# Appen vs Rerun Appen records 665,840 monthly visits with +0.2% month-over-month growth and Contact for Pricing pricing. Rerun records 54,018 monthly visits with -23.7% growth and Free pricing. | Metric | [Appen](https://openaitools.app/tool/appen-com) | [Rerun](https://openaitools.app/tool/rerun) | | :--- | :--- | :--- | | Website URL | https://appen.com | https://rerun.io | | Categories | AI Models, Large Language Models (LLMs), AI Developer Tools | AI Developer Tools, Log Management, AI For Data Analytics, AI Monitor | | Pricing Model | Contact for Pricing | Free | | Monthly Visits | 665,840 | 54,018 | | MoM Growth Rate | +0.2% | -23.7% | | Primary Market | United States | United States | | Direct Visits | 238,314 | 24,880 | | Review Score | 4.4 | 4.6 | | Saved Count | 2 | 1 | | Supported Platforms | Website | Website | ## Appen Appen is one of the oldest pure-play AI data companies, tracing its roots to 1996 when it began building human-labeled datasets for speech recognition and NLP. Over three decades it supported successive generations of model development — search relevance in the 2000s, machine translation, deep-learning vision annotation, Transformer-era semantics, GPT-3 scale corpora, RLHF for conversational AI, and now agentic and multimodal foundation models. Today Appen positions itself around six specialised capability areas: Frontier Alignment (chain-of-thought reasoning traces, SME RLHF, supervised fine-tuning demonstrations, adversarial red teaming), Agentic AI (golden trajectories, RL environment design, failure-mode taxonomies, SWE-driven evaluation), Multimodal and Speech AI (multilingual ASR and conversational corpora, expressive TTS annotation, VLM training data, video action labelling), Physical AI (LiDAR point-cloud annotation, multi-camera sensor fusion, robotics demonstration trajectories, world-model rollouts), Model Integrity (hallucination benchmarking, bias detection, regulatory audits and continuous monitoring), and Research Services (context engineering, calibrated LLM-as-a-judge, synthetic data and custom benchmarking). Delivery runs through Appen's AI Data Platform (ADAP), which blends automation with human oversight, and the company cites SOC 2 and ISO 27001 certification, 500+ global locales, and a client roster that includes major cloud, search, retail and aerospace organisations. Appen does not publish self-serve pricing; engagements start with a scoping conversation and are quoted per project, so cost depends on data volume, language coverage, modality and annotation complexity. **Pricing & Plans:** Enterprise/contact-for-pricing. Appen does not publish self-serve plans; projects are scoped and quoted individually. Appen also sells off-the-shelf (OTS) datasets and crowd/contractor workforce programs with separate terms. - **Frontier alignment data:** Chain-of-thought reasoning traces, SME-level RLHF, supervised fine-tuning demonstrations and adversarial red teaming for the most capable models. - **Agentic AI datasets:** Golden trajectories, RL environment design, failure-mode taxonomies and software-engineering-driven deep evaluation for autonomous agents. - **Multimodal and speech corpora:** Multilingual ASR and conversational speech data, expressive TTS annotation, vision-language model training data, video action labelling and voice-agent evaluation. - **Physical AI annotation:** LiDAR point-cloud labeling, multi-camera sensor fusion, robot demonstration trajectories, world-model rollouts and embodied interaction logs. - **Model integrity and evaluation:** Hallucination benchmarking, bias detection, regulatory audits and continuous monitoring, plus independent model evaluation. - **AI Data Platform (ADAP):** A production platform that combines automation with human oversight, with SOC 2 and ISO 27001 certification and 500+ locales covered. **Strengths:** Three decades of annotation methodology, tooling and workforce management experience; Very broad coverage: frontier alignment, agentic, speech, multimodal, physical AI and evaluation in one vendor; 500+ locales and multilingual expertise that most in-house teams cannot match; Enterprise-grade security posture (SOC 2, ISO 27001) and publicly listed governance **Limitations:** No transparent self-serve pricing; every engagement requires a sales/scoping cycle; Enterprise-oriented contracts can be heavy for small teams or indie developers; Quality depends on workforce management, so results vary by project and locale; Overlaps with many cheaper specialist annotation vendors, making vendor selection non-trivial [Profile (Markdown)](https://openaitools.app/tool/appen-com.md) ## Rerun Rerun is an open-source SDK and visualizer built for multi-rate, multimodal data, and it markets itself as the data layer for physical AI. Engineers use it to log sensor streams, computer-vision outputs, 3D transforms, and time-series metrics from the first recording through to large-scale review. Because it is a flexible framework rather than a locked platform, teams can extend the viewer with their own views and tools at each stage of the pipeline. The SDK ships with Python, Rust, and C++ APIs, so it fits into existing robotics and perception stacks without a rewrite. Data is stored as column-chunks in rrd files, which supports full dataframe or SQL queries over any recording, down into the columns, time ranges, and values inside it rather than only metadata. Beyond the free tooling, Rerun offers Rerun Hub, a production backend that catalogs recordings, indexes byte ranges, and turns object stores