AI 개발을위한 12 개의 무료 API

오늘날의 빠르게 진화하는 디지털 세계에서 인공 지능 (AI)을 사용할 수 있다는 것은 생존에 필수적이되고 있습니다. 비즈니스는 이제 큰 언어 모델 또는 LLM의 도움으로 고객 관계를 개선하고 프로세스를 최적화하며 혁신을 촉진 할 수 있습니다. 그러나 많은 돈이나 경험 없이이 잠재력을 어떻게 실현할 수 있습니까? LLM API는 최첨단 AI 기능을 앱에 부드럽게 통합하는 열쇠입니다.

LLM API 덕분에 처음부터 복잡한 모델을 만들지 않고도 자연 언어 처리 (NLP)와 이해력을 사용할 수 있습니다. LLM API는 소프트웨어와 인공 지능의 어려운 영역 사이의 중개자 역할을합니다. LLM API는 지능형 코딩 어시스턴트를 만들거나 고객 서비스 챗봇을 개선하려는 경우 성공하는 데 필요한 리소스를 제공합니다.

LLM API 이해

LLM API는 간단한 요청-응답 모델에서 작동합니다.

1. 제출 요청 : 응용 프로그램은 JSON에서 형식으로 모델 변형, 프롬프트 및 매개 변수를 포함하는 API에 요청을 보냅니다.
2. 처리 : API는이 요청을 LLM으로 전달하여 NLP 기능을 사용하여 처리합니다.
3. 응답 전달 : LLM은 응답을 생성하며 API가 응용 프로그램으로 다시 보냅니다.

가격 및 토큰

• 토큰 : LLM의 맥락에서 토큰은 모델에서 처리하는 가장 작은 텍스트 단위입니다. 가격은 일반적으로 사용 된 토큰 수를 기준으로하며 입력 및 출력 토큰에 대한 별도의 요금이 있습니다.
• 비용 관리 : 대부분의 제공 업체는 비용 지불 가격을 제공하므로 기업은 사용 패턴에 따라 비용을 효과적으로 관리 할 수 ​​있습니다.

LLMS 리소스 용 무료 API

비용이 들지 않고 시작하는 데 도움을주기 위해 설명, 장점, 가격 및 토큰 제한과 함께 LLM-Free API 제공 업체의 포괄적 인 목록이 있습니다.

1. OpenRouter – 무료 API

OpenRouter는 다양한 작업을위한 다양한 LLM을 제공하므로 개발자에게 다양한 선택이됩니다. 이 플랫폼은 분당 최대 20 개의 요청과 하루에 200 개의 요청을 허용합니다.

사용 가능한 주목할만한 모델 중 일부는 다음과 같습니다.

• Deepseek R1
• Llama 3.3 70b 지시
• Mistral 7b 지시

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 높은 요청 제한.
• 다양한 모델.

가격 : 무료 계층 사용 가능.

예제 코드

from openai import OpenAI
client = OpenAI(
 base_url="
 api_key="",
)
completion = client.chat.completions.create(
 model="cognitivecomputations/dolphin3.0-r1-mistral-24b:free",
 messages=[
   {
     "role": "user",
     "content": "What is the meaning of life?"
   }
 ]
)
print(completion.choices[0].message.content)

산출

The meaning of life is a profound and multifaceted question explored through
diverse lenses of philosophy, religion, science, and personal experience.
Here's a synthesis of key perspectives:
1. **Existentialism**: Philosophers like Sartre argue life has no inherent
meaning. Instead, individuals create their own purpose through actions and
choices, embracing freedom and responsibility.
2. **Religion/Spirituality**: Many traditions offer frameworks where meaning
is found through faith, divine connection, or service to a higher cause. For
example, in Christianity, it might relate to fulfilling God's will.
3. **Psychology/Philosophy**: Viktor Frankl proposed finding meaning through
work, love, and overcoming suffering. Others suggest meaning derives from
personal growth, relationships, and contributing to something meaningful.
4. **Science**: While natural selection emphasizes survival, many see life's
meaning in consciousness, creativity, or bonds formed with others,
transcending mere biological imperatives.
5. **Art/Culture**: Through art, music, or literature, individuals express
their search for meaning, often finding it in beauty, expression, or
collective storytelling.
**Conclusion**: Ultimately, the meaning of life is subjective. It emerges
from the interplay of experiences, beliefs, and personal choices. Whether
through love, contribution, spirituality, or self-discovery, it is a journey
where individuals define their own purpose. This diversity highlights the
richness and mystery of existence, inviting each person to explore and craft
their own answer.

2. Google AI 스튜디오 – 무료 API

Google AI Studio는 AI 모델 실험을위한 강력한 플랫폼으로 개발자에게 관대 한 제한을 제공합니다. 그것은 허용합니다 분당 1,000,000 개의 토큰과 하루에 1,500 개의 요청.

