The process
The WebRTC tools guide is split into multiple categories. Each category represents an area where developers use specific tools. These can be TURN servers, media servers, full CPaaS services and even low level SDKs. Per category, I look at multiple aspects:
Which of the well known tools in the category is an AI service going to suggest when I ask for assistance.
When a decision is made to use a tool, what can AI figure out and find on its own about the tool's capabilities.
Where does the results above fit with my own mental model of the WebRTC world and the tools in it.
How do WebRTC tools get discovered today
A developer today doesn't open ten browser tabs to pick a tool. He isn't even going to Google Search to see what's available out there (when did you do that last?)
Developers ask an AI: "what should I use, and can you help me build it?"
That is the question I put to the machines, systematically, across three models: Claude Sonnet 4.6, Gemini 2.5 Flash, and GPT-4o. Same prompts, same time window, same lane for each category. Then I write down what came back and set it next to what I know the tools are actually worth.
In reality, multiple variants of the question are prompted, to see in which of the queries which of the tools show up. Interestingly, I've seen tools crossing category boundaries even when they shouldn't, trickling into queries where they aren't even a suitable fit.
All of the analysis took place in June 2026. This may be executed again in the future periodically, to see how the ecosystem changes and shifts over time.
What gets measured
Below are the certain aspects measured and covered in the WebRTC tools guide.
AI Visibility
0–10This is the machine side of the guide. AI Visibility (0 to 10) measures how often an AI model names a tool when you ask it for a recommendation in that category. I run the prompts across all three models and average the result.
It's presence-based: a mention counts. If a model names the tool, it gains a score. I am not yet weighting "strongly recommended" differently from "mentioned in passing". AI Visibility measures the models, not the vendor's product. A great tool can score 0 here simply because the models have never learned to say its name.
Quality
0–100The quality score in each category is different. Here, I mapped what are the types of features you'd expect in the category. What are the must-haves and the nice-to-haves. Then, I unleashed AI to go figure out for each of the tool based on its documentation and website the availability of these must-haves and nice-to-haves and suggest a quality score.
The selection of features can be seen as subjective (my decision), but the scoring against them was done by the objectivity of AI.
The Quality score (0 to 100) is a blend of documentation depth and feature coverage. Documentation depth is how findable and complete the docs are. Feature coverage is how much of the category's real work the tool actually does.
The feature set is per category. A media server gets scored on SFU behavior, recording, simulcast, mobile SDKs, observability and the rest. A TURN service gets scored on what TURN services have to do. So a 94 on one page and a 94 on another are both honest, but they were earned against different checklists. Read Quality within its own category.
Agent-ready
✓~ tieredSeparate from the scores, I record whether the vendor ships something an AI coding agent can actually operate: an MCP server, or a hand-authored llms.txt. This is tiered, not graded into the numbers. It tells you whether an agent building on top of the tool has a paved road or has to feel its way through human documentation or read code directly.
MCP servers are starting to show up in some categories. Plenty of strong products still ship nothing here, and that gap matters more every month.
Pricing
context onlyPricing is indicative context, gathered from each vendor's public pages. It is not a score and it is not a column I rank on. Pricing models differ across the guide - per-minute, per-MAU, per-GB, credits, build tiers - so the numbers are not always like-for-like, and I flag it where they aren't. Treat every price as an initial starting point only. Confirm current rates and limits with the vendor for your own volume before you lean on them.
The point of all this
Where AI and reality diverge
This is the whole point of the guide. Each tool sits at two measurements: how often AI names it, and how good AI thinks it is once it reads its documentation. The gap between the two is the interesting part.
This is not a leaderboard.
I never composite AI Visibility, Quality, Agent-ready and pricing into one number, because they answer different questions and collapsing them would hide the divergence that makes the guide worth reading. The grades on every page measure the machines, not my feelings about the vendors. My own call shows up only in the labelled right-most column and I kept that as sparse as possible on purpose - I am not the interesting part here.
I'm an independent WebRTC analyst. No vendor pays for placement, position, or a badge. Where I have a commercial interest - rtcStats is my own product - I say so on the page and pull the editorial calls altogether.
Ask the models the same questions yourself. You'll get the same names. I just do it systematically and write down what I find.
Oh - and if you need help figuring out in detail what I did, and even improve your own standing - you know where to find me.