The developer
AI cloud.

The developer
AI cloud.

Deploy, scale, and fine-tune models at lightning speed

Deploy, scale, and fine-tune models at lightning speed

Deploy, scale, and fine-tune models at lightning speed

Built for developers by developers

Built for developers by developers

Built for developers by developers

Inference isn’t one size fits all.

Inference
isn’t one size fits all.

Inference isn’t one size fits all.

Real-time

Ultra-low latency for live demands

Batch

Low-cost for high-volume, bulk processing

Powered by Adaptive Inference, our platform intelligently scales to fit your workload, ensuring accuracy, high throughput, cost optimization, and total privacy.

Powered by Adaptive Inference, our platform intelligently scales to fit your workload, ensuring accuracy, high throughput, cost optimization, and total privacy.

Powered by Adaptive Inference, our platform intelligently scales to fit your workload, ensuring accuracy, high throughput, cost optimization, and total privacy.

Real-time

Batch

from openai import OpenAI
# OpenAI compatible API
client = OpenAI(
base_url="https://api.kluster.ai/v1",
api_key="my_klusterai_api_key"
)
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=[
        {
            "role": "user",
            "content": "Provide an analysis of market trends in AI."
        }
    ]
)
print(response.choices[0].message.content)

Real-time

Batch

from openai import OpenAI
# OpenAI compatible API
client = OpenAI(
base_url="https://api.kluster.ai/v1",
api_key="my_klusterai_api_key"
)
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=[
        {
            "role": "user",
            "content": "Provide an analysis of market trends in AI."
        }
    ]
)
print(response.choices[0].message.content)

Real-time

Batch

from openai import OpenAI
# OpenAI compatible API
client = OpenAI(
base_url="https://api.kluster.ai/v1",
api_key="my_klusterai_api_key"
)
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=[
        {
            "role": "user",
            "content": "Provide an analysis of market trends in AI."
        }
    ]
)
print(response.choices[0].message.content)

Fine-tune your
AI for a perfect fit

Fine-tune your
AI for a perfect fit

Fine-tune your
AI for a perfect fit

Refine models to your data and build AI that works for you.

Upload dataset

Start fine-tuning job

Monitor progress

Use model

from openai import OpenAI
client = OpenAI(api_key="YOUR_KEY", base_url="https://api.kluster.ai/v1")

file = client.files.create(
  file=open("training_data.jsonl", "rb"),
  purpose="fine-tune"
)

Upload dataset

Start fine-tuning job

Monitor progress

Use model

from openai import OpenAI
client = OpenAI(api_key="YOUR_KEY", base_url="https://api.kluster.ai/v1")

file = client.files.create(
  file=open("training_data.jsonl", "rb"),
  purpose="fine-tune"
)

Upload

dataset

Start fine

tuning job

Monitor

progress

Use

model