사용 가능한 일부 모델에는 다음이 포함됩니다.

• Gemini 2.0 플래시
• Gemini 1.5 플래시

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 강력한 모델에 대한 액세스.
• 높은 토큰 제한.

가격 : 무료 계층 사용 가능.

예제 코드

from google import genai
client = genai.Client(api_key="YOUR_API_KEY")
response = client.models.generate_content(
   model="gemini-2.0-flash",
   contents="Explain how AI works",
)
print(response.text)

산출

/usr/local/lib/python3.11/dist-packages/pydantic/_internal/_generate_schema.py:502: UserWarning:  function any> is not a Python type (it may be an instance of an object),
Pydantic will allow any object with no validation since we cannot even
enforce that the input is an instance of the given type. To get rid of this
error wrap the type with `pydantic.SkipValidation`.
  warn(
Okay, let's break down how AI works, from the high-level concepts to some of
the core techniques.  It's a vast field, so I'll try to provide a clear and
accessible overview.
**What is AI, Really?**
At its core, Artificial Intelligence (AI) aims to create machines or systems
that can perform tasks that typically require human intelligence.  This
includes things like:
*   **Learning:** Acquiring information and rules for using the information
*   **Reasoning:** Using information to draw conclusions, make predictions,
and solve problems.
*   **Problem-solving:**  Finding solutions to complex situations.
*   **Perception:**  Interpreting sensory data (like images, sound, or text).
*   **Natural Language Processing (NLP):**  Understanding and generating
human language.
*   **Planning:** Creating sequences of actions to achieve a goal.
**The Key Approaches & Techniques**
AI isn't a single technology, but rather a collection of different approaches
and techniques. Here are some of the most important:
1.  **Machine Learning (ML):**
    *   **The Foundation:** ML is the most prominent approach to AI today.
Instead of explicitly programming a machine to perform a task, you *train*
it on data.  The machine learns patterns from the data and uses those
patterns to make predictions or decisions on new, unseen data.
    *   **How it works:**
        *   **Data Collection:**  Gather a large dataset relevant to the task
you want the AI to perform.  For example, if you want to build an AI to
recognize cats in images, you need a dataset of many images of cats (and
ideally, images that aren't cats).
        *   **Model Selection:**  Choose a suitable ML model.  Different
models are good for different types of problems. Examples include:
            *   **Linear Regression:** For predicting continuous values
(e.g., house prices).
            *   **Logistic Regression:** For predicting categorical values
(e.g., spam/not spam).
            *   **Decision Trees:** For making decisions based on a tree-like
structure.
            *   **Support Vector Machines (SVMs):**  For classification
tasks, finding the best boundary between classes.
            *   **Neural Networks:**  Inspired by the structure of the human
brain, excellent for complex tasks like image recognition, natural language
processing, and more.
        *   **Training:** Feed the data into the chosen model.  The model
adjusts its internal parameters (weights, biases, etc.) to minimize errors
and improve its ability to make accurate predictions. This process involves:
            *   **Forward Propagation:** The input data is passed through the
model to generate a prediction.
            *   **Loss Function:**  A loss function calculates the difference
between the model's prediction and the actual correct answer.  The goal is
to minimize this loss.
            *   **Backpropagation:** The model uses the loss to adjust its
internal parameters (weights and biases) to improve its predictions in the
future.  This is how the model "learns."
            *   **Optimization:** Algorithms (like gradient descent) are used
to find the parameter values that minimize the loss function.
        *   **Evaluation:**  After training, you evaluate the model on a
separate dataset (the "test set") to see how well it generalizes to unseen
data.  This helps you determine if the model is accurate enough and if it's
overfitting (performing well on the training data but poorly on new data).
        *   **Deployment:** If the model performs well, it can be deployed to
make predictions on real-world data.
    *   **Types of Machine Learning:**
        *   **Supervised Learning:** The model is trained on labeled data
(data where the correct answer is already known).  Examples: classification 
(categorizing data) and regression (predicting continuous values).
        *   **Unsupervised Learning:** The model is trained on unlabeled
data.  It tries to find patterns and structures in the data on its own.
  Examples: clustering (grouping similar data points together) and
dimensionality reduction (simplifying data while preserving important
information).
        *   **Reinforcement Learning:** The model learns by interacting with
an environment and receiving rewards or penalties for its actions. It aims
to learn a policy that maximizes its cumulative reward.  Examples: training
AI agents to play games or control robots.
2.  **Deep Learning:**
    *   **A Subfield of ML:** Deep learning is a type of machine learning
that uses artificial neural networks with many layers (hence "deep"). These
deep networks are capable of learning very complex patterns.
    *   **Neural Networks:**  Neural networks are composed of interconnected
