Photo by author
# Introduction
You’ve probably heard people talk about APIs. Basically, an API allows a piece of software to ask another piece of software for help. For example, when we use our weather app, it can use a real-time API to get data from a remote server. This small talk saves you from having to do everything yourself. In this article, we’re going to look at five APIs that are actually fun and surprisingly easy to use. You will explore AI models, web data, search engines, model fine-tuning, and synthetic data. Each of these APIs opens up opportunities to learn, experiment, and build small projects without heavy setup. So, let’s begin.
# 1. Open router
When I was working on my research paper and needed to call several large language models, one of the biggest headaches for me was keeping track of all the different API keys. I really wanted to have access to all of them at once (quite the problem Open router solution). It’s a unified API gateway for major language models that gives you access to over 100 models from major providers such as OpenAI, Entropic, Google, Meta, Mustal, Coheir and many other open source alternatives. So, you only need one API key and one integration, and you can switch between models just by changing the parameter. It also handles smart provider routing, automatic fallback if a model is down, and routing based on cost, latency, or availability. Responses come in a standard format (text or image support), support streaming via SSE, and work out-of-the-box with all SDKs/clients compatible with OpenAIS (Python, JS, etc.). Pricing isn’t your minimum, starting at a fraction of a cent per token, and there’s a free tier for testing.
# 2. Allstep
I personally believe the two biggest challenges with using LLM are getting real-time data to make sure your information is up-to-date and converting it into a structured format that your model can actually use. And All-stop Solves them both. It is a web data API that allows you to scrape, crawl and search any publicly available website and get the results you want instantly. You can feed search results, news, or other online content directly into your application. Allostep also takes care of the data structure. It supports multiple endpoints, such as,/scrapes For individual URLs, /crawls To repeatedly follow links across a site, /batches Processing thousands of URLs in parallel, and /answers Allowing “Web of the Web” style questions where you get extracted answers (with sources) instead of raw HTML. The API also automatically handles JavaScript-rendered pages, proxies, and anti-bot mechanisms, making it reliable even for complex websites. Pricing starts at free (500 applications), with paid tiers ranging from $9/month (5K applications) to $399/month (1M applications), plus credit packs for flexibility.
# 3. Tinker API
Tinker API A new API from Thinking Machines Lab (launching October 2025) that aims to facilitate fine-tuning and custom training of open-weight large language models by giving you complete control over the training loop i.e. forward_backward, opt_step, sample, safe_state, etc. It supports variations of popular base models such as LAMA, Mistral, and GPT, with data-centric endpoints such as instant LORA/QLORA fine-tunes, multi-agent simulation, and artificial enhancement or bias mitigation. It also includes a sandbox-like interface for prototyping in minutes. Tinker is currently in private beta with a free tier for small experiments (like <1b params), and it's already being used by research groups at universities like Princeton, Stanford, and UC Berkeley. Measuring hourly models for each company starting at US$0.50/hour for mid-range GPUs.
# 4. Sarappi
Sirappi is a real-time web search API that makes it easy to get structured search results from Google and other search engines. It can take organic results, news, images, shopping lists, maps, and knowledge graph books, and deliver them in plain JSON (or optionally raw HTML). The API handles the complex parts for you, including solving captchas, serving JavaScript, managing proxies, and simulating real user behavior, so you get accurate and up-to-date results. You can control many parameters, including search query, language, location, device type, search type, pagination, and output format. This makes it easy to fix the data you receive. Pricing starts with a free tier that offers 250 searches per month. Paid plans include Developer at US$75 for 5,000 searches, Product at US$150 for 15,000 searches, and Big Data at US$275 for 30,000 searches. All plans are month-to-month, with 99.95% uptime for payment options and custom high-volume plans available.
# 5. Most AI generator API
Most of the AI ​​generator API Helps you create realistic, privacy-protected data from your real datasets. You start by training the generator on your tables, CSVs, or database. The generator learns patterns, correlations and relationships in your data while preserving private information. After training, you can create as many new records as you need using the API or Python SDK. It works with many types of data, including numbers, categories, text, time series, geographic location, and multi-table datasets. You can use conditional sampling, a proportional distribution, or imputing missing values. The platform provides detailed reports so you can see how closely the simulated data matches the original, including distribution and correlation. You can use this data to securely share across teams, test machine learning models, or run experiments where using real data is risky. It gives you actionable, flexible data you can rely on for analytics, AI training, or research without exposing sensitive information.
# wrap up
These five APIs show how much you can do without building everything from scratch. OpenRouter makes it easy to work with multiple LLMs with one API key. Allstep gives you live web data and converts it into a structured format that your models can use. Tinker lets you fine-tune and experiment with LLM without complicated setup. SERPAPI makes real-time search easy and reliable, and most AI generator APIs help you generate realistic, privacy-protected data for testing and experimentation. Each is powerful, but also beginner-friendly to try early.
Which APIs do you like the most? Have you tried any of them, or do you use others? Share your favorites in the comments below. I’d love to see what you’re working with 🙂
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI with medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT.” As a 2022 Google Generation Scholar for APAC, she champions diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a MITACS GlobalLink Research Scholar, and a Harvard Wicked Scholar. Kanwal is a passionate advocate for change, having founded the Fame Code to empower women in stem fields.