diff --git a/README.md b/README.md
index 2da4c17c..28e0e735 100644
--- a/README.md
+++ b/README.md
@@ -23,14 +23,14 @@ We have 4 public models available:
 3) UMLS Dutch v1.10 (a modelpack provided by UMC Utrecht containing [UMLS entities with Dutch names](https://github.com/umcu/dutch-umls) trained on Dutch medical wikipedia articles and a negation detection model [repository](https://github.com/umcu/negation-detection/)/[paper](https://doi.org/10.48550/arxiv.2209.00470) trained on EMC Dutch Clinical Corpus).
 4) UMLS Full. >4MM concepts trained self-supervised on MIMIC-III. v2022AA of UMLS.
 
-To download any of these models, please [follow this link](https://uts.nlm.nih.gov/uts/login?service=https://medcat.rosalind.kcl.ac.uk/auth-callback) and sign into your NIH profile / UMLS license. You will then be redirected to the MedCAT model download form. Please complete this form and you will be provided a download link.
+To download any of these models, please [follow this link](https://uts.nlm.nih.gov/uts/login?service=https://medcat.sites.er.kcl.ac.uk/auth-callback) (or [this link for API key based download](https://medcat.sites.er.kcl.ac.uk/auth-callback-api)) and sign into your NIH profile / UMLS license. You will then be redirected to the MedCAT model download form. Please complete this form and you will be provided a download link.
 
 ## News
 - **Paper** van Es, B., Reteig, L.C., Tan, S.C. et al. [Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods](https://doi.org/10.1186/s12859-022-05130-x). BMC Bioinformatics 24, 10 (2023). 
 - **New tool in the Cogstack ecosystem \[19. December 2022\]** [Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records](https://arxiv.org/abs/2212.08072)
 - **New Paper using MedCAT \[21. October 2022\]**: [A New Public Corpus for Clinical Section Identification: MedSecId.](https://aclanthology.org/2022.coling-1.326.pdf)
 - **Major Change to the Permissions of Use \[4. August 2022\]** MedCAT now uses the [Elastic License 2.0](https://github.com/CogStack/MedCAT/pull/271/commits/c9f4e86116ec751a97c618c97dadaa23e1feb6bc). For further information please click [here.](https://www.elastic.co/licensing/elastic-license)
-- **New Downloader \[15. March 2022\]**: You can now [download](https://uts.nlm.nih.gov/uts/login?service=https://medcat.rosalind.kcl.ac.uk/auth-callback) the latest SNOMED-CT and UMLS model packs via UMLS user authentication.
+- **New Downloader \[15. March 2022\]**: You can now [download](https://uts.nlm.nih.gov/uts/login?service=https://medcat.sites.er.kcl.ac.uk/auth-callback) (or [API key based download](https://medcat.sites.er.kcl.ac.uk/auth-callback-api)) the latest SNOMED-CT and UMLS model packs via UMLS user authentication.
 - **New Feature and Tutorial \[7. December 2021\]**: [Exploring Electronic Health Records with MedCAT and Neo4j](https://towardsdatascience.com/exploring-electronic-health-records-with-medcat-and-neo4j-f376c03d8eef)
 - **New Minor Release \[20. October 2021\]** Introducing model packs, new faster multiprocessing for large datasets (100M+ documents) and improved MetaCAT.
 - **New Release \[1. August 2021\]**: Upgraded MedCAT to use spaCy v3, new scispaCy models have to be downloaded - all old CDBs (compatble with MedCAT v1) will work without any changes.
@@ -54,7 +54,7 @@ To install the latest version of MedCAT without torch GPU support run the follow
 pip install medcat --extra-index-url https://download.pytorch.org/whl/cpu/
 ```
 ## Demo
-A demo application is available at [MedCAT](https://medcat.rosalind.kcl.ac.uk). This was trained on MIMIC-III and all of SNOMED-CT.
+A demo application is available at [MedCAT](https://medcat.sites.er.kcl.ac.uk). This was trained on MIMIC-III and all of SNOMED-CT.
 PS: This link can take a long time to load the first time around. The machine spins up as needed and spins down when inactive.