nodes (neurons) organized in layers. Each connection has a weight associated
with it, which determines the strength of the connection.  The network
learns by adjusting these weights.
    *   **How it works:** Deep learning models are trained in a similar way
to other ML models, but they require significantly more data and
computational power due to their complexity.  The layers of the network
learn increasingly abstract features from the data.  For example, in image
recognition, the first layers might learn to detect edges and corners, while
the later layers learn to recognize more complex objects like faces or cars.
    *   **Applications:** Deep learning has achieved remarkable success in
areas like image recognition, natural language processing, speech
recognition, and game playing.  Examples include:
        *   **Computer Vision:** Image classification, object detection,
image segmentation.
        *   **Natural Language Processing:** Machine translation, text
summarization, sentiment analysis, chatbot development.
        *   **Speech Recognition:** Converting speech to text.
3.  **Natural Language Processing (NLP):**
    *   **Enabling AI to Understand and Generate Language:** NLP focuses on
enabling computers to understand, interpret, and generate human language.
    *   **Key Techniques:**
        *   **Tokenization:** Breaking down text into individual words or
units (tokens).
        *   **Part-of-Speech (POS) Tagging:** Identifying the grammatical
role of each word (e.g., noun, verb, adjective).
        *   **Named Entity Recognition (NER):** Identifying and classifying
named entities (e.g., people, organizations, locations).
        *   **Sentiment Analysis:** Determining the emotional tone of a piece 
of text (e.g., positive, negative, neutral).
        *   **Machine Translation:** Translating text from one language to 
another.
        *   **Text Summarization:** Generating a concise summary of a longer 
text.
        *   **Topic Modeling:** Discovering the main topics discussed in a
collection of documents.
    *   **Applications:** Chatbots, virtual assistants, machine translation,
sentiment analysis, spam filtering, search engines, and more.
4.  **Knowledge Representation and Reasoning:**
    *   **Symbolic AI:** This approach focuses on representing knowledge
explicitly in a symbolic form (e.g., using logical rules or semantic 
networks).
    *   **Reasoning:**  AI systems can use this knowledge to reason and draw
conclusions, often using techniques like:
        *   **Inference Engines:**  Apply logical rules to derive new facts 
from existing knowledge.
        *   **Rule-Based Systems:**  Use a set of rules to make decisions or
solve problems.
        *   **Semantic Networks:**  Represent knowledge as a graph of
interconnected concepts.
    *   **Applications:** Expert systems (systems that provide expert-level
advice in a specific domain), automated reasoning systems, and knowledge-
based systems.
5.  **Robotics:**
    *   **Combining AI with Physical Embodiment:** Robotics combines AI with
mechanical engineering to create robots that can perform physical tasks.
    *   **Key Challenges:**
        *   **Perception:** Enabling robots to perceive their environment
using sensors (e.g., cameras, lidar, sonar).
        *   **Planning:** Planning sequences of actions to achieve a goal.
        *   **Control:** Controlling the robot's movements and actions.
        *   **Localization and Mapping:**  Enabling robots to determine their
location and build a map of their environment.
    *   **Applications:** Manufacturing, logistics, healthcare, exploration,
and more.
**The AI Development Process (Simplified)**
Here's a simplified view of how an AI project typically unfolds:
1.  **Define the Problem:** Clearly identify the task you want the AI to
perform.
2.  **Gather Data:** Collect a relevant dataset.  The quality and quantity of
data are crucial for AI success.
3.  **Choose an Approach:** Select the appropriate AI technique (e.g., machine learning, deep learning, rule-based system).
4.  **Build and Train the Model:** Develop and train the AI model using the
collected data.
5.  **Evaluate the Model:**  Assess the model's performance and make
adjustments as needed.
6.  **Deploy and Monitor:**  Deploy the AI system and continuously monitor
its performance, retraining as needed.
**Important Considerations:**
*   **Ethics:**  AI raises important ethical considerations, such as bias in
algorithms, privacy concerns, and the potential for job displacement.
*   **Bias:**  AI models can inherit biases from the data they are trained
on, leading to unfair or discriminatory outcomes.
*   **Explainability:**  Some AI models (especially deep learning models) can
be difficult to understand and explain, which raises concerns about
accountability and trust.
*   **Security:**  AI systems can be vulnerable to attacks, such as
adversarial attacks that can fool the system into making incorrect
predictions.
**In Summary:**
AI is a broad and rapidly evolving field that aims to create intelligent
machines. It relies on a variety of techniques, including machine learning,
deep learning, natural language processing, knowledge representation, and
robotics.  While AI has made remarkable progress in recent years, it also
presents significant challenges and ethical considerations that must be 
addressed.  It's a field with immense potential to transform many aspects of
our lives, but it's important to approach it responsibly.

