The open-source data labeling tool market is experiencing robust growth, driven by the increasing demand for high-quality training data in machine learning and artificial intelligence applications. The market's expansion is fueled by several factors: the rising adoption of AI across various sectors (including IT, automotive, healthcare, and finance), the need for cost-effective data annotation solutions, and the inherent flexibility and customization offered by open-source tools. While cloud-based solutions currently dominate the market due to scalability and accessibility, on-premise deployments remain significant, particularly for organizations with stringent data security requirements. The market's growth is further propelled by advancements in automation and semi-supervised learning techniques within data labeling, leading to increased efficiency and reduced annotation costs. Geographic distribution shows a strong concentration in North America and Europe, reflecting the higher adoption of AI technologies in these regions; however, Asia-Pacific is emerging as a rapidly growing market due to increasing investment in AI and the availability of a large workforce for data annotation. Despite the promising outlook, certain challenges restrain market growth. The complexity of implementing and maintaining open-source tools, along with the need for specialized technical expertise, can pose barriers to entry for smaller organizations. Furthermore, the quality control and data governance aspects of open-source annotation require careful consideration. The potential for data bias and the need for robust validation processes necessitate a strategic approach to ensure data accuracy and reliability. Competition is intensifying with both established and emerging players vying for market share, forcing companies to focus on differentiation through innovation and specialized functionalities within their tools. The market is anticipated to maintain a healthy growth trajectory in the coming years, with increasing adoption across diverse sectors and geographical regions. The continued advancements in automation and the growing emphasis on data quality will be key drivers of future market expansion.

The AI Data Labeling Solutions market is experiencing robust growth, driven by the increasing demand for high-quality data to train and improve the accuracy of AI and machine learning models. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This substantial growth is fueled by several key factors. The proliferation of AI applications across diverse sectors like healthcare, automotive, and finance necessitates extensive data labeling. The rise of sophisticated AI algorithms that require larger and more complex datasets is another major driver. Cloud-based solutions are gaining significant traction due to their scalability, cost-effectiveness, and ease of access, contributing significantly to market expansion. However, challenges remain, including data privacy concerns, the need for skilled data labelers, and the potential for bias in labeled data. These restraints need to be addressed to ensure the sustainable and responsible growth of the market. The segmentation of the market reveals a diverse landscape. Cloud-based solutions currently dominate, reflecting the industry shift toward flexible and scalable data processing. Application-wise, the IT sector is currently the largest consumer, followed by automotive and healthcare. However, growth in financial services and other sectors indicates the broadening application of AI data labeling solutions. Key players in the market are constantly innovating to improve accuracy, efficiency, and cost-effectiveness, leading to a competitive and rapidly evolving market. The regional distribution shows strong market presence in North America and Europe, driven by early adoption of AI technologies and a well-established technological infrastructure. Asia-Pacific is also demonstrating significant growth potential due to increasing technological advancements and investments in AI research and development. The forecast period of 2025-2033 presents substantial opportunities for market expansion, contingent upon addressing the challenges and leveraging emerging technologies.

Global Data Labeling Software market size 2025 was XX Million. Data Labeling Software Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

Snapshot img

Enterprise Labeling Software Market Size 2024-2028

The enterprise labeling software market size is forecast to increase by USD 133.9 mn at a CAGR of 6.59% between 2023 and 2028.

The market is witnessing significant growth due to several key trends. The adoption of enterprise labeling solutions is increasing as businesses seek to streamline their labeling processes and improve efficiency. Dynamic labeling, which allows for real-time updates to labels, is gaining popularity as it enables companies to quickly respond to changing regulations or product information. The market is experiencing growth as companies leverage data integration and analytics to streamline labeling processes, ensuring greater accuracy, compliance, and operational efficiency. Moreover, stringent government regulations mandating accurate and compliant labeling are driving the need for enterprise labeling software. These factors are expected to fuel market growth In the coming years. The market landscape is constantly evolving, and staying abreast of these trends is essential for businesses looking to remain competitive.

