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LinkedIn API pricing: what you should know

Intro
All developers who deal with LinkedIn API have two groups of questions of great importance. The first one is how to get access to this API and use it effectively. You can find responses to these issues in our LinkedIn API tutorial. The second one is how much it will cost. In this article, you can find a detailed guide for calculating LinkedIn API costs with practical tips, as well as restrictions for using it.

LinkedIn API price: the main features and calculation procedure

To begin with, we should admit that Data365 offers a simple pricing model without any complex subscriptions. The cost of LinkedIn API depends on the amount of LinkedIn data you would like to obtain.

The base price for our LinkedIn API is €300. This plan includes 500k mentions per month. As the overall utilization of mention increases, the cost rises too.

You should pay for the next month monthly. Every month we issue an invoice to the user taking into account the selected tariff plan. You are free to use a bank transfer or credit card for your payment.
But what does Mention mean?
Let's take a detailed look at this indicator.
Mention is a term used for accounting for the requested data. You can check more detailed information about Mention and its meaning for each data type on the API mentions quota page. Here we also give a couple of counting examples.
First example: let's say you requested to download a data of LinkedIn members and 100 posts (including activities) with comments:
Request cost will be approximately 9 + 100 * 5 = 509 mentions
9 is the cost of member data
100 is the number of posts
5 is the cost of a post with 50 comments (1 mention per 10 comments)
If the LinkedIn member had only 40 posts and no comments, then:
The estimated cost of the request will be 9 + 40 * 1 = 49 mentions
9 is the cost of member data
40 is the number of posts
1 is the cost of a post without comments
Second example: suppose you asked to find 80 posts with comments, then:
The approximate cost of the request will be 7 + 80 * 7 = 567 mentions
7 charged per search query
80 is the number of posts
7 – the cost of a post with 70 comments
If only 50 posts have been found, 20 of which had no comments, and 30 posts had 60 comments per each, then:
The estimated cost of the request will be 7 + 20 * 1 + 30 * 6 = 207 mentions
7 charged per search query
20 * 1 is the cost of 20 posts without comments
30 * 6 – the cost of 30 posts with 60 comments
You can also use our cost calculator for approximate mentions computing. Just enter which data you would like to receive and in what volume. As a result, you will get the total mentions value.

LinkedIn API limits: what are they?

Rate limiting is a crucial component of API scalability and Internet security. To provide our APIs with efficiently as possible, we enforce a limit on the number of requests for users and the quantity of data they can consume.

Data365 LinkedIn API limit on the amount of data in 1 request is:
Data type
Limits
Posts per profile
up to 500
Posts per search
up to 300*
Comments per post**
500+
* a number of posts returned by the web version of LinkedIn. You can increase this number by configuring the auto-monitoring function and sending requests with various parameters (for example, date_posted - filtering by publication date).
** usually you can get all the comments on a post, except for highly activity posts.
But you should also keep in mind our LinkedIn API rate limit: you can get up to 100 such requests per second. The time of response depends on the amount of data in the request and usually is within 1-5 minutes.

So, LinkedIn API restrictions are needed to increase security, business impact, and efficiency.
We can conclude that with the Data365 LinkedIn API, you can choose a price policy that fits your needs the best. And our user-friendly guide will help you to calculate applying LinkedIn API costs.

In our LinkedIn API documentation, we also provide recommendations for setting limits on certain types of data (for example, comments on posts) to optimize queries and improve the performance of the data retrieval process.