3. Mistral (플랫폼) – 무료 API

Mistral은 고성능에 중점을 둔 다양한 응용 프로그램에 대한 다양한 모델을 제공합니다. 플랫폼이 허용합니다 초당 1 초당 요청 및 분당 500,000 토큰. 사용 가능한 일부 모델에는 다음이 포함됩니다.

• Mistral-Large-2402
• Mistral-8B-Latest

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 높은 요청 제한.
• 실험에 집중하십시오.

가격 : 무료 계층 사용 가능.

예제 코드

import os
from mistralai import Mistral
api_key = os.environ["MISTRAL_API_KEY"]
model = "mistral-large-latest"
client = Mistral(api_key=api_key)
chat_response = client.chat.complete(
   model= model,
   messages = [
       {
           "role": "user",
           "Content": "What is the best French cheese?",
       },
   ]
)
print(chat_response.choices[0].message.content)

산출

The "best" French cheese can be subjective as it depends on personal taste
preferences. However, some of the most famous and highly regarded French
cheeses include:
1. Roquefort: A blue-veined sheep's milk cheese from the Massif Central
region, known for its strong, pungent flavor and creamy texture.
2. Brie de Meaux: A soft, creamy cow's milk cheese with a white rind,
originating from the Brie region near Paris. It is known for its mild,
buttery flavor and can be enjoyed at various stages of ripeness.
3. Camembert: Another soft, creamy cow's milk cheese with a white rind,
similar to Brie de Meaux, but often more pungent and runny. It comes from
the Normandy region.
4. Comté: A hard, nutty, and slightly sweet cow's milk cheese from the
Franche-Comté region, often used in fondues and raclettes.
5. Munster: A semi-soft, washed-rind cow's milk cheese from the Alsace
region, known for its strong, pungent aroma and rich, buttery flavor.
6. Reblochon: A semi-soft, washed-rind cow's milk cheese from the Savoie
region, often used in fondue and tartiflette.

4. Huggingface 서버리스 추론 – 무료 API

Huggingface는 다양한 개방형 모델을 배포하고 사용할 수있는 플랫폼을 제공합니다. 10GB보다 작은 모델과 제안으로 제한됩니다. 한 달에 가변 크레딧.

사용 가능한 일부 모델에는 다음이 포함됩니다.

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 광범위한 모델.
• 쉬운 통합.

가격 : 한 달에 가변 크레딧.

예제 코드

from huggingface_hub import InferenceClient
client = InferenceClient(
 provider="hf-inference",
 api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx"

)
messages = [
 {
   "role": "user",
   "content": "What is the capital of Germany?"
 }
]
completion = client.chat.completions.create(
   model="meta-llama/Meta-Llama-3-8B-Instruct",
 messages=messages,
 max_tokens=500,
)
print(completion.choices[0].message)

산출

ChatCompletionOutputMessage(role="assistant", content="The capital of Germany
is Berlin.", tool_calls=None)

5. 뇌 – 무료 API

Cerebras는 고성능에 중점을 둔 라마 모델에 대한 액세스를 제공합니다. 플랫폼이 허용합니다 분당 30 개의 요청과 분당 60,000 개의 토큰.

사용 가능한 일부 모델에는 다음이 포함됩니다.

• 3.1 8b로 전화하십시오
• 3.3 70b로 전화하십시오

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 높은 요청 제한.
• 강력한 모델.

가격 : 무료 계층 사용 가능하고 대기자 명단에 가입하십시오

예제 코드

import os
from cerebras.cloud.sdk import Cerebras
client = Cerebras(
 api_key=os.environ.get("CEREBRAS_API_KEY"),
)
chat_completion = client.chat.completions.create(
 messages=[
 {"role": "user", "content": "Why is fast inference important?",}
],
 model="llama3.1-8b",
)

