Ukuthunyelwa Kwemodeli Yokufunda Ngomshini
I-SaaS Browser ilandelela izinkampani zesofthiwe ze-104 Ukuthunyelwa Kwemodeli Yokufunda Ngomshini, kanti i-16 ingeziwe ezinsukwini ezingu-30 ezedlule. Amazwe aphezulu e-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS afaka phakathi i-United States (9), India (2), Cyprus (1).
Amaphuzu Edatha Abalulekile e-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini
104
Isofthiwe Ephelele1
16
Okusha (Izinsuku ezingu-30 ezedlule)2
3
Iminyaka Emaphakathi (Iminyaka)4
+76
Okusha (Izinsuku ezingu-365 ezedlule)5
11
I-Churned (Unyaka Odlule)6
12.6%
Izinga Lonyaka Lokukhushulwa7
27.9%
Isivivinyo Samahhala8
Ukusatshalaliswa Kosayizi Wenkampani9
1
Okukhulu (201-1,000)
5.3% kwesamba
18
Okuncane (2-50)
94.7% kwesamba
Izinkampani Ezise-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini Ezisungulwe Kakhulu
| Inkampani | Abasebenzi | Izinga Lesizinda | Yasungulwa | Kungeziwe |
|---|---|---|---|---|
| Gradio | — | 71 | — | 2026-01-08 |
| Metaflow | — | 56 | — | 2025-02-27 |
| InterpretML | — | 52 | — | 2025-06-08 |
| ApX Machine Learning | 10 | 50 | 2024 | 2025-02-09 |
| NannyML | 10 | 47 | — | 2025-02-08 |
| Nextmv | 50 | 46 | 2019 | 2025-02-08 |
| Inferless | 10 | 45 | 2023 | 2025-02-10 |
| Jaqpot | — | 42 | — | 2025-02-11 |
| Datatron | 50 | 41 | 2016 | 2025-02-08 |
| MLOne | 50 | 36 | 2022 | 2025-04-21 |
Amazwe ayi-10 aphezulu e-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS
Amazwe alandelayo amelela lapho izinkampani eziningi ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS zinezinhloko zazo, ngokusekelwe kudatha yethu yemikhiqizo yesofthiwe ye-104.
| Izwe | Bala | % wesamba |
|---|---|---|
| United States | 9 | 8.7% |
| India | 2 | 1.9% |
| Cyprus | 1 | 1.0% |
| Spain | 1 | 1.0% |
| United Kingdom | 1 | 1.0% |
| Israel | 1 | 1.0% |
| Australia | 1 | 1.0% |
| Italy | 1 | 1.0% |
| Sri Lanka | 1 | 1.0% |
| Mali | 1 | 1.0% |
I-SEO kanye ne-Domain Authority ye-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini
Isilinganiso Sezinga Lesizinda10
15
uma kuqhathaniswa nesilinganiso esiphelele se-13
Idatha yamanani e-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini
Intengo Ephakathi Yokuqala (i-USD)12
$0/ngenyanga
Intengo Ephakeme Emaphakathi (i-USD)13
$60/ngenyanga
Ukugcwala Kwemakethe kwe-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini
Leli phuzu libalwa kusukela ekuhlanganisweni kwemikhiqizo iyonke, igunya lesizinda, izizinda ezibhekisela kuzo, ubudala benkampani, ukukhula kwethrafikhi, amanani, kanye nokuba khona kwebhizinisi.
Isikolo Sokugcwalisa14
25 / 100 (Phansi)
Okusanda Kwengezwa i-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS
Premforge
Kungezwe i-2026-10-16
A3P Cloud
Kungezwe i-2026-10-16
mantid
Kungezwe i-2026-10-16
ModelArcs
Kungezwe i-2026-10-13
FlexAI
Kungezwe i-2026-10-10
MatrixAI
Kungezwe i-2026-10-08
Optywise
Kungezwe i-2026-10-07
Asymtot
Kungezwe i-2026-10-07
inferctl
Kungezwe i-2026-10-06
SursumAI
Kungezwe i-2026-10-05
Isilinganiso Seminyaka Yezinkampani ze-SaaS ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini
Inkampani ejwayelekile ye-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS yasungulwa eminyakeni engu-3.0 eyedlule (isilinganiso sonyaka wokusungulwa: 2023).