What will be the Size of the Enterprise Labeling Software Market During the Forecast Period?

Request Free Sample

The market encompasses solutions designed for creating, managing, and printing labels in various industries. Compliance with regulations and ensuring labeling accuracy are key drivers for this market. Real-time updates and customizable templates enable businesses to maintain consistency and adapt to changing requirements. Integration capabilities with enterprise systems, data management planning, and the printing process are essential for streamlining workflows and improving efficiency. Innovative technology, such as automation and machine learning, enhances labeling quality and speed, providing a competitive edge.
A user-friendly interface and economic conditions influence market demand. Urbanization and the growing need for packaging solutions, branding, and on-premises-based software further expand the market's reach. Overall, the market continues to grow, offering significant benefits to businesses seeking to optimize their labeling processes.

How is this Enterprise Labeling Software Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  On-premise
  Cloud


End-user

  FMCG
  Retail and e-commerce
  Healthcare
  Warehousing and logistics
  Others


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    Germany


  Middle East and Africa



  South America

By Deployment Insights

The on-premise segment is estimated to witness significant growth during the forecast period.

The market is driven by the need for compliance, creation, management, printing, and real-time updates of labels in various industries. Large enterprises require unique labeling solutions to meet diverse industry standards and traceability regulations, ensuring product quality and customer satisfaction. On-premise and cloud-based enterprise labeling software offer agility, scalability, and flexibility, optimizing operations and enhancing resilience and adaptability. Compliance management, seamless collaboration, contactless processes, safety measures, and predictive analytics are key features. Driving factors include digitalization, automation, and evolving challenges in logistics and e-commerce. However, varying industry standards, implementation costs, legacy systems, and integration challenges pose restraining factors. Enterprise labeling software solutions offer customizable templates, integration capabilities, and language support, catering to the economic condition, urbanization, and packaging solutions.

Brands prioritize a data-driven approach and regulatory requirements In their labeling strategy. The market is expected to grow, with key players catering to enterprise sizes and time to market.

Get a glance at the Enterprise Labeling Software Industry report of share of various segments Request Free Sample

The On-premise segment was valued at USD 163.80 mn in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 41% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

The market in APAC is projected to experience significant growth due to the increasing number of end-users in sectors such as food and beverage, personal care products, and pharmaceuticals.

The U.S. Data Collection And Labeling Market size was valued at USD 855.0 million in 2023 and is projected to reach USD 3964.16 million by 2032, exhibiting a CAGR of 24.5 % during the forecasts period. The US Data Collection and Labeling Market implies the process of gathering and labeling data for the creation of machine learning, artificial intelligence, as well as other data-related applications. The market helps various sectors including retail health care, automotive, and finance through supplying labeled data which is critical in training and improving models used in AI and overall decision-making. Some of the primary applications are related to image and speech recognition, self-driving cars and many others related to Predictive analysis. New directions promote the development of a greater degree of automatization of processes, the use of highly specialized annotation tools, and the need for further development of specialized data labeling services. The market is also experiencing incorporation of artificial intelligence for the automation of several data labeling tasks. Recent developments include: In July 2022, IBM announced the acquisition of Databand.ai to augment its software portfolio across AI, data and automation. For the record, Databand.ai was IBM's fifth acquisition in 2022, signifying the latter’s commitment to hybrid cloud and AI skills and capabilities. , In June 2022, Oracle completed the acquisition of Cerner as the Austin-based company gears up to ramp up its cloud business in the hospital and health system landscape. .

Explore the Data Collection And Labeling Global Market Report 2025 Market trends! Covers key players, growth rate 28.4% CAGR, market size $12.08 Billion, and forecasts to 2033. Get insights now!