산출

Fast inference is crucial in various applications because it has several
benefits, including:
1. **Real-time decision making**: In applications where decisions need to be
made in real-time, such as autonomous vehicles, medical diagnosis, or online
recommendation systems, fast inference is essential to avoid delays and
ensure timely responses.
2. **Scalability**: Machine learning models can process a high volume of data
in real-time, which requires fast inference to keep up with the pace. This
ensures that the system can handle large numbers of users or events without
significant latency.
3. **Energy efficiency**: In deployment environments where power consumption
is limited, such as edge devices or mobile devices, fast inference can help
optimize energy usage by reducing the time spent on computations.
4. **Cost-effectiveness**: Faster inference can help reduce computing
resources, such as GPU or CPU capacity, which can lead to lower costs and
more efficient usage.
5. **Improved user experience**: Fast inference ensures that users receive
quick and accurate results, leading to a better overall experience and
increasing user engagement.
6. **Reduced latency**: In applications where latency is critical, such as
online gaming, voice assistants, or customer service, fast inference
minimizes the time between user input and response, resulting in a smoother
experience.
7. **Optimization for inference engines**: Many inference engines have
optimized for faster inference speeds for deployment on edge devices. Some
cloud-based services specifically optimize their inference speed and
latency.
Key areas where fast inference is essential include:
1. **Computer vision**: Applications like image classification, object
detection, and facial recognition require fast inference to analyze and
process visual data in real-time.
2. **Natural Language Processing (NLP)**: NLP models need fast inference to
understand and process text input, such as chatbots, speech recognition, and
sentiment analysis.
3. **Recommendation systems**: Online recommendation systems rely on fast
inference to predict and personalize user experiences.
4. **Autonomous systems**: Autonomous vehicles, drones, and robots require
fast inference to make real-time decisions about navigation, obstacle
avoidance, and control.
In summary, fast inference is crucial in various applications where real-time
decision making, scalability, energy efficiency, cost-effectiveness, user
experience, and reduced latency are critical factors.Fast inference is
crucial in various applications because it has several benefits, including:
1. **Real-time decision making**: In applications where decisions need to be
made in real-time, such as autonomous vehicles, medical diagnosis, or online
recommendation systems, fast inference is essential to avoid delays and
ensure timely responses.
2. **Scalability**: Machine learning models can process a high volume of data
in real-time, which requires fast inference to keep up with the pace. This
ensures that the system can handle large numbers of users or events without
significant latency.
3. **Energy efficiency**: In deployment environments where power consumption
is limited, such as edge devices or mobile devices, fast inference can help
optimize energy usage by reducing the time spent on computations.
4. **Cost-effectiveness**: Faster inference can help reduce computing
resources, such as GPU or CPU capacity, which can lead to lower costs and
more efficient usage.
5. **Improved user experience**: Fast inference ensures that users receive
quick and accurate results, leading to a better overall experience and
increasing user engagement.
6. **Reduced latency**: In applications where latency is critical, such as
online gaming, voice assistants, or customer service, fast inference
minimizes the time between user input and response, resulting in a smoother
experience.
7. **Optimization for inference engines**: Many inference engines have
optimized for faster inference speeds for deployment on edge devices. Some
cloud-based services specifically optimize their inference speed and
latency.
Key areas where fast inference is essential include:
1. **Computer vision**: Applications like image classification, object
detection, and facial recognition require fast inference to analyze and
process visual data in real-time.
2. **Natural Language Processing (NLP)**: NLP models need fast inference to
understand and process text input, such as chatbots, speech recognition, and
sentiment analysis.
3. **Recommendation systems**: Online recommendation systems rely on fast
inference to predict and personalize user experiences.
4. **Autonomous systems**: Autonomous vehicles, drones, and robots require
fast inference to make real-time decisions about navigation, obstacle
avoidance, and control.
In summary, fast inference is crucial in various applications where real-time
decision making, scalability, energy efficiency, cost-effectiveness, user
experience, and reduced latency are critical factors.

6. Groq – 무료 API

Groq는 다양한 응용 프로그램에 대한 다양한 모델을 제공하여 허용합니다 하루에 1,000 개의 요청과 분당 6,000 개의 토큰.

사용 가능한 일부 모델에는 다음이 포함됩니다.

• Deepseek R1 Distill Llama 70b
• Gemma 2 9B 지시

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 높은 요청 제한.
• 다양한 모델 옵션.

가격 : 무료 계층 사용 가능.

예제 코드

import os
from groq import Groq
client = Groq(
   api_key=os.environ.get("GROQ_API_KEY"),
)
chat_completion = client.chat.completions.create(
   messages=[
       {
           "role": "user",
           "content": "Explain the importance of fast language models",
       }
   ],
   model="llama-3.3-70b-versatile",
)
print(chat_completion.choices[0].message.content)

