Humanloop Review (2026): Features, Pricing & Verdict
Humanloop was acqui-hired by Anthropic (announced August 2025) and its standalone platform ceased operations on 8 September 2025. The team joined Anthropic to strengthen evaluation and safety infrastructure for the Claude model family, and the closed-source product is no longer available to new or existing users. This review covers what Humanloop was, why it mattered, and — most importantly — the actively-supported alternatives to use instead.
Humanloop was, for several years, one of the most important and genuinely pioneering tools in the entire LLM-operations space — described by its own founders as “the first development platform for LLM applications,” a startup that helped shape industry standards for how teams manage and evaluate AI. Founded in London in 2020 by Raza Habib, Jordan Burgess and Peter Hayes, and backed by Y Combinator (W20) with investors including Index Ventures, Humanloop built an enterprise-grade platform around a simple but influential thesis: that evaluation should be a first-class engineering discipline for AI, the same way unit testing is for software. Its product combined prompt management, an evaluation framework and production observability into one unified workflow, and its defining innovation was collaboration — making prompt iteration and evaluation accessible to non-technical domain experts and product managers, working alongside engineers, through a polished UI-first-and-code-first interface. That “evals-driven development plus collaborative development” philosophy resonated with serious teams, and Humanloop won enterprise customers including Gusto, Vanta, Duolingo and Dixa. Then, in August 2025, Anthropic announced it had acqui-hired the company, and the Humanloop platform shut down on 8 September 2025. The acquisition — characterised by Sifted as a talent-focused acqui-hire — brought Humanloop’s evaluation and safety expertise into Anthropic to help build the infrastructure behind Claude’s ongoing development. For anyone researching Humanloop today, the headline is therefore simple and decisive: however good the product was — and it was very good — it is no longer available, it was closed-source so nothing remains to self-host, and new adoption is impossible. This review documents its genuine strengths for the historical record, but its practical purpose is to point you toward the actively-supported evaluation and prompt-management platforms that now carry its torch.
- Status
- Discontinued — platform ceased operations 8 September 2025 following Anthropic acqui-hire
- What it was
- Enterprise LLM evaluation, prompt-management and observability platform — evals-driven, collaborative (UI-first + code-first), for PMs, domain experts and engineers
- Why it mattered
- One of the first LLM-app development platforms; pioneered evals-as-a-discipline and non-technical prompt collaboration
- Acquirer
- Anthropic (announced Aug 2025) — talent-focused acqui-hire to strengthen Claude evaluation & safety infrastructure
- Use instead
- Comet (Opik), Arize AI (Phoenix), Evidently AI, Weights & Biases (Weave) — all actively supported
What Was Humanloop?
Humanloop was an enterprise LLM evaluation platform — one that bundled prompt management, model evaluation and production observability into a single, collaborative workflow for teams building AI features with large language models. Its core product thesis, and its lasting contribution to the field, was the idea that AI evaluation deserves the same rigour software engineering applies to testing: “you wouldn’t write code without writing unit tests, and you shouldn’t deploy AI models without evaluations,” as one customer put it. Around that principle Humanloop built a platform designed to work both UI-first (for product managers and subject-matter experts) and code-first (for engineers), so that non-technical domain experts could collaborate directly with data scientists and developers on prompt engineering and evaluation — a genuinely influential idea at a time when most tooling was code-only and engineer-only. Founded in London in 2020 by Raza Habib, Jordan Burgess and Peter Hayes, and part of Y Combinator’s W20 batch, Humanloop grew into an enterprise-focused, closed-source SaaS with customers including Gusto, Vanta, Duolingo and Dixa, and it positioned itself explicitly as “the LLM evals platform for enterprises.” Its two headline best practices — evals-driven development (putting evaluation at the heart of the product lifecycle, refining features through continuous feedback and iteration) and collaborative development (letting non-technical experts and PMs work seamlessly with engineers) — captured what made it distinctive. In the context of this site’s Machine Learning & MLOps category, Humanloop sat squarely in the LLM-native evaluation-and-observability cluster alongside tools like LangSmith, Langfuse and Comet’s Opik. What sets this review apart from those is that Humanloop is no longer a live option: Anthropic’s August 2025 acqui-hire and the platform’s 8 September 2025 shutdown mean the sections that follow describe a product that was, not one you can adopt. They’re worth reading to understand the lineage of today’s tools — and to choose the right successor.
