Description
Access to data through APIs has become one of the key pieces of today's digital ecosystem. Public administrations, international organizations and private companies publish information so that third parties can reuse it in applications, analyses or artificial intelligence projects. In this situation, talking about open data is, almost inevitably, also talking about APIs.
However, access to an API is rarely completely free and unlimited. There are restrictions, controls and protection mechanisms that seek to balance two objectives that, at first glance, may seem opposite: facilitating access to data and guaranteeing the stability, security and sustainability of the service. These limitations generate frequent doubts: are they really necessary, do they go against the spirit of open data, and to what extent can they be applied without closing access?
This article discusses how these constraints are managed, why they are necessary, and how they fit – far from what is sometimes thought – within a coherent open data strategy.
Why you need to limit access to an API
An API is not simply a "faucet" of data. Behind it there is usually technological infrastructure, servers, update processes, operational costs and equipment responsible for the service working properly.
When a data service is exposed without any control, well-known problems appear:
- System saturation, caused by an excessive number of simultaneous queries.
- Abusive use, intentional or unintentional, that degrades the service for other users.
- Uncontrolled costs, especially when the infrastructure is deployed in the cloud.
- Security risks, such as automated attacks or mass scraping.
In many cases, the absence of limits does not lead to more openness, but to a progressive deterioration of the service itself.
For this reason, limiting access is not usually an ideological decision, but a practical necessity to ensure that the service is stable, predictable and fair for all users.
The API Key: basic but effective control
The most common mechanism for managing access is the API Key. While in some cases, such as the datos.gob.es National Open Data Catalog API , no key is required to access published information, other catalogs require a unique key that identifies each user or application and is included in each API call.
Although from the outside it may seem like a simple formality, the API Key fulfills several important functions. It allows you to identify who consumes the data, measure the actual use of the service, apply reasonable limits and act on problematic behavior without affecting other users.
In the Spanish context there are clear examples of open data platforms that work in this way. The State Meteorological Agency (AEMET), for example, offers open access to high-value meteorological data, but requires requesting a free API Key for automated queries. Access is free of charge, but not anonymous or uncontrolled.
So far, the approach is relatively familiar: consumer identification and basic limits of use. However, in many situations this is no longer enough.
When API becomes a strategic asset
Leading API management platforms, such as MuleSoft or Kong among others, were pioneers in implementing advanced mechanisms for controlling and protecting access to APIs. Its initial focus was on complex business environments, where multiple applications, organizations, and countries consume data services intensively and continuously.
Over time, many of these practices have also been extended to open data platforms. As certain open data services gain relevance and become key dependencies for applications, research, or business models, the challenges associated with their availability and stability become similar. The downfall or degradation of large-scale open data services—such as those related to Earth observation, climate, or science—can have a significant impact on multiple systems that depend on them.
In this sense, advanced access management is no longer an exclusively technical issue and becomes part of the very sustainability of a service that becomes strategic. It's not so much about who publishes the data, but the role that data plays within a broader ecosystem of reuse. For this reason, many open data platforms are progressively adopting mechanisms that have already been tested in other areas, adapting them to their principles of openness and public access. Some of them are detailed below.
Limiting the flow: regulating the pace, not the right of access
One of the first additional layers is the limitation of the flow of use, which is usually known as rate limiting. Instead of allowing an unlimited number of calls, it defines how many requests can be made in a given time interval.
The key here is not to prevent access, but to regulate the rhythm. A user can still use the data, but it prevents a single application from monopolizing resources. This approach is common in the Weather, Mobility, or Public Statistics APIs, where many users access it simultaneously.
More advanced platforms go a step further and apply dynamic limits, which are adjusted based on system load, time of day, or historical consumer behavior. The result is fairer and more flexible control.
Context, Origin, and Behavior: Beyond Volume
Another important evolution is to stop looking only at how many calls are made and start analyzing where and how they are made from. This includes measures such as restriction by IP addresses, geofencing, or differentiation between test and production environments.
In some cases, these limitations respond to regulatory frameworks or licenses of use. In others, they simply allow you to protect more sensitive parts of the service without shutting down general access. For example, an API can be globally accessible in query mode, but limit certain operations to very specific situations.
Platforms also analyze behavior patterns. If an application starts making repetitive, inconsistent queries or very different from its usual use, the system can react automatically: temporarily reduce the flow, launch alerts or require an additional level of validation. It is not blocked "just because", but because the behavior no longer fits with a reasonable use of the service.
Measuring impact, not just calls
A particularly relevant trend is to stop measuring only the number of requests and start considering the real impact of each one. Not all queries consume the same resources: some transfer large volumes of data or execute more expensive operations.
A clear example in open data would be an urban mobility API. Checking the status of a stop or traffic at a specific point involves little data and limited impact. On the other hand, downloading the entire vehicle position history of a city at once for several years is a much greater load on the system, even if it is done in a single call.
For this reason, many platforms introduce quotas based on the volume of data transferred, type of operation, or query weight. This avoids situations where seemingly moderate usage places a disproportionate load on the system.
How does all this fit in with open data?
At this point, the question inevitably arises: is data still open when all these layers of control exist?
The answer depends less on technology and more on the rules of the game. Open data is not defined by the total absence of technical control, but by principles such as non-discriminatory access, the absence of economic barriers, clarity in licensing, and the real possibility of reuse.
Requesting an API Key, limiting flow, or applying contextual controls does not contradict these principles if done in a transparent and equitable manner. In fact, in many cases it is the only way to guarantee that the service continues to exist and function correctly in the medium and long term.
The key is in balance: clear rules, free access, reasonable limits and mechanisms designed to protect the service, not to exclude. When this balance is achieved, control is no longer perceived as a barrier and becomes a natural part of an ecosystem of open, useful and sustainable data.
Content created by Juan Benavente, senior industrial engineer and expert in technologies related to the data economy. The content and views expressed in this publication are the sole responsibility of the author.