산출

Fast language models are crucial for various applications and industries, and
their importance can be highlighted in several ways:
1. **Real-Time Processing**: Fast language models enable real-time processing
of large volumes of text data, which is essential for applications such as:
* Chatbots and virtual assistants (e.g., Siri, Alexa, Google Assistant) that
need to respond quickly to user queries.
* Sentiment analysis and opinion mining in social media, customer feedback,
and review platforms.
* Text classification and filtering in email clients, spam detection, and content moderation.
2. **Improved User Experience**: Fast language models provide instant responses, which is vital for:
* Enhancing user experience in search engines, recommendation systems, and
content retrieval applications.
* Supporting real-time language translation, which is essential for global
communication and collaboration.
* Facilitating quick and accurate text summarization, which helps users to
quickly grasp the main points of a document or article.
3. **Efficient Resource Utilization**: Fast language models:
* Reduce the computational resources required for training and deployment,
making them more energy-efficient and cost-effective.
* Enable the processing of large volumes of text data on edge devices, such
as smartphones, smart home devices, and wearable devices.
4. **Competitive Advantage**: Organizations that leverage fast language models can:
* Respond faster to changing market conditions, customer needs, and competitor activity.
* Develop more accurate and personalized models, which can lead to improved
customer engagement, retention, and acquisition.
5. **Research and Development**: Fast language models accelerate the research
and development process in natural language processing (NLP) and artificial
intelligence (AI), allowing researchers to:
* Quickly test and validate hypotheses, which can lead to new breakthroughs
and innovations.
* Explore new applications and domains, such as multimodal processing,
explainability, and interpretability.
6. **Scalability and Flexibility**: Fast language models can be easily scaled
up or down to accommodate varying workloads, making them suitable for:
* Cloud-based services, where resources can be dynamically allocated and
deallocated.
* On-premises deployments, where models need to be optimized for specific
hardware configurations.
7. **Edge AI and IoT**: Fast language models are essential for edge AI and
IoT applications, where:
* Low-latency processing is critical for real-time decision-making, such as
in autonomous vehicles, smart homes, and industrial automation.
* Limited computational resources and bandwidth require efficient models that
can operate effectively in resource-constrained environments.
In summary, fast language models are essential for various applications,
industries, and use cases, as they enable real-time processing, improve user
experience, reduce computational resources, and provide a competitive
advantage.

7. Scaleway Generative Free API

Scaleway는 무료로 다양한 생성 모델을 제공합니다. 분당 100 개의 요청과 분당 200,000 개의 토큰.

사용 가능한 일부 모델에는 다음이 포함됩니다.

• BGE-Multingual-Gemma2
• LLAMA 3.1 70B 지시

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 관대 한 요청 제한.
• 다양한 모델.

가격 : 2025 년 3 월까지 무료 베타.

예제 코드

from openai import OpenAI

# Initialize the client with your base URL and API key
client = OpenAI(
   base_url="
   api_key=""
)
# Create a chat completion for Llama 3.1 8b instruct
completion = client.chat.completions.create(
   model="llama-3.1-8b-instruct",
   messages=[{"role": "user", "content": "Describe a futuristic city with advanced technology and green energy solutions."}],
   temperature=0.7,
   max_tokens=100
)
# Output the result
print(completion.choices[0].message.content)

산출

**Luminaria City 2125: A Beacon of Sustainability**
Perched on a coastal cliff, Luminaria City is a marvel of futuristic
architecture and innovative green energy solutions. This self-sustaining
metropolis of the year 2125 is a testament to humanity's ability to engineer
a better future.
**Key Features:**
1. **Energy Harvesting Grid**: A network of piezoelectric tiles covering the
city's streets and buildings generates electricity from footsteps,
vibrations, and wind currents. This decentralized energy system reduces
reliance on fossil fuels and makes Luminaria City nearly carbon-neutral.
2. **Solar Skiescraper**: This 100-story skyscraper features a unique double-
glazed facade with energy-generating windows that amplify solar radiation,
providing up to 300% more illumination and 50% more energy for the city's
homes and businesses.
3. **Floating Farms**: Aerodynamically designed and vertically integrated
cities of the future have floating aerial fields providing urban
communities' with access to fresh locally sourced goods such as organics.
4. **Smart-Grid Management**: An advanced artificial intelligence system,
dubbed SmartLum, oversees energy distribution, optimizes resource
allocation, and adjusts energy production according to demand.
5. **Water Management**: Self-healing, concrete-piezoelectric stormwater
harvesting systems ensure pure drinking water for residents, using the
potential energy generated by vibrations in stormwater flow for generating
electrical energy for Luminaria.
6. **Algae-Based Oxygenation**: A 10-kilometer-long algae-based bio-reactor
embedded in the city's walls and roof helps purify the atmosphere, produce
oxygen, and create valuable bio-energy molecules.
7. **Electric-Vehicle Infrastructure**: From sleek personal magnetometers to
large-scale omnibus systems, sustainable urban transportation is entirely
electric, effortlessly integrated with Luminaria City's omnipresent AI
network.
8. **Sky Tree**: A slender, aerodynamically-engineered skyscraper extends
high into the atmosphere, acting as a giant wind turbine and rainwater
harvester.
9. **Botanical Forestal Architecture**: The innovative "Forest Walls"
integrate living plants, water-collecting surfaces, and carbon capture
infrastructure to sustain life in a unique symbiotic process.
10. **Advanced Public Waste Systems**: An ultra-efficient system assimilates,
recycles and combusts the city's waste efficiently and sustainably due to
advanced waste-pre-treatment facilities.
**Luminaria City: The Model for a Sustainable Future**
Luminaria City showcases humanity's ability to reimagine urban planning and
technologies to preserve a thriving planet. By harnessing advanced
technologies, harnessed new, and maximizing human symbiosis with nature,
this stunning metropolis will inspire cities around the world to embark on
their own sustainable journey to a brighter future.

