The large model API platform provides interfaces for text, inference, multimodality, and tool calls, serving as a fundamental service for developing AI products. Pages compare model lineages, context, structured outputs, function calls, rate limits, caching, batch processing, regional compliance, and pricing, helping teams control vendors and operational risks.
Scrapingdog is a web scraping and search data API for developers, data collection teams, and businesses that need search results or map data when calling APIs such as Google SERPs, AI Overview, Maps, News, and more to extract structured data. It focuses on encapsulating proxies, headless browsers, and anti-crawling processing into a unified API, with key capabilities including web scraping APIs, including Google SERPs, AI Mode, AI Overview, and Maps APIs, and support for structured JSON data extraction. It's better suited for teams with clear budget and process needs. Before use, it should be noted that the frequency of data collection should be controlled, the platform rules should be followed, and the scope of service use should be confirmed. If you plan to adopt it for a long time, it is recommended to test input lead time, output availability, manual review costs, and permission boundaries with real samples before deciding whether to put it into a fixed process.
ScrapeGraphAI is a web data scraping API for the AI era for developers, data teams, and product teams that need structured web data to extract structured data from websites, reducing agents, selectors, and maintenance efforts. It focuses on streamlining the web scraping process into a callable, maintainable data interface, with key capabilities such as providing ScrapeGraphAI V2, eliminating the need for proxies and selectors, API documentation, and startup resources. It's better suited for teams with clear budget and process needs. Note Before Use: Adhere to the target website's terms, robots rules, and data usage authorization before scraping. If you plan to adopt it for a long time, it is recommended to test input lead time, output availability, manual review costs, and permission boundaries with real samples before deciding whether to put it into a fixed process.
Runware is a multi-model generative AI API platform for developers, AI product teams, and enterprises that need unified access to multiple types of generative models when calling images, videos, audio, LLMs, 3D, and more through a single API. It focuses on integrating multi-model capabilities into products in a low-cost, on-demand scaling manner, including support for image, video, audio, LLM, and 3D, providing free test credits, and emphasizing low cost and instant scale. It offers free entry or trial credits, which are suitable for verifying results with small tasks first. Note before use: The output quality, copyright boundaries, and fee structure of different models need to be tested by product scenario. If you plan to adopt it for a long time, it is recommended to test input lead time, output availability, manual review costs, and permission boundaries with real samples before deciding whether to put it into a fixed process.
Rube is an AI-oriented, cross-app action tool for developers, automation teams, and users who want to connect AI to everyday tools when it comes to letting AI perform actions through apps like Gmail, Slack, Calendar, social media, and more. It focuses on connecting application certification, tool selection, and cross-app actions to AI use, with key capabilities including being launched by Composio, supporting over 600 apps, and offering Marketplace, Recipes, and Pricing. It offers free entry or trial credits, which are suitable for verifying results with small tasks first. Before use, you should set permissions, confirmation steps, and audit records before performing actions such as email, calendar, and social posting. If you plan to adopt it for a long time, it is recommended to test input lead time, output availability, manual review costs, and permission boundaries with real samples before deciding whether to put it into a fixed process.
Raindrop AI is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
Ragie is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
PredictOPs is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
Predictive Equations is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
Plat.AI is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
Pinecone is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
Phare is an AI workflow tool for teams that need to create, organize, convert, or review task-specific material before final use. It should be used with clear source material, a defined output goal, and human review for accuracy, rights, privacy, and publishing quality.
Pangeanic is an AI data annotation, model evaluation and multilingual data platform. It is mainly used to provide multilingual training data, model alignment, LLM evaluation, data annotation management and enterprise-level AI deployment support. It is suitable for AI teams, government agencies, enterprise R & D, language technology teams and organizations that require multilingual data operations. Common uses include building multilingual model training data, evaluating LLM output quality and security, and deploying linguistic AI systems for enterprises or governments. When using it, it should be noted that data operation projects usually require clear labeling specifications, privacy processing and quality sampling mechanisms, and cannot rely solely on automated processes. For corporate and project-based procurement, it is usually necessary to contact the team to confirm the scope and quotation. It is recommended to use one or two low-risk tasks to test input materials, output quality, manual modification amount and final adoption ratio before deciding whether to put them into a fixed process.
Novita AI is an AI model API and proxy cloud platform that is mainly used to access multiple models, proxy sandboxes and GPU resources through one API. It is suitable for developers, entrepreneurial teams, AI application teams and platform engineers. It can provide multi-model API access, launch agent sandboxes and GPU instances, and is also suitable for quickly building AI applications and prototypes. Pay attention when using it, and evaluate model costs, current limiting, data security and fault coverage before going online. It is recommended that one or two low-risk tasks be used to test input materials, output quality, manual modifications, and final adoption ratios before deciding whether to put them into a fixed process and document whether they are suitable for long-term use and team review.
LlamaIndex is an AI data framework and document parsing platform developed for AI Agents and knowledge assistants, providing document OCR, parsing, data access, and workflow capabilities to help developers connect unstructured data to large model applications. It's suitable for AI engineers, data teams, enterprise knowledge base projects, and development teams that need to work with complex documents. Before use, it is recommended to conduct small-scale testing with real materials or real processes, focusing on observing output quality, review costs, payment boundaries, data permissions, and whether the team can establish a stable manual review process. Before handling formal business, it should also be judged based on material authorization, privacy requirements, and manual review standards, and avoid using automatic results directly for external release or key decisions. If used in team, client, or teaching scenarios, the source of information, the responsibility for reviewing the results, and the scope of external use should also be clearly entered first.
