[role="xpack"]
[[inference-apis]]
== {infer-cap} APIs

IMPORTANT: The {infer} APIs enable you to use certain services, such as built-in
{ml} models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure,
Google AI Studio or Hugging Face. For built-in models and models uploaded
through Eland, the {infer} APIs offer an alternative way to use and manage
trained models. However, if you do not plan to use the {infer} APIs to use these
models or if you want to use non-NLP models, use the
<<ml-df-trained-models-apis>>.

The {infer} APIs enable you to create {infer} endpoints and use {ml} models of
different providers - such as Amazon Bedrock, Anthropic, Azure AI Studio,
Cohere, Google AI, Mistral, OpenAI, or HuggingFace - as a service. Use
the following APIs to manage {infer} models and perform {infer}:

* <<delete-inference-api>>
* <<get-inference-api>>
* <<post-inference-api>>
* <<put-inference-api>>
* <<update-inference-api>>

[[inference-landscape]]
.A representation of the Elastic inference landscape
image::images/inference-landscape.jpg[A representation of the Elastic inference landscape,align="center"]

An {infer} endpoint enables you to use the corresponding {ml} model without
manual deployment and apply it to your data at ingestion time through
<<semantic-search-semantic-text, semantic text>>. 

Choose a model from your provider or use ELSER – a retrieval model trained by 
Elastic –, then create an {infer} endpoint by the <<put-inference-api>>.
Now use <<semantic-search-semantic-text, semantic text>> to perform
<<semantic-search, semantic search>> on your data.


[discrete]
[[default-enpoints]]
=== Default {infer} endpoints

Your {es} deployment contains some preconfigured {infer} endpoints that makes it easier for you to use them when defining `semantic_text` fields or {infer} processors.
The following list contains the default {infer} endpoints listed by `inference_id`:

* `.elser-2-elasticsearch`: uses the {ml-docs}/ml-nlp-elser.html[ELSER] built-in trained model for `sparse_embedding` tasks (recommended for English language texts)
* `.multilingual-e5-small-elasticsearch`: uses the {ml-docs}/ml-nlp-e5.html[E5] built-in trained model for `text_embedding` tasks (recommended for non-English language texts)

Use the `inference_id` of the endpoint in a <<semantic-text,`semantic_text`>> field definition or when creating an <<inference-processor,{infer} processor>>.
The API call will automatically download and deploy the model which might take a couple of minutes.
Default {infer} enpoints have {ml-docs}/ml-nlp-auto-scale.html#nlp-model-adaptive-allocations[adaptive allocations] enabled.
For these models, the minimum number of allocations is `0`. 
If there is no {infer} activity that uses the endpoint, the number of allocations will scale down to `0` automatically after 15 minutes.


include::delete-inference.asciidoc[]
include::get-inference.asciidoc[]
include::post-inference.asciidoc[]
include::put-inference.asciidoc[]
include::update-inference.asciidoc[]
include::service-alibabacloud-ai-search.asciidoc[]
include::service-amazon-bedrock.asciidoc[]
include::service-anthropic.asciidoc[]
include::service-azure-ai-studio.asciidoc[]
include::service-azure-openai.asciidoc[]
include::service-cohere.asciidoc[]
include::service-elasticsearch.asciidoc[]
include::service-elser.asciidoc[]
include::service-google-ai-studio.asciidoc[]
include::service-google-vertex-ai.asciidoc[]
include::service-hugging-face.asciidoc[]
include::service-mistral.asciidoc[]
include::service-openai.asciidoc[]
include::service-watsonx-ai.asciidoc[]