8. OVH AI 엔드 포인트 – 무료 API

OVH는 다양한 AI 모델에 대한 액세스를 무료로 제공하여 분당 12 개의 요청. 사용 가능한 일부 모델에는 다음이 포함됩니다.

• Codellama 13B 지시
• LLAMA 3.1 70B 지시

문서 및 사용 가능한 모든 모델 :

장점

• 사용하기 쉽습니다.
• 다양한 모델.

가격 : 무료 베타 사용 가능.

예제 코드

import os
from openai import OpenAI
client = OpenAI(
   base_url="
   api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)
def chat_completion(new_message: str) -> str:
   history_openai_format = [{"role": "user", "content": new_message}]
   return client.chat.completions.create(
       model="Llama-2-13b-chat-hf",
       messages=history_openai_format,
       temperature=0,
       max_tokens=1024
   ).choices.pop().message.content
if __name__ == '__main__':
   print(chat_completion("Write a story in the style of James Joyce. The story should be about a trip to the Irish countryside in 2083, to see the beautiful scenery and robots.d"))

산출

Sure, I'd be happy to help! Here's a story in the style of James Joyce, set
in the Irish countryside in 2083: As I stepped off the pod-train and onto
the lush green grass of the countryside, the crisp air filled my lungs and
invigorated my senses. The year was 2083, and yet the rolling hills and
sparkling lakes of Ireland seemed unchanged by the passage of time. The only
difference was the presence of robots, their sleek metallic bodies and
glowing blue eyes a testament to the advancements of technology. I had come
to this place seeking solace and inspiration, to lose myself in the beauty
of nature and the wonder of machines. As I wandered through the hills, I
came across a group of robots tending to a field of crops, their delicate
movements and precise calculations ensuring a bountiful harvest. One of the
robots, a sleek and agile model with wings like a dragonfly, fluttered over
to me and offered a friendly greeting. "Good day, traveler," it said in a
melodic voice. "What brings you to our humble abode?" I explained my desire
to experience the beauty of the Irish countryside, and the robot nodded
sympathetically. "Ah, yes," it said. "There is much to see and explore here.
Would you like a guided tour?" I eagerly accepted the offer, and the robot
led me on a journey through the rolling hills and sparkling lakes. We saw
towering waterfalls and ancient ruins, and the robot shared stories of the
history and culture of the land. As we walked, the sun began to set, casting
a golden glow over the landscape. As the stars began to twinkle in the night
sky, the robot and I sat down on a hill overlooking the countryside. "This
is a special place," the robot said, its voice filled with a sense of
wonder. "A place where nature and technology coexist in harmony." I nodded
in agreement, feeling a sense of awe and gratitude for this wondrous place.
And as I looked out at the stars, I knew that this trip to the

9. 함께 무료 API

함께 특정 제한이 언급되지 않은 다양한 LLM에 액세스하기위한 공동 작업 플랫폼입니다. 사용 가능한 일부 모델에는 다음이 포함됩니다.

• LLAMA 3.2 11B 비전 교육
• Deepseek R1 Distil Llama 70b

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 다양한 모델에 대한 액세스.
• 협업 환경.

가격 : 무료 계층 사용 가능.

예제 코드

from together import Together
client = Together()
stream = client.chat.completions.create(
 model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
 messages=[{"role": "user", "content": "What are the top 3 things to do in New York?"}],
 stream=True,
)
for chunk in stream:
 print(chunk.choices[0].delta.content or "", end="", flush=True)

산출

The city that never sleeps - New York! There are countless things to see and
do in the Big Apple, but here are the top 3 things to do in New York:
1. **Visit the Statue of Liberty and Ellis Island**: Take a ferry to Liberty
Island to see the iconic Statue of Liberty up close. You can also visit the
Ellis Island Immigration Museum to learn about the history of immigration in
the United States. This is a must-do experience that offers breathtaking
views of the Manhattan skyline.
2. **Explore the Metropolitan Museum of Art**: The Met, as it's
affectionately known, is one of the world's largest and most famous museums.
With a collection that spans over 5,000 years of human history, you'll find
everything from ancient Egyptian artifacts to modern and contemporary art.
The museum's grand architecture and beautiful gardens are also worth
exploring.
3. **Walk across the Brooklyn Bridge**: This iconic bridge offers stunning
views of the Manhattan skyline, the East River, and Brooklyn. Take a
leisurely walk across the bridge and stop at the Brooklyn Bridge Park for
some great food and drink options. You can also visit the Brooklyn Bridge's
pedestrian walkway, which offers spectacular views of the city.
Of course, there are many more things to see and do in New York, but these
three experiences are a great starting point for any visitor.
Additional suggestions:
- Visit the Top of the Rock Observation Deck for panoramic views of the city.
- Take a stroll through Central Park, which offers a peaceful escape from the
hustle and bustle of the city.
- Catch a Broadway show or a performance at one of the many music venues in
the city.
- Explore the vibrant neighborhoods of Chinatown, Little Italy, and Greenwich
Village.
- Visit the 9/11 Memorial & Museum to pay respects to the victims of the 9/11 attacks.
Remember to plan your itinerary according to your interests and the time of
year you visit, as some attractions may have limited hours or be closed due
to weather or other factors.