What It Offered (For the Record)
Prompt management and collaborative iteration
Prompt management was Humanloop’s primary product surface, and it was built around collaboration and version control. The platform provided a polished prompt editor with model configuration, parameter tuning and output-format settings, letting teams create, test and deploy prompts either in code or through a user-friendly interface, regardless of which proprietary or open-source model they used. Its standout characteristic was accessibility to non-engineers: product managers and domain experts could experiment with which prompt produced the most accurate outputs for a given use case, view a full history of changes, evaluate how variants performed and roll back with ease when needed — all without touching code, removing the long-standing bottleneck of developer-only prompt tuning. For engineers, Humanloop maintained coding best practices by integrating with existing workflows such as Git and CI/CD pipelines, and it centralised prompt history so teams could always answer the operational questions that plague production LLM apps: which prompt version is running, what changed between the version that worked and the one that degraded quality, and how to A/B test variants systematically. Prompts were deeply integrated with the platform’s evaluation and observability capabilities, so iteration, testing and monitoring formed a single feedback loop rather than three disconnected tools. This tight editing-to-evaluation loop — less context-switching, more collaboration — was widely regarded as Humanloop’s strongest everyday feature. Its honest limitation, noted by reviewers, was that its version control was linear rather than git-style: it lacked branching, merge workflows and formal approval gates, which more recent prompt-management tools have since made a focus.
The evaluation framework
Evaluation was Humanloop’s defining differentiator and the capability most responsible for its reputation. Built for “evals-driven development,” it gave teams dedicated tools to systematically test AI agents and LLM outputs, and to validate model behaviour before shipping to production. Teams could define evaluation criteria — quality rubrics, expected output patterns and human rating workflows — and run systematic comparisons between prompt versions, model families or parameter configurations against those criteria, often with built-in statistical testing to remove guesswork from the choice between variants. Automatic evaluation sets ensured consistency across product lines, and the framework supported both automated metrics and structured human feedback, giving enterprises the detailed interaction logging, advanced evaluation metrics and audit trails that compliance-sensitive organisations require. This is where the platform’s philosophy showed most clearly: rather than treating evaluation as an afterthought, Humanloop made it the organising principle of AI development, so that no new model deployment decision was made without first evaluating candidates through the platform. Customers credited it with shipping AI features roughly three times faster and, in at least one case, doubling revenue on AI product lines by letting legal experts, data scientists, PMs and engineers collaborate on evaluation at scale. It’s precisely this evaluation-and-safety expertise — the discipline of measuring AI output quality with the rigour of software testing — that made Humanloop an attractive acqui-hire for Anthropic, whose mission centres on reliable, safe AI. The framework’s limitation, relative to some specialists, was that its metric depth was considered more basic than dedicated evaluation-first tools, and its scope stayed prompt-and-output-centric rather than extending into deep agent-step analysis.
Observability, deployment and where it stopped
Humanloop’s third pillar was observability and monitoring: it tracked AI performance continuously in production, logging LLM inputs, outputs and user feedback in structured form for quality analysis, with an emphasis on reliability and safety tracking so teams could identify issues before they reached end users. Combined with deployment and collaboration features that integrated into existing development workflows and enabled cross-functional teamwork between product managers, engineers and domain experts, this rounded out a platform that credibly spanned the LLM development lifecycle from prompt authoring through evaluation to production monitoring. It integrated with the major model providers and clouds — OpenAI, Anthropic, Cohere, Google Cloud, AWS Bedrock and Azure — and offered enterprise necessities on its top tier, including private-cloud and on-premises deployment, SSO/SAML and SOC-2 Type II compliance. But it’s worth being precise about where Humanloop’s observability stopped, because it shapes which of today’s tools is the right replacement. Humanloop focused on the prompt layer — editing, versioning, testing and production logging — and did not extend into full-application observability: span-level tracing, deep agent debugging, drift detection and quality-aware alerting across an entire pipeline were outside its scope, and teams with complex, multi-agent workflows typically paired Humanloop with a broader observability platform. It was also, distinctively, closed-source and proprietary — unlike the open-source alternatives that dominate this cluster — which meant that when the company was acquired and the service shut down, nothing was left behind to self-host or fork. That combination (prompt-centric scope plus closed-source shutdown) is the crux of the guidance below: with Humanloop gone, teams need a replacement, and the best choice depends on whether they need prompt-centric collaboration, deep observability, or both.