Ukuhlukaniswa kwe-B2B / B2C kwe-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini16
18
Kokubili i-B2B ne-B2C
17.3%
86
Ibhizinisi (B2B)
82.7%
Kususelwa ezinkampanini ze-104 ezinedatha yohlobo lwabathengi (100.0% yenani eliphelele).
Ithrendi Yokutholwa Kwe-SaaS ye-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini
Inani lokusakazwa lwe-SaaS lenyanga zonke ye-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini uma kuqhathaniswa nengxenye yayo ye-SaaS yonke etholakele. Iphesenti elikhulayo likhombisa ukuthi lesi sigaba sikhula ngokushesha kunemakethe jikelele.
Thola i-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS
Bonisa Izihlungi
Izihlungi Ezithuthukile
X
This tool provides a rapid, user-friendly platform for designing and deploying interactive web interfaces that showcase machine learning models, making complex AI accessible to a broad audience. Its main features include quick setup with minimal coding, seamless integration with Python libraries, automatic generation of shareable links or web embeds, and options for permanent hosting on cloud services, which collectively facilitate prototype development, model demonstration, and remote collaboration. It addresses challenges faced by data scientists, developers, and researchers in efficiently visualizing, sharing, and deploying AI models without extensive web development expertise, enabling faster iteration, broader accessibility, and real-time testing across various domains such as computer vision, natural language processing, and healthcare.
Machine Learning Model Deployment
Isigaba salolu hlu.
Voxotec
Umnikazi uqinisekise imininingwane yephrofayela yakhe eboniswe kuleli khasi.
This platform offers comprehensive AI-driven solutions designed to automate workflows, enhance customer engagement, and optimize business operations through voice and chat experiences, analytics, and integrations. Its main features include intelligent voice assistants, chatbots, seamless CRM and data warehouse integrations, real-time analytics dashboards, and rapid deployment of web systems, solving problems like manual task overload, slow response times, and disjointed communication channels. It is ideal for businesses seeking to increase efficiency, improve customer interactions, and scale operations quickly by leveraging advanced automation, natural language processing, and data insights.
Intengo:
A$2800-A$8200/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Sales Automation Software
Isigaba salolu hlu.
Business Intelligence & Analytics
Isigaba salolu hlu.
MatrixAI
Umnikazi uqinisekise imininingwane yephrofayela yakhe eboniswe kuleli khasi.
This solution provides an on-premise, auditable machine learning platform that allows users to build, evaluate, and deploy predictive models directly on their own infrastructure, ensuring data privacy and security. Its main features include supporting multiple machine learning engines such as gradient-boosted trees and neural networks, automated model selection through cross-validation, detailed decision explanations with transparent reasoning, and the ability to run models locally via Docker on CPU or GPU hardware. It addresses challenges related to data confidentiality, reproducibility, and model interpretability, making it ideal for data scientists, organizations with sensitive datasets, and those seeking trustworthy, transparent AI development without relying on proprietary black-box systems.
Intengo:
€2/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
EasyDeploy AI
Isivivinyo Samahhala
Umnikazi uqinisekise imininingwane yephrofayela yakhe eboniswe kuleli khasi.
EasyDeploy AI Light Mode enables businesses of all sizes to quickly develop and deploy machine learning models without extensive technical expertise or costly infrastructure, streamlining data-driven decision-making processes. Its core features include automated model building, real-time predictions, customizable data analysis, and seamless integration for applications such as customer churn forecasting, demand planning, lead scoring, inventory optimization, and marketing performance enhancement, solving problems related to resource allocation, forecasting accuracy, customer retention, and operational efficiency. This platform is designed for business professionals, small to medium enterprises, marketers, data analysts, and operational managers seeking accessible, scalable AI solutions to improve strategic planning, optimize workflows, and gain competitive advantage without the need for in-house data science teams.
Intengo:
$499-$4999/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
Daten & Wissen
Umnikazi uqinisekise imininingwane yephrofayela yakhe eboniswe kuleli khasi.
