positioned as an important foundation for building more capable AI agents.

The platform was therefore relevant to AI-agent development as well as conventional LLM applications. Developers could connect models to tools, manage those tools centrally, evaluate their behavior, and monitor application performance.

Humanloop was designed for collaboration between technical and non-technical teams. Engineers could work through APIs and SDKs, while product managers and domain experts could use the graphical interface for prompt engineering and evaluation. This UI-first and code-first approach was one of the platform’s defining characteristics.

The platform provided official Python and TypeScript SDKs, alongside an API that could be accessed through HTTP requests. Developers could use the SDKs to manage prompts, evaluations, logs, and other platform functionality programmatically.

Humanloop’s development environment included a multi-LLM playground where teams could experiment with different models and prompt configurations. This allowed developers to compare model behavior before incorporating a particular configuration into an application.

Versioning and deployment controls were another important part of the platform. Teams could maintain different versions of prompts and track changes, helping them reproduce previous configurations and roll back when necessary.

Humanloop also offered datasets and dataset versioning for evaluation workflows. This made it possible to maintain consistent test cases and measure how changes affected application performance over time.

The platform supported both offline and online evaluation. Offline evaluations could be used during development, while online evaluators could analyze new production data as applications were being used.

Humanloop also provided integration with CI/CD workflows. Teams could incorporate evaluation checks into software development pipelines so that changes could be tested automatically before being deployed.

Security was another focus of the enterprise platform. Humanloop listed SOC 2 Type II and GDPR compliance, with additional enterprise capabilities including SSO, SAML, role-based access controls, VPC deployment, dedicated environments, regional hosting, and HIPAA-related provisions.

Humanloop supported multiple deployment options for enterprise customers, including its standard cloud offering, region-specific deployments, dedicated instances, and self-hosted deployment within a customer’s AWS VPC.

The platform also used a bring-your-own-model-provider approach. Customers supplied their own API keys for providers such as OpenAI or Anthropic and paid those providers separately for model usage. This meant Humanloop was primarily the development, evaluation, and observability layer rather than the provider of the underlying model inference.

Humanloop’s customer base included organizations developing AI-powered products and internal AI systems. Its platform was particularly relevant to teams that needed repeatable evaluation processes, collaborative prompt development, production monitoring, and the ability to work across multiple LLM providers.

The platform’s overall philosophy centered on evals-driven AI development. Instead of treating an AI system as finished after a prompt produces acceptable results, Humanloop encouraged teams to continuously evaluate outputs, collect feedback, compare versions, and improve the system through an iterative development process.

This approach addressed an important challenge with LLM applications: model outputs are probabilistic, and traditional software testing does not always capture whether an AI response is useful, accurate, safe, or aligned with a particular business requirement.

Humanloop therefore provided tools for teams to define what a good output looked like and measure AI performance against those criteria.

Platform Status

Humanloop is no longer an active standalone platform. In August 2025, Humanloop announced that its team was joining Anthropic and that the Humanloop platform would be sunset. Billing stopped on July 30, 2025, and the platform was scheduled to become inaccessible on September 8, 2025.

This means Humanloop should now be treated as a discontinued/deprecated AI development platform, rather than a currently available SaaS product.

Its technology and team became part of Anthropic following the acquisition. Humanloop stated that the company was excited to continue its work within Anthropic and contribute to the development of AI systems.

Pricing

Humanloop’s historical pricing included a free trial and enterprise plans. The pricing page listed a trial with 2 members, 50 evaluation runs, and 10,000 logs per month, while enterprise customers could access features such as SSO/SAML, role-based access controls, enterprise support, and VPC deployment.

However, Humanloop is no longer accepting new paid usage because the platform was sunset in 2025. Its historical commercial pricing should therefore not be treated as current pricing.

Pricing Model

Contact for pricing

Pricing Note

Humanloop historically offered a free trial and custom enterprise pricing. The platform was sunset in September 2025, with billing stopped on July 30, 2025.

Review

Humanloop was a strong platform for teams that needed a structured way to develop and evaluate LLM applications. Its combination of prompt management, evaluation, observability, human feedback, and multi-model support addressed several of the most difficult parts of deploying AI applications reliably.

Its evaluation capabilities were particularly valuable. Teams could combine automated evaluators, human judgments, datasets, and production monitoring instead of relying solely on subjective testing.

The collaborative workspace was another strength. Engineers could work through code and SDKs while product managers and domain experts could participate through the interface. This reduced the separation between technical AI development and business-oriented prompt refinement.

Multi-provider support also made Humanloop flexible. Teams could experiment with OpenAI, Anthropic, Google, Azure, Cohere, AWS Bedrock, and other models without designing their entire development process around one provider.

The platform’s main limitation was that it was primarily designed for professional AI development teams rather than casual users. Getting the greatest value required understanding prompts, evaluation methodology, model behavior, APIs, and production monitoring.

Another consideration was that Humanloop was an additional platform layer rather than the underlying AI model provider. Customers still needed their own model-provider accounts and paid those providers separately for inference.

Most importantly for anyone evaluating the product today, Humanloop is discontinued. Its platform can no longer be used as a current commercial AI development service.