The Enterprise Labeling Software market is experiencing robust growth, with a market size of $356.23 million in 2025 and a projected Compound Annual Growth Rate (CAGR) of 6.59% from 2025 to 2033. This expansion is driven by several key factors. The increasing need for efficient supply chain management across various industries, including FMCG, retail, healthcare, and warehousing & logistics, fuels the demand for sophisticated labeling solutions. Furthermore, the shift towards cloud-based deployment models offers scalability and cost-effectiveness, contributing significantly to market growth. Stringent regulatory compliance requirements across sectors also necessitate accurate and standardized labeling, further boosting market adoption. Competition is intense, with established players and emerging companies vying for market share through strategic partnerships, product innovation, and geographic expansion. While the on-premise deployment model still holds a significant portion of the market, the cloud segment is witnessing faster growth, driven by its flexibility and accessibility benefits. Regionally, North America and Europe currently dominate the market, but the APAC region, particularly China and India, is expected to witness substantial growth in the coming years due to expanding industrialization and e-commerce sectors. The market's restraints include the high initial investment costs associated with enterprise labeling software implementation and the need for specialized technical expertise for efficient deployment and maintenance. However, these challenges are being mitigated by the emergence of user-friendly interfaces, cloud-based subscription models, and the availability of robust vendor support. Future growth will be significantly influenced by advancements in technologies like AI and machine learning, enabling automated label generation and improved data management capabilities. The continued integration of barcode and RFID technologies within labeling systems will further enhance supply chain visibility and efficiency. The competitive landscape is marked by a mix of large multinational corporations and specialized software providers, creating a dynamic environment for innovation and strategic partnerships.

The data collection and labeling market is experiencing robust growth, fueled by the escalating demand for high-quality training data in artificial intelligence (AI) and machine learning (ML) applications. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 25% over the forecast period (2025-2033), reaching approximately $75 billion by 2033. This expansion is primarily driven by the increasing adoption of AI across diverse sectors, including healthcare (medical image analysis, drug discovery), automotive (autonomous driving systems), finance (fraud detection, risk assessment), and retail (personalized recommendations, inventory management). The rising complexity of AI models and the need for more diverse and nuanced datasets are significant contributing factors to this growth. Furthermore, advancements in data annotation tools and techniques, such as active learning and synthetic data generation, are streamlining the data labeling process and making it more cost-effective. However, challenges remain. Data privacy concerns and regulations like GDPR necessitate robust data security measures, adding to the cost and complexity of data collection and labeling. The shortage of skilled data annotators also hinders market growth, necessitating investments in training and upskilling programs. Despite these restraints, the market’s inherent potential, coupled with ongoing technological advancements and increased industry investments, ensures sustained expansion in the coming years. Geographic distribution shows strong concentration in North America and Europe initially, but Asia-Pacific is poised for rapid growth due to increasing AI adoption and the availability of a large workforce. This makes strategic partnerships and global expansion crucial for market players aiming for long-term success.

Variable Data Printing Labels Market Report is Segmented by Component (Hardware, Software and Services), by Label (Release Liner Labels and Linerless Labels), by Printing Method (Thermal Transfer Printing, Direct Thermal Printing, Inkjet Printing, Electrophotography, Flexographic Printing and Other Methods), by End-Use Industry (Healthcare, Retail & E-Commerce, Food & Beverage, Logistics and Other End-Use Industries), and by Geography (North America, Europe, Asia Pacific, South America and Middle East and Africa). The Market Sizing and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The open dataset, software, and other files accompanying the manuscript "An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models," submitted for publication to Integrated Materials and Manufacturing Innovations. Machine learning and autonomy are increasingly prevalent in materials science, but existing models are often trained or tuned using idealized data as absolute ground truths. In actual materials science, "ground truth" is often a matter of interpretation and is more readily determined by consensus. Here we present the data, software, and other files for a study using as-obtained diffraction data as a test case for evaluating the performance of machine learning models in the presence of differing expert opinions. We demonstrate that experts with similar backgrounds can disagree greatly even for something as intuitive as using diffraction to identify the start and end of a phase transformation. We then use a logarithmic likelihood method to evaluate the performance of machine learning models in relation to the consensus expert labels and their variance. We further illustrate this method's efficacy in ranking a number of state-of-the-art phase mapping algorithms. We propose a materials data challenge centered around the problem of evaluating models based on consensus with uncertainty. The data, labels, and code used in this study are all available online at data.gov, and the interested reader is encouraged to replicate and improve the existing models or to propose alternative methods for evaluating algorithmic performance.