Scored Categories
These scores reflect what Humanloop offered while operational, heavily weighted by the decisive reality that the platform is now discontinued and cannot be adopted.
Pricing
| Tier (historical) | Price | Notes |
|---|---|---|
| Free trial | $0 | Reported as 2 members, 50 evaluation runs, 10K logs/month |
| Paid tiers | ~$99–$1,000/mo | Reported entry from ~$99/mo; Starter ~$100/mo (1,000 datapoints), Team ~$1,000/mo (10,000 datapoints) — figures varied by source |
| Enterprise | Custom | Unlimited projects, private cloud/on-prem, SSO/SAML, SOC-2 Type II, dedicated support |
| Current status | Unavailable | Platform shut down 8 September 2025 — no plans are purchasable; new sign-ups impossible |
Strengths (When Operational)
- Pioneering — among the first LLM-application development platforms
- Best-in-class collaboration: PMs and domain experts iterate alongside engineers
- Strong, evals-driven evaluation framework with statistical comparison
- Polished, tight prompt-editing-to-evaluation loop (UI-first + code-first)
- Version control with history and easy rollback
- Production observability with structured logging and safety focus
- Git/CI-CD integration; multi-provider (OpenAI, Anthropic, Bedrock, Azure…)
- Enterprise-grade: private cloud/on-prem, SSO/SAML, SOC-2 Type II
- Trusted by Gusto, Vanta, Duolingo, Dixa
Weaknesses / Why Not to Adopt
- Discontinued — platform shut down 8 September 2025
- Closed-source, so nothing remains to self-host or fork
- New sign-ups impossible; not a viable choice for any new project
- Linear prompt versioning — no git-style branching, merge or approvals
- Observability narrower than full platforms (no span-level tracing/agent debugging/drift)
- Evaluation metric depth more basic than eval-first specialists
- Some historical complaints of API reliability, timeouts and rate limits
- No fine-tuning workflow support
Verdict: 5.4 / 10 — A Genuinely Pioneering Platform, But No Longer Available
Humanloop earns a 5.4 — a score that reflects a difficult tension. On product merit alone, when it was operational, Humanloop was excellent: a genuinely pioneering LLM-operations platform with a best-in-class approach to collaborative, evals-driven development, a polished prompt-editing experience, a strong evaluation framework and real enterprise credentials, trusted by companies like Gusto, Vanta and Duolingo. It helped define the discipline of AI evaluation and shaped standards the whole industry now takes for granted, and that historical influence is real. But a review must serve the reader making a decision today, and by that measure the verdict is unambiguous: Humanloop is discontinued. Anthropic acqui-hired the company (announced August 2025) and the platform ceased operations on 8 September 2025, taking its team’s evaluation and safety expertise into the development of Claude. Because Humanloop was closed-source and proprietary, its shutdown left nothing behind — no open-source core to self-host, no community fork — so unlike some sunset tools it offers no residual path forward. New adoption is impossible, and existing users will have had to migrate. The low score is therefore not a criticism of the product’s quality but an honest signal of its current viability: you cannot build on a platform that no longer exists. The clean takeaway: admire Humanloop for what it pioneered, but do not attempt to use it. For the collaborative prompt-management, evaluation and observability capabilities it offered, turn to the actively-supported alternatives below — several of them open-source, and several of which now go deeper on the full-application observability and git-style workflows where Humanloop was thinner.
Frequently Asked Questions
What happened to Humanloop — is it still available?