This advanced AI-powered platform transforms raw data and multimedia streams into actionable insights by utilizing computer vision, natural language processing, predictive analytics, and custom AI agents to automate, optimize, and enhance various business operations. Its main features include intelligent video analytics, real-time alerts, automated detection systems, customizable AI solutions tailored to specific industry needs, and autonomous agents that learn and act independently, solving problems related to security, operational efficiency, customer engagement, compliance, and predictive maintenance for diverse sectors such as healthcare, banking, pharmaceuticals, FMCG, education, and manufacturing. Designed for enterprises, industry professionals, and organizations seeking innovative solutions, it helps improve safety, reduce costs, automate workflows, forecast future trends, and deliver personalized experiences, thereby addressing industry-specific challenges with.
Intengo:
₹400-₹4000/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
Fenn
Umnikazi uqinisekise imininingwane yephrofayela yakhe eboniswe kuleli khasi.
Fenn is an open-source framework designed to streamline deep learning workflows by automating configuration management, structured logging, experiment tracking, and monitoring, all while allowing full access to underlying PyTorch code. Its main features include YAML-based experiment configuration, automated logging and hyperparameter tracking, seamless integration with dashboards and notification systems for real-time updates, and pre-built training templates to enhance reproducibility and efficiency for machine learning practitioners. This tool addresses common challenges faced by data scientists and ML engineers such as experiment reproducibility, cumbersome configuration management, inconsistent logging, and difficulty monitoring long-running training processes, making it ideal for researchers, developers, and teams working on complex deep learning projects.
Machine Learning Model Deployment
Isigaba salolu hlu.
Data Science Notebooks & Platforms
Isigaba salolu hlu.
Datrov
Isivivinyo Samahhala
Umnikazi uqinisekise imininingwane yephrofayela yakhe eboniswe kuleli khasi.
This platform provides an integrated environment for data analysis, machine learning model development, deployment, and interactive querying, streamlining data workflows for both technical and non-technical users. It offers features such as seamless data ingestion from various sources, automatic model training with different algorithms, instant deployment of models as accessible APIs, natural language-based data interrogation, and secure workspace management—all designed to solve challenges related to data modeling, deployment complexity, and collaborative insights, making it ideal for data analysts, data scientists, and software engineers aiming to accelerate data-driven decision-making and operational efficiency.
Intengo:
$0-$20/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
AI-powered Document Processing
Isigaba salolu hlu.
Machine Learning Model Deployment
Isigaba salolu hlu.
VectorScaleDB
Umnikazi uqinisekise imininingwane yephrofayela yakhe eboniswe kuleli khasi.
This system is a specialized database that unifies time-series data, vector embeddings, and multidimensional spatial information within a single, efficient index, enabling advanced temporal and behavioral analysis. Its key features include ultra-low query latency under one millisecond, native support for similarity searches across behavioral trajectories, anomaly detection, and pattern recognition over time, while overcoming the limitations of traditional time-series and vector databases. It addresses complex challenges faced by organizations managing diverse temporal, spatial, and semantic data sources, simplifying data architecture by replacing multiple disparate systems and empowering users such as data scientists, engineers, and analytics teams to perform sophisticated temporal-semantic queries, behavioral pattern matching, and anomaly detection seamlessly.
Intengo:
Kusuka ku-$0/ngenyanga
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
API & Backend-as-a-Service Platforms
Isigaba salolu hlu.
Machine Learning Model Deployment
Isigaba salolu hlu.
The platform facilitates the efficient deployment, monitoring, and governance of machine learning models, significantly reducing the time and cost associated with bringing models into production. Key features include real-time monitoring for bias and performance anomalies, a centralized model catalog for streamlined management, and seamless integration with existing IT infrastructures, making it ideal for businesses, data scientists, and engineering teams looking to enhance their AI capabilities while ensuring compliance and operational efficiency.
Machine Learning Model Deployment
Isigaba salolu hlu.
This solution provides comprehensive tools for analyzing, monitoring, and optimizing the costs associated with deploying large language models (LLMs) and artificial intelligence systems, addressing challenges such as hidden expenses, security threats, and inefficient resource usage. Its main features include detailed cost tracking by task, agent, and customer; real-time security measures to block malicious prompts and injection attacks; simulation capabilities to evaluate different pricing models; and side-by-side model comparisons to forecast expenses and performance. It is designed for organizations deploying AI at scale, data science teams, AI developers, and operations managers seeking to reduce operational costs, enhance security, improve efficiency, and make informed pricing and deployment decisions.