into a queryable, streamable foundation. Hub supports running transforms on the edge or close to the data and streaming datasets directly to GPUs for training without an export step. **Pricing & Plans:** The Rerun SDK and viewer are open source and free to use. Rerun also offers Rerun Hub, a production backend, whose pricing is arranged separately with the team. Check the Rerun website and GitHub repository for current terms. - **Multimodal data visualization:** Log, store, and view images, point clouds, 3D transforms, and time series together in one viewer. - **Open-source logging SDK:** Record multi-rate, multimodal data as column-chunks in rrd files, or convert from other formats. - **Query and transform:** Run full dataframe or SQL queries over robotics data, down into columns, time ranges, and values. - **Edge and stream transforms:** Add derived columns and evolve schemas without breaking history, on the edge or near the data. - **Rerun Hub backend:** A hosted backend with catalog, byte-range indexing, and retrieval that turns object stores into a queryable dataset. **Strengths:** Open-source core with permissive use across research and production; Handles multimodal and multi-rate data in one viewer; APIs for Python, Rust, and C++; Query and transform data without an export step **Limitations:** Requires coding in Rust, Python, or C++ to get full value; The hosted Rerun Hub backend is a newer, additional product beyond the free SDK; Visualization work is aimed at engineering teams, not general business users; Advanced production features assume existing logging pipelines [Profile (Markdown)](https://openaitools.app/tool/rerun.md) ## Frequently Asked Questions ### How do Appen and Rerun compare in monthly traffic? Appen has 665,840 monthly visits, while Rerun has 54,018. Traffic estimates describe audience size, not product quality. ### How do their growth rates compare? Appen has a month-over-month growth rate of +0.2%; Rerun has -23.7%. ### How does pricing compare? Appen: Contact for Pricing. Enterprise/contact-for-pricing. Appen does not publish self-serve plans; projects are scoped and quoted individually. Appen also sells off-the-shelf (OTS) datasets and crowd/contractor workforce programs with separate terms. Rerun: Free. The Rerun SDK and viewer are open source and free to use. Rerun also offers Rerun Hub, a production backend, whose pricing is arranged separately with the team. Check the Rerun website and GitHub repository for current terms. ## Alternatives | # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | 1 | [Prolific](https://openaitools.app/tool/prolific-com) | 18,697,671 | -1.8% | Paid | Connects teams directly with verified human participants for preference tuning and studies, but lacks managed end-to-end labeling pipelines. | [.md](https://openaitools.app/tool/prolific-com.md) | | 2 | [Datadog](https://openaitools.app/tool/datadog) | 5,175,760 | -4.8% | Freemium, Free Trial, Paid, Contact for Pricing, Free | Unified observability, security and incident response for cloud-scale engineering teams | [.md](https://openaitools.app/tool/datadog.md) | | 3 | [Invisible Technologies](https://openaitools.app/tool/invisible-technologies-inc) | 1,364,520 | +64.4% | Contact for Pricing | Blends custom software with dedicated human expert teams to handle complex operations, but operates as a bespoke service. | [.md](https://openaitools.app/tool/invisible-technologies-inc.md) | | 4 | [Labelbox](https://openaitools.app/tool/labelbox) | 762,803 | -22.8% | Contact for Pricing | Combines robust data labeling tools with automated workflows, but focuses more on software tooling than managed human workforces. | [.md](https://openaitools.app/tool/labelbox.md) | | 5 | [Scale](https://openaitools.app/tool/scale) | 616,915 | +12.5% | Contact for Pricing | Provides enterprise-grade AI training data and model evaluation at massive scale, but often with higher enterprise pricing. | [.md](https://openaitools.app/tool/scale.md) | | 6 | [SuperAnnotate](https://openaitools.app/tool/super-annotate) | 386,034 | +37.4% | Free Trial, Contact for Pricing | Delivers advanced multimodal data curation and annotation infrastructure, but requires your own annotation workforce for labeling tasks. | [.md](https://openaitools.app/tool/super-annotate.md) | | 7 | [Weights & Biases (CoreWeave Forge)](https://openaitools.app/tool/prompts) | 315,624 | -80.2% | Freemium, Contact for Pricing, Free | Track, evaluate, and ship AI models and agents in one connected environment. | [.md](https://openaitools.app/tool/prompts.md) | | 8 | [Arize AI](https://openaitools.app/tool/arize-com) | 249,028 | +3.2% | Paid, Contact for Pricing | Agent observability, evaluation, and continuous improvement for production AI | [.md](https://openaitools.app/tool/arize-com.md) | | 9 | [Encord](https://openaitools.app/tool/encord-com) | 244,204 | +9.4% | Contact for Pricing | The multimodal data layer for computer vision and physical AI teams | [.md](https://openaitools.app/tool/encord-com.md) | | 10 | [Defined.ai](https://openaitools.app/tool/definedcrowd-com) | 146,251 | -21% | Contact for Pricing | Offers pre-packaged AI training datasets and custom data collection services, but with a smaller global crowd than Appen. | [.md](https://openaitools.app/tool/definedcrowd-com.md) | --- Source: https://openaitools.app/compare/appen-com-vs-rerun More Markdown: [Categories](https://openaitools.app/categories.md) · [Traffic rankings](https://openaitools.app/rankings.md) · [llms.txt](https://openaitools.app/llms.txt)