Latitude is an observability platform for AI Agents that helps teams monitor, evaluate, and improve agent performance, and provides support around prompting, evaluation runs, and engineering processes. It's suitable for AI engineering teams, product teams, platform teams, and organizations that need to maintain agents for a long time. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process. Before handling formal business, it should also be judged based on material authorization, privacy requirements, and manual review standards, and avoid using automatic results directly for external release or key decisions. If you are using it for a team, client, or teaching scenario, it is recommended to first confirm the source of the input material, the responsibility for reviewing the results, and the scope of external use.
LangWatch is a test, evaluation, and observability platform for AI Agent and LLM applications, supporting simulated user testing, regression protection, debugging, monitoring, and evaluation. It's suitable for AI engineering teams, product teams, platform teams, and organizations that need to continuously verify agent quality. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process. Before handling formal business, it should also be judged based on material authorization, privacy requirements, and manual review standards, and avoid using automatic results directly for external release or key decisions. If you are using it for a team, client, or teaching scenario, it is recommended to first confirm the source of the input material, the responsibility for reviewing the results, and the scope of external use.
Langtail is a prompt management platform for product teams that helps teams build, test, and deploy AI prompts, with LLM testing, logging, AI firewalls, and prompt collaboration capabilities. It's suitable for AI product managers, developers, LLM application teams, and those who need to manage prompt versions. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process. Before handling formal business, it should also be judged based on material authorization, privacy requirements, and manual review standards, and avoid using automatic results directly for external release or key decisions. If you are using it for a team, client, or teaching scenario, it is recommended to first confirm the source of the input material, the responsibility for reviewing the results, and the scope of external use.
Landing AI is a vision AI platform that helps teams build AI-powered applications focused on creating, deploying, and managing computer vision models. It is suitable for manufacturing, quality inspection, industrial vision, retail inspection, and teams that need to implement image recognition capabilities into business processes. The platform offers a monthly points plan. Before use, you should prepare the annotation data, validation set, deployment environment, and service acceptance indicators, and check the model for false positives, missed checks, and data deviations in real-world scenarios. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process. Before handling formal business, it should also be judged by team processes, material authorization, and manual review criteria to avoid using automated results directly for external release or key decisions.
LAION is a Large-scale Artificial Intelligence Open Network, a non-profit open AI organization dedicated to making machine learning resources, including datasets, tools, and model-related projects, available to the public. It is suitable for researchers, machine learning engineers, open-source communities, educational institutions, and those who need to learn about open data resources. LAION itself is not a single application, but an open source network. Before using their data or models, they should confirm compliance with licenses, data sources, risk of bias, ethical restrictions, and downstream tasks. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process.
Label Studio is an open-source data annotation and AI evaluation platform that supports a wide range of data types, including computer vision, document AI, NLP, audio transcription, agent trajectory, LLM evaluation, RLHF, and more. It's suitable for machine learning teams, data annotation teams, researchers, and businesses that need to build training or evaluation datasets. The platform offers open-source versions and commercial solutions. When using it, you should design annotation specifications, quality inspection processes, permission management, and data compliance policies to avoid low-quality annotations affecting model performance. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process. Before handling formal business, it should also be judged by team processes, material authorization, and manual review criteria to avoid using automated results directly for external release or key decisions.
Klu is a development platform for LLM application teams, covering prompt design, application deployment, data integration, evaluation, collaborative prompt engineering, and model fine-tuning. It is suitable for AI product teams, developers, machine learning teams, and internal automation projects to advance large language model applications from experimentation to monitorable, evaluative production environments. The platform offers paid plans. Before use, you should confirm the data source, model selection, evaluation metrics, privacy boundaries, and team collaboration process to avoid judging the effect based on a single presentation. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process.
Kie AI is an AI API platform for developers and product teams, providing access capabilities to models such as chat, images, videos, and music, with an emphasis on free API keys, stable performance, scalable calls, and real-time stream output. It's suitable for AI application development, content generation products, multimodal feature integration, and teams that require a unified model entrance. Before accessing, you need to evaluate the response quality, latency, quota, cost, content security policy, generation material authorization, and failure retry mechanism to avoid directly connecting interface capabilities to formal services. Before use, it is recommended to conduct a small-scale test with real materials, focusing on observing the output quality, review cost, payment boundaries, data permissions, and whether the team can establish a stable manual review process.
Kapsul is a data infrastructure platform that provides Storage as a Software with a focus on programmable storage, adaptive privacy, and managing data by context. It is suitable for developers, AI application teams, and products that require fine-grained control over data storage, permissions, and costs to build a more flexible data layer. The platform offers free API requests and per-GB billing plans. Evaluate data types, permission models, encryption requirements, latency, cost, and compliance responsibilities before onboarding. It's more suitable for users with clear goals, input materials, and boundaries, and small-scale testing can help you determine whether the results are worth going into the formal process faster. Before use, you should also use your own data sources, team processes, and review criteria to avoid direct automatic results into official release, submission, or business decisions.
The Jobo API is a job data infrastructure for recruiting, job searching, and career platforms, offering capabilities related to extensive job listings, ATS platform coverage, data synchronization, and automated applications. It is suitable for recruiting technology products, job search tools, career service platforms, and internal talent systems to access job data, sync to business systems, and build job search related functions. The current page is more about API and platform services than individual job seeker tools. Before accessing, data authorization, application automation boundaries, platform terms, and user privacy need to be evaluated. If you want to include it in a long-term process, it is recommended to use a small task to verify the output quality, quota consumption, authorization boundaries, and manual modification costs before deciding whether to expand the scope of use.