Machine Learning Model Deployment
Isigaba salolu hlu.
"Deployment from Scratch" is a comprehensive guide that teaches how to set up and deploy web applications using Linux virtual servers and Docker containers. With over 1,000 copies sold and a 40x five-star rating, this book simplifies the process of taking applications to production, making it accessible for both beginners and experienced developers.
Intengo:
$35-$50/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
MLOne is a cloud-based platform that simplifies building, deploying, and managing machine learning models without the need for complex code. With fully automated processes, users can focus on model development while the platform handles deployment and monitoring seamlessly.
Intengo:
Kusuka ku-$100/ngenyanga
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
This open-source framework streamlines the development and management of real-world machine learning, artificial intelligence, and data science projects, enabling users to efficiently build workflows using familiar Python libraries. Key features include seamless deployment to production, automatic versioning for easy experiment tracking, robust orchestration of workflows, and the ability to leverage cloud computing resources, making it ideal for machine learning engineers and data scientists seeking to enhance collaboration and scalability in their projects.
Machine Learning Model Deployment
Isigaba salolu hlu.
This tool provides a unified framework for managing and centralizing machine learning data logic, enhancing productivity and reducing data debt through flexible data contract and model contract definitions. It offers features such as a Pythonic interface using dataclass-like structures, seamless integration with data sources and model deployment environments, automated data transformation and validation, and support for versioning and documentation, solving problems related to inconsistent data handling, manual code duplication, and complex data pipeline management. Designed for data scientists, machine learning engineers, and AI practitioners, it streamlines the development, testing, and deployment of machine learning systems by ensuring reliable, adaptable, and maintainable data and model workflows.
Machine Learning Model Deployment
Isigaba salolu hlu.
ApX Machine Learning
Le SaaS iyathrenda! Sithole ukukhula kwethrafikhi kwanyanga zonke.
This open-source toolkit provides production-ready Python utilities and modular components for building large language model applications, including capabilities for data chunking, embedding creation, retrieval-augmented generation, and agent orchestration to assemble end-to-end AI workflows. It solves memory management, deployment, and scalability challenges by offering a local model directory, memory-footprint estimators to prevent out-of-memory errors, performance rankings, and tooling for quantization and fine-tuning, making it ideal for AI students, developers, and researchers who want practical, production-grade LLM development and experimentation.
Intengo:
$0-$59/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
InterpretML is a toolkit designed to help users understand machine learning models and promote responsible practices in model development. It offers a range of state-of-the-art techniques for model interpretability, allowing users to explore model behavior, debug issues, and ensure compliance with regulatory standards.
Machine Learning Model Deployment
Isigaba salolu hlu.
3LC offers a powerful platform designed to enhance machine learning workflows by providing real-time debugging, diagnosis, and improvement of training data. With features like model-guided data visualization, dataset versioning, and seamless integration into existing ML tools, users can gain deeper insights into their models and optimize performance efficiently.
Machine Learning Model Deployment
Isigaba salolu hlu.
NannyML Cloud offers a comprehensive solution for monitoring machine learning models post-deployment, focusing on performance-centric workflows that prioritize meaningful alerts and actionable insights. With advanced capabilities for detecting data drift and automating monitoring processes, it empowers data science teams to maintain optimal model performance without the burden of false alarms.
Intengo:
$0-$3800/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
This platform streamlines the development and management of decision models through an integrated DecisionOps workflow, enabling users to efficiently validate, deploy, and monitor their models. Key features include a model marketplace for tailored applications, seamless integration with existing tools, comprehensive logging for performance tracking, and the ability to conduct scenario simulations, making it ideal for operations researchers, data scientists, software developers, and business stakeholders seeking to enhance decision-making processes and maximize the value of their optimization efforts.
Intengo:
$20-$250/mo
Ibanga lanyanga zonke lentengo yalo mkhiqizo we-SaaS. Izintengo zenziwa ngokwejwayelekile zibe amanani enyanga lapho kungenzeka khona.
Machine Learning Model Deployment
Isigaba salolu hlu.