No, Humanloop is no longer available. In August 2025, Anthropic — the AI-safety company behind the Claude model family — announced that it had acquired Humanloop, and the standalone Humanloop platform ceased operations on 8 September 2025. The transaction has been widely described as a talent-focused acqui-hire: Anthropic brought Humanloop’s founding team and their evaluation-and-safety expertise in-house specifically to strengthen the infrastructure used to evaluate and safely develop Claude, rather than to keep the commercial product running. Humanloop’s founders framed it as the beginning of a new chapter, reflecting with pride that they had built “the first development platform for LLM applications” and helped shape industry standards for managing and evaluating AI before joining Anthropic. For anyone who relied on Humanloop, the consequences are concrete and final: the service is switched off, no new accounts can be created, and — because Humanloop was a closed-source, proprietary platform rather than an open-source project — there is no code base to self-host, fork or continue independently. That last point distinguishes Humanloop’s shutdown from some other discontinued tools in this category: a few sunset products open-sourced their code or shipped a data-export utility to ease migration, but a closed-source platform simply goes dark. Existing customers will have needed to export their data and migrate to another platform around the shutdown date. If you’re researching Humanloop now, treat any older tutorial, pricing page or “sign up” link as historical: the practical answer to “can I use Humanloop?” is no, and the right next step is to choose one of the actively-supported alternatives covered below.
What should I use instead of Humanloop?
The best replacement depends on which part of Humanloop mattered most to you, because the category it helped create now offers several strong, actively-supported options — many of them open-source, which is an advantage Humanloop never had. If you want a broad, unified platform that combines experiment tracking with LLM observability and evaluation, Comet is an excellent choice: its open-source Opik project is one of the fastest-growing LLM-evaluation tools, with deep tracing, LLM-as-a-judge metrics, prompt management and CI testing. If your priority is standards-based observability and evaluation with a genuinely free, self-hostable core, Arize AI’s Phoenix is a leading option built on OpenTelemetry, covering tracing and evaluation with no vendor lock-in — and it goes deeper on the full-application, span-level observability that Humanloop deliberately stayed out of. If you want the most open, lightweight framework spanning both classical ML and LLM evaluation, Evidently AI is fully open-source under Apache 2.0, with 100-plus built-in metrics and a very low-friction adoption path. And if you’re already invested in experiment tracking and want an integrated LLM layer, Weights & Biases offers Weave for LLM observability and evaluation alongside its established ML tooling. Two other names worth knowing in this specific niche are LangSmith (LangChain’s framework-agnostic evaluation, tracing and prompt-management platform) and Langfuse (a popular open-source alternative with prompt management, tracing and evaluation) — both are strong successors to Humanloop’s prompt-and-eval focus. The rough decision guide: choose Comet or Weights & Biases for breadth across ML and LLM; Arize Phoenix or Langfuse for open, self-hostable observability; Evidently for the most open cross-ML/LLM evaluation framework; and a prompt-centric platform if collaborative, non-technical prompt iteration (Humanloop’s signature strength) is your single most important requirement. All of these are actively developed and available today, which is the decisive advantage over Humanloop.
Why did Anthropic acquire Humanloop?
Anthropic’s acquisition of Humanloop is best understood as a strategic acqui-hire aimed at securing evaluation and safety expertise, which sits at the heart of Anthropic’s mission to develop AI responsibly. Humanloop’s entire product thesis was that AI evaluation should be treated as a rigorous engineering discipline — defining test cases and expected outputs, running LLM pipelines against test suites, comparing quality metrics across versions, and catching regressions when models change — and it had spent years building tooling and, crucially, expertise around exactly that problem for enterprise customers. For a company like Anthropic, whose work on the Claude model family depends heavily on being able to evaluate model behaviour rigorously and monitor for safety and reliability, that expertise is directly relevant and valuable. Commentators (including Sifted) characterised the deal as focused on talent rather than on integrating Humanloop’s commercial technology, which is consistent with the outcome: the standalone platform was shut down on 8 September 2025 rather than being kept running or folded in as a product, and the team moved to Anthropic to help build internal evaluation and safety infrastructure. The acquisition also fits a broader pattern seen across the AI industry in this period, in which larger AI labs absorbed specialist evaluation, observability and infrastructure startups to strengthen their in-house capabilities — comparable in shape to other acqui-hires of MLOps and observability companies by major labs and platform vendors. For users, the “why” ultimately matters less than the “what now”: the expertise that made Humanloop valuable is now inside Anthropic and no longer available as a product, so the practical response is to adopt one of the actively-supported evaluation and observability platforms that remain on the market. The episode is also a useful reminder for teams choosing tooling in a fast-moving space: favouring open-source or well-capitalised, independent options can reduce the disruption when a promising startup is acquired and sunset.