 
 ## Tutorials
diff --git a/docs/main.md b/docs/main.md
index b8796487..c4bd6dbd 100644
--- a/docs/main.md
+++ b/docs/main.md
@@ -11,12 +11,12 @@ MedCAT can be used to extract information from Electronic Health Records (EHRs)
 
 **Discussion Forum [here](https://discourse.cogstack.org/)**
 
-**Available Models (requires UMLS license) [here](https://uts.nlm.nih.gov/uts/login?service=https://medcat.rosalind.kcl.ac.uk/auth-callback)**
+**Available Models (requires UMLS license) [here](https://uts.nlm.nih.gov/uts/login?service=https://medcat.sites.er.kcl.ac.uk/auth-callback) (or [this link for API key based download](https://medcat.sites.er.kcl.ac.uk/auth-callback-api))**
 
 ## News
 - **Paper** [A New Public Corpus for Clinical Section Identification: MedSecId](https://aclanthology.org/2022.coling-1.326.pdf)
 - **New Release** \[5. October 2022\]**: Logging changes, and various small updates. [Full changelog](https://github.com/CogStack/MedCAT/compare/v1.3.0...v1.4.0)
-- **New Downloader \[15. March 2022\]**: You can now [download](https://uts.nlm.nih.gov/uts/login?service=https://medcat.rosalind.kcl.ac.uk/auth-callback) the latest SNOMED-CT and UMLS model packs via UMLS user authentication.
+- **New Downloader \[15. March 2022\]**: You can now [download](https://uts.nlm.nih.gov/uts/login?service=https://medcat.sites.er.kcl.ac.uk/auth-callback) (or [API key download](https://medcat.sites.er.kcl.ac.uk/auth-callback-api)) the latest SNOMED-CT and UMLS model packs via UMLS user authentication.
 - **New Feature and Tutorial \[7. December 2021\]**: [Exploring Electronic Health Records with MedCAT and Neo4j](https://towardsdatascience.com/exploring-electronic-health-records-with-medcat-and-neo4j-f376c03d8eef)
 - **New Minor Release \[20. October 2021\]** Introducing model packs, new faster multiprocessing for large datasets (100M+ documents) and improved MetaCAT.
 - **New Release \[1. August 2021\]**: Upgraded MedCAT to use spaCy v3, new scispaCy models have to be downloaded - all old CDBs (compatble with MedCAT v1) will work without any changes.
@@ -27,7 +27,7 @@ MedCAT can be used to extract information from Electronic Health Records (EHRs)
   (with respect to potential bug fixes), after it will still be available but not updated anymore.
 
 ## Demo
-A demo application is available at [MedCAT](https://medcat.rosalind.kcl.ac.uk). This was trained on MIMIC-III and all of SNOMED-CT.
+A demo application is available at [MedCAT](https://medcat.sites.er.kcl.ac.uk). This was trained on MIMIC-III and all of SNOMED-CT.
 
 ## Tutorials
 A guide on how to use MedCAT is available at [MedCAT Tutorials](https://github.com/CogStack/MedCATtutorials). Read more about MedCAT on [Towards Data Science](https://towardsdatascience.com/medcat-introduction-analyzing-electronic-health-records-e1c420afa13a).
@@ -116,7 +116,7 @@ python medcat/utils/model_creator.py tests/model_creator/config_example.yml
 
 ## Models
 ### SNOMED-CT and UMLS
-If you have access to UMLS or SNOMED-CT, you can download the pre-built CDB and Vocab for those databases by signing in and filling out [the online form](https://uts.nlm.nih.gov/uts/login?service=https://medcat.rosalind.kcl.ac.uk/auth-callback). This link first requires you to authenticate your ontology access via the NIH portal.
+If you have access to UMLS or SNOMED-CT, you can download the pre-built CDB and Vocab for those databases by signing in and filling out [the online form](https://uts.nlm.nih.gov/uts/login?service=https://medcat.sites.er.kcl.ac.uk/auth-callback) (or [this link for API key based download](https://medcat.sites.er.kcl.ac.uk/auth-callback-api)). This link first requires you to authenticate your ontology access via the NIH portal.
 
 ### MedMentions
 A basic trained model is made public. It contains ~ 35K concepts available in `MedMentions`. This was compiled from MedMentions and does not have any data from [NLM](https://www.nlm.nih.gov/research/umls/) as that data is not publicaly available.