10. 코셔 – 무료 API

Cohere는 다양한 응용 프로그램에 대한 강력한 언어 모델에 대한 액세스를 제공하여 분당 20 개의 요청과 한 달에 1,000 요청. 사용 가능한 일부 모델에는 다음이 포함됩니다.

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 사용하기 쉽습니다.
• NLP 작업에 집중하십시오.

가격 : 무료 계층 사용 가능.

예제 코드

import cohere
co = cohere.ClientV2(">")
response = co.chat(
   model="command-r-plus",
   messages=[{"role": "user", "content": "hello world!"}]
)
print(response)

산출

id='703bd967-fbb0-4758-bd60-7fe01b1984c7' finish_reason='COMPLETE'
prompt=None message=AssistantMessageResponse(role="assistant",
tool_calls=None, tool_plan=None, content=
[TextAssistantMessageResponseContentItem(type="text", text="Hello! How can I
help you today?")], citations=None)
usage=Usage(billed_units=UsageBilledUnits(input_tokens=3.0,
output_tokens=9.0, search_units=None, classifications=None),
tokens=UsageTokens(input_tokens=196.0, output_tokens=9.0)) logprobs=None

11. Github 모델 – 무료 API

Github는 다양한 AI 모델 모음을 제공하며, 속도 제한은 가입 계층.

사용 가능한 일부 모델에는 다음이 포함됩니다.

• AI21 잠바 1.5 큰
• 코셔 명령 r

문서 및 사용 가능한 모든 모델 : 링크

장점

• 광범위한 모델에 대한 액세스.
• Github와의 통합.

가격 : GitHub 계정으로 무료.

예제 코드

import os
from openai import OpenAI
token = os.environ["GITHUB_TOKEN"]
endpoint = "
model_name = "gpt-4o"
client = OpenAI(
   base_url=endpoint,
   api_key=token,
)
response = client.chat.completions.create(
   messages=[
       {
           "role": "system",
           "content": "You are a helpful assistant.",
       },
       {
           "role": "user",
           "content": "What is the capital of France?",
       }
   ],
   temperature=1.0,
   top_p=1.0,
   max_tokens=1000,
   model=model_name
)
print(response.choices[0].message.content)

산출

The capital of France is **Paris**.

12. 불꽃 놀이 AI – 무료 API

불꽃 놀이는 다양한 강력한 AI 모델을 제공하며 서버리스 추론이 최대 6,000 rpm, 25 억 토큰/일

사용 가능한 일부 모델에는 다음이 포함됩니다.

• LLAMA-V3P1-405B- 강조.
• Deepseek-R1

사용 가능한 모든 모델 : 링크
문서 : 링크

장점

• 비용 효율적인 사용자 정의
• 빠른 추론.

가격 : 무료 크레딧은 $ 1에 제공됩니다.

예제 코드

from fireworks.client import Fireworks
client = Fireworks(api_key="")
response = client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p1-8b-instruct",
messages=[{
  "role": "user",
  "content": "Say this is a test",
}],
)
print(response.choices[0].message.content)

산출

I'm ready for the test! Please go ahead and provide the questions or prompt
and I'll do my best to respond.

LLM 프리 API 사용의 이점

1. 접근성 : 심층 AI 전문 지식 또는 인프라 투자가 필요하지 않습니다.
2. 사용자 정의 : 특정 작업 또는 도메인에 대한 미세 조정 모델.
3. 확장 성 : 비즈니스가 성장함에 따라 많은 양의 요청을 처리합니다.

LLM이없는 API의 효율적인 사용을위한 팁

1. 올바른 모델을 선택하십시오 : 기본 작업을위한 간단한 모델로 시작하고 필요에 따라 확장하십시오.
2. 사용 모니터링 : 대시 보드를 사용하여 토큰 소비를 추적하고 지출 한도를 설정하십시오.
3. 토큰 최적화 : Craft Concise Promps는 토큰 사용량을 최소화하면서 원하는 결과를 얻습니다.

결론

이러한 무료 API의 가용성을 통해 개발자와 비즈니스는 상당한 선불 비용없이 고급 AI 기능을 응용 프로그램에 쉽게 통합 할 수 있습니다. 이러한 리소스를 활용하면 프로젝트에서 사용자 경험을 향상시키고 작업을 자동화하고 혁신을 유도 할 수 있습니다. 오늘이 API를 탐색하고 응용 프로그램에서 AI의 잠재력을 잠금 해제하십시오.