The size of the Data Annotation Tool Market market was valued at USD 3.9 USD billion in 2023 and is projected to reach USD 6.64 USD billion by 2032, with an expected CAGR of 7.9% during the forecast period. A Data Annotation Tool is defined as the software that can be employed to make annotations to data hence helping a learning computer model learn patterns. These tools provide a way of segregating the data types to include images, texts, and audio, as well as videos. Some of the subcategories of annotation include images such as bounding boxes, segmentation, text such as entity recognition, sentiment analysis, audio such as transcription, sound labeling, and video such as object tracking. Other common features depend on the case but they commonly consist of interfaces, cooperation with others, suggestion of labels, and quality assurance. It can be used in the automotive industry (object detection for self-driving cars), text processing (classification of text), healthcare (medical imaging), and retail (recommendation). These tools get applied in training good quality, accurately labeled data sets for the engineering of efficient AI systems. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Adverse Health Effect May Hamper Market Growth. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Leaves from genetically unique Juglans regia plants were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA). Soil samples were collected in Fall of 2017 from the riparian oak forest located at the Russell Ranch Sustainable Agricultural Institute at the University of California Davis. The soil was sieved through a 2 mm mesh and was air dried before imaging. A single soil aggregate was scanned at 23 keV using the 10x objective lens with a pixel resolution of 650 nanometers on beamline 8.3.2 at the ALS. Additionally, a drought stressed almond flower bud (Prunus dulcis) from a plant housed at the University of California, Davis, was scanned using a 4x lens with a pixel resolution of 1.72 µm on beamline 8.3.2 at the ALS Raw tomographic image data was reconstructed using TomoPy. Reconstructions were converted to 8-bit tif or png format using ImageJ or the PIL package in Python before further processing. Images were annotated using Intel’s Computer Vision Annotation Tool (CVAT) and ImageJ. Both CVAT and ImageJ are free to use and open source. Leaf images were annotated in following Théroux-Rancourt et al. (2020). Specifically, Hand labeling was done directly in ImageJ by drawing around each tissue; with 5 images annotated per leaf. Care was taken to cover a range of anatomical variation to help improve the generalizability of the models to other leaves. All slices were labeled by Dr. Mina Momayyezi and Fiona Duong.To annotate the flower bud and soil aggregate, images were imported into CVAT. The exterior border of the bud (i.e. bud scales) and flower were annotated in CVAT and exported as masks. Similarly, the exterior of the soil aggregate and particulate organic matter identified by eye were annotated in CVAT and exported as masks. To annotate air spaces in both the bud and soil aggregate, images were imported into ImageJ. A gaussian blur was applied to the image to decrease noise and then the air space was segmented using thresholding. After applying the threshold, the selected air space region was converted to a binary image with white representing the air space and black representing everything else. This binary image was overlaid upon the original image and the air space within the flower bud and aggregate was selected using the “free hand” tool. Air space outside of the region of interest for both image sets was eliminated. The quality of the air space annotation was then visually inspected for accuracy against the underlying original image; incomplete annotations were corrected using the brush or pencil tool to paint missing air space white and incorrectly identified air space black. Once the annotation was satisfactorily corrected, the binary image of the air space was saved. Finally, the annotations of the bud and flower or aggregate and organic matter were opened in ImageJ and the associated air space mask was overlaid on top of them forming a three-layer mask suitable for training the fully convolutional network. All labeling of the soil aggregate and soil aggregate images was done by Dr. Devin Rippner. These images and annotations are for training deep learning models to identify different constituents in leaves, almond buds, and soil aggregates Limitations: For the walnut leaves, some tissues (stomata, etc.) are not labeled and only represent a small portion of a full leaf. Similarly, both the almond bud and the aggregate represent just one single sample of each. The bud tissues are only divided up into buds scales, flower, and air space. Many other tissues remain unlabeled. For the soil aggregate annotated labels are done by eye with no actual chemical information. Therefore particulate organic matter identification may be incorrect. Resources in this dataset:Resource Title: Annotated X-ray CT images and masks of a Forest Soil Aggregate. File Name: forest_soil_images_masks_for_testing_training.zipResource Description: This aggregate was collected from the riparian oak forest at the Russell Ranch Sustainable Agricultural Facility. The aggreagate was scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 0,0,0; pores spaces have a value of 250,250, 250; mineral solids have a value= 128,0,0; and particulate organic matter has a value of = 000,128,000. These files were used for training a model to segment the forest soil aggregate and for testing the accuracy, precision, recall, and f1 score of the model.Resource Title: Annotated X-ray CT images and masks of an Almond bud (P. Dulcis). File Name: Almond_bud_tube_D_P6_training_testing_images_and_masks.zipResource Description: Drought stressed almond flower bud (Prunis dulcis) from a plant housed at the University of California, Davis, was scanned by X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 4x lens with a pixel resolution of 1.72 µm using. For masks, the background has a value of 0,0,0; air spaces have a value of 255,255, 255; bud scales have a value= 128,0,0; and flower tissues have a value of = 000,128,000. These files were used for training a model to segment the almond bud and for testing the accuracy, precision, recall, and f1 score of the model.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads Resource Title: Annotated X-ray CT images and masks of Walnut leaves (J. Regia) . File Name: 6_leaf_training_testing_images_and_masks_for_paper.zipResource Description: Stems were collected from genetically unique J. regia accessions at the 117 USDA-ARS-NCGR in Wolfskill Experimental Orchard, Winters, California USA to use as scion, and were grafted by Sierra Gold Nursery onto a commonly used commercial rootstock, RX1 (J. microcarpa × J. regia). We used a common rootstock to eliminate any own-root effects and to simulate conditions for a commercial walnut orchard setting, where rootstocks are commonly used. The grafted saplings were repotted and transferred to the Armstrong lathe house facility at the University of California, Davis in June 2019, and kept under natural light and temperature. Leaves from each accession and treatment were scanned using X-ray micro-computed tomography (microCT) on the X-ray μCT beamline (8.3.2) at the Advanced Light Source (ALS) in Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA USA) using the 10x objective lens with a pixel resolution of 650 nanometers. For masks, the background has a value of 170,170,170; Epidermis value= 85,85,85; Mesophyll value= 0,0,0; Bundle Sheath Extension value= 152,152,152; Vein value= 220,220,220; Air value = 255,255,255.Resource Software Recommended: Fiji (ImageJ),url: https://imagej.net/software/fiji/downloads