This open-source toolkit enables teams to wrangle unstructured data in Python at scale, harness AI-assisted preprocessing, and track experiments while sharing insights from machine learning projects. It automatically detects ML frameworks, Python requirements, and data schemas; stores model metadata in a human-readable YAML format; supports a registry-like workflow and versioned artifacts; deploys models anywhere with a single command, and switches deployment targets effortlessly, making it ideal for data scientists, ML engineers, researchers, and platform teams seeking reproducibility, deployment flexibility, and streamlined collaboration.
Machine Learning Model Deployment
Isigaba salolu hlu.
* Ezinye noma zonke izingxenye zaleli khasi zingase zenziwe i-AI, ngakho-ke sicela uqinisekise noma yiluphi ulwazi olubalulekile ngokuzimela.
imibuzo ejwayelekile ukubuzwa
Zingaki izinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS ezikhona?
Isiphequluli se-SaaS silandelela imikhiqizo yesofthiwe ye-104 Ukuthunyelwa Kwemodeli Yokufunda Ngomshini kusukela ku-October 2026. Izinkampani ezintsha ze-16 Ukuthunyelwa Kwemodeli Yokufunda Ngomshini ze-SaaS zengezwe ezinsukwini ezingu-30 ezedlule.
Zingaki izinkampani ezintsha ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS ezingezwayo njalo ngenyanga?
Izinkampani ezintsha ze-16 Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS zengezwe ku-SaaS Browser ezinsukwini ezingu-30 ezedlule. Ingqalasizinda yethu yokukhasa eyimfihlo iyaqhubeka nokuthola imikhiqizo emisha yesofthiwe.
Yiziphi izinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS ezisungulwe kakhulu?
Izinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS ezidumile kakhulu ngokwegunya lesizinda zifaka phakathi i-Gradio, Metaflow, InterpretML, ApX Machine Learning, NannyML. Lezi zinkampani zimele amaphuzu aphezulu kakhulu esizinda esigabeni se-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini.
Ungakanani usayizi wenkampani ojwayelekile we-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS?
Phakathi kwezinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS ezinemininingwane yabasebenzi, ukusatshalaliswa kosayizi yilokhu: - 1 (5.3%), - 18 (94.7%).
Liyini igunya elimaphakathi lesizinda sezinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS?
Izinga lesizinda elimaphakathi lezinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS lingu-15, uma kuqhathaniswa nephakathi le-SaaS lisonke elingu-13.
Yimaphi amazwe anezinkampani ze-SaaS ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini eziningi kakhulu?
Amazwe aphezulu e-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS yilawa: United States (9), India (2), Cyprus (1), Spain (1), United Kingdom (1). Lawa amelela amazinga amakhulu ezinkampani zesofthiwe ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini emhlabeni jikelele.
Ingakanani iminyaka yobudala yezinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS?
Inkampani ejwayelekile ye-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS yasungulwa eminyakeni engu-3.0 eyedlule, kanti isilinganiso sonyaka wokusungulwa kwayo sasingu-2023.
Ingabe izinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS zisebenza kakhulu njenge-B2B noma i-B2C?
Ngokusekelwe kudatha etholakalayo, izinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS zihlukaniswa kanje: Kokubili i-B2B ne-B2C (17.3%), Ibhizinisi (B2B) (82.7%).
Yiziphi izinkampani ezintsha ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS?
Izinkampani ze-Ukuthunyelwa Kwemodeli Yokufunda Ngomshini SaaS ezisanda kutholakala zifaka phakathi i-Premforge, A3P Cloud, mantid, ModelArcs, FlexAI. I-SaaS Browser ithola futhi ibhale imikhiqizo emisha yesofthiwe njalo isebenzisa ingqalasizinda yokukhasa eyimfihlo.
Mayelana nesiphequluli se-SaaS
I-SaaS Browser iyisizindalwazi se-SaaS esikhulu kunazo zonke nesisesikhathini kakhulu ku-inthanethi - ithuluzi lokuhlola nokucwaninga eliphelele lezinkampani ze-SaaS.
Sithola amathuluzi amasha ngemva kwamasonto okwethulwa, ngaphambi kokuba avele ku-Apollo, ZoomInfo, noma kwezinye izizindalwazi. Ipulatifomu yethu eyakhelwe ngenhloso ilandelela kuphela imikhiqizo yesofthiwe yangempela enedatha eqinisekisiwe, ngokungafani namathuluzi okuhlola ajwayelekile angcoliswe yizinhlangano, izinhlangano zokubonisana, kanye nohlu oluphelelwe yisikhathi.
Kusukela emabhizinisini e-Fortune 500 kuya ezinkampanini ezintsha eziqaliswe kuleli sonto - uma kuyi-SaaS, siyayilandela.
Indlela lezi zinombolo ezibalwa ngayo
- Isofthiwe Ephelele. Inani lezinkampani ze-SaaS ezishicilelwe njengamanje ezisohlwini lalokhu okukhethiwe. Izinkampani eziphasa ukuhlola kwethu kwekhwalithi okuzenzakalelayo kuphela ezibalwayo.
- Okusha (Izinsuku ezingu-30 ezedlule). Inani eliyiqiniso lezinkampani ze-SaaS ezisanda kushicilelwa kulokhu okukhethiwe ezinsukwini ezingu-30 ezedlule.
- Iminyaka Emaphakathi (Iminyaka). Iminyaka ephakathi, ngeminyaka, yezinkampani kulokhu okukhethiwe, ngokusekelwe onyakeni wokusungulwa. Iphakathi liyinani eliphakathi, ngakho-ke uhhafu badala futhi uhhafu basebancane.
- Okusha (Izinsuku ezingu-365 ezedlule). Inani lezinkampani ze-SaaS ezishicilelwe kulokhu okukhethiwe ezinsukwini ezingu-365 ezedlule.
- I-Churned (Unyaka Odlule). Izinkampani ezishicilelwe phakathi nonyaka odlule ezingasekho ohlwini — zaya offline, zasuswa, noma zayeka ukudlula ekuhloleni kwethu kwekhwalithi.
- Izinga Lonyaka Lokukhushulwa. Izinga lokushona lonyaka — izinkampani ezishonile njengephesenti lazo zonke izinkampani ezishicilelwe onyakeni odlule (ezisesohlwini kanye nezishonile).
- Isivivinyo Samahhala. Iphesenti lezinkampani ezisohlwini kulokhu okukhethiwe ezinikeza ukulingwa kwamahhala.
- Ukusatshalaliswa Kosayizi Wenkampani. Ukusatshalaliswa kwezinkampani ngenani labasebenzi: oyedwa (1), encane (2–50), ephakathi (51–200), enkulu (201–1,000), ye-enthaphrayizi (1,000+). Amaphesenti angawezinkampani ezinedatha yabasebenzi.
- Isilinganiso Sezinga Lesizinda. Izinga lesizinda eliphakathi (0–100) kuzo zonke izinkampani ezisohlwini, isilinganiso sethu segunya lezixhumanisi esivela kugrafu yewebhu ye-Common Crawl. Eliphezulu liqine kakhulu. Liboniswa lingelinye nephakathi eliphelele kuzo zonke ze-SaaS ukuze kuqhathaniswe.
- Intengo Ephakathi Yokuqala (i-USD). Intengo yanyanga yokungena ephakathi (USD) kuzo zonke izinkampani kulokhu okukhethiwe ezinedatha yamanani.
- Intengo Ephakeme Emaphakathi (i-USD). Intengo yanyanga eliphakeme ephakathi (USD) kuzo zonke izinkampani kulokhu okukhethiwe ezinedatha yamanani.
- Isikolo Sokugcwalisa. Isikolo sokugcwala kwemakethe esingu-0–100 esihlanganisa imikhiqizo ephelele, igunya lesizinda, izizinda ezibhekisela, iminyaka yenkampani, ukukhula kwethrafikhi, amanani, kanye nokuba khona kwe-enthaphrayizi. Eliphezulu lisho ukuncintisana okukhulu.
- Ukuhlukaniswa kwe-B2B / B2C. Ukuhlukaniswa kwezinkampani ngokwemakethe okuqondiwe — ibhizinisi (B2B), umthengi (B2C), noma kokubili — phakathi kwalezo ezinedatha yohlobo lomthengi.