This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020. 1. data folder The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL. 2. scripts floder The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms. smell-term/general_smell_terms.txt contains general code smell terms, such as "code smell". smell-term/specific_smell_terms.txt contains specific code smell terms, such as "dead code". smell-term/misspelling_terms_of_smell.txt contains the misspelling terms of 'smell', such as "ssell". get_changes.py is used for getting code changes from OpenStack. get_comments.py is used for getting review comments for each code change. smell_search.py is used for searching review comments that contain code smell terms. 3. project folder The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download. Data Labeling & Encoding for RQ2.mx12 is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool. Data Labeling & Encoding for RQ3.mx12 is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.

We present Proline (http://www.profiproteomics.fr/proline/), a robust software suite for analysis of MS-based proteomics data; it provides high performance in a user-friendly interface for all data set sizes from small to very large

The size and share of the market is categorized based on Application (Data classification tools, Data tagging software, Information categorization tools, Data labeling tools, Data organization solutions) and Product (Data management, Security enhancement, Compliance management, Information governance, Risk management) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The global label creation and management software market is anticipated to grow from USD 395 million in 2025 to USD 640.3 million by 2033, at a CAGR of 6% from 2025 to 2033. The increasing requirement for efficient and automated labeling processes in various industries, such as food and beverage, manufacturing, logistics and shipping, healthcare, and retail, is driving the market growth. The adoption of cloud-based solutions for label creation and management is also gaining traction due to its cost-effectiveness and flexibility. Furthermore, the rising demand for customized and high-quality labels, along with the growing need for compliance with regulatory standards, is contributing to the demand for label creation and management software. The market is fragmented with numerous companies, both established and emerging, offering a wide range of solutions. Key players include Loftware, Seagull Scientific, Esko, TEKLYNX, enLabel Global Services, OPAL Associates, 5FLOW, Brady, Perigord, MHC Automation, Kallik, Innovatum, Tharo Systems, CYBRA, Artwork Flow (Bizongo), and Zebra. Regional analysis shows that North America currently dominates the market, followed by Europe and Asia Pacific. The Asia Pacific region is expected to witness significant growth in the coming years due to the increasing industrialization and urbanization in the region.

Machine learning (ML) has gained much attention and has been incorporated into our daily lives. While there are numerous publicly available ML projects on open source platforms such as GitHub, there have been limited attempts in filtering those projects to curate ML projects of high quality. The limited availability of such high-quality dataset poses an obstacle to understanding ML projects. To help clear this obstacle, we present NICHE, a manually labelled dataset consisting of 572 ML projects. Based on evidences of good software engineering practices, we label 441 of these projects as engineered and 131 as non-engineered. In this repository we provide "NICHE.csv" file that contains the list of the project names along with their labels, descriptive information for every dimension, and several basic statistics, such as the number of stars and commits. This dataset can help researchers understand the practices that are followed in high-quality ML projects. It can also be used as a benchmark for classifiers designed to identify engineered ML projects.

GitHub page: https://github.com/soarsmu/NICHE

The global structured product labeling (SPL) software market is projected to reach USD XX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The growth of the market is attributed to factors such as the increasing demand for efficient and accurate product labeling, growing regulatory requirements for product safety, and the adoption of cloud-based SPL solutions. The market is segmented based on application, type, and region. Among applications, the biopharmaceutical segment is expected to hold a significant market share due to the stringent regulatory requirements for pharmaceutical products. In terms of type, the cloud hosting deployment segment is anticipated to witness substantial growth due to its cost-effectiveness, flexibility, and scalability. Geographically, North America is projected to dominate the market, followed by Europe and Asia Pacific. The presence of a large number of biopharmaceutical companies and CROs in these regions is a key driver for market growth.

Isobaric labeling-based proteomics is widely applied in deep proteome quantification. Among the platforms for isobaric labeled proteomic data analysis, the commercial software Proteome Discoverer (PD) is widely used, incorporating the search engine CHIMERYS, while FragPipe (FP) is relatively new, free for noncommercial purposes, and integrates the engine MSFragger. Here, we compared PD and FP over three public proteomic data sets labeled using 6plex, 10plex, and 16plex tandem mass tags. Our results showed the protein abundances generated by the two software are highly correlated. PD quantified more proteins (10.02%, 15.44%, 8.19%) than FP with comparable NA ratios (0.00% vs. 0.00%, 0.85% vs. 0.38%, and 11.74% vs. 10.52%) in the three data sets. Using the 16plex data set, PD and FP outputs showed high consistency in quantifying technical replicates, batch effects, and functional enrichment in differentially expressed proteins. However, FP saved 93.93%, 96.65%, and 96.41% of processing time compared to PD for analyzing the three data sets, respectively. In conclusion, while PD is a well-maintained commercial software integrating various additional functions and can quantify more proteins, FP is freely available and achieves similar output with a shorter computational time. Our results will guide users in choosing the most suitable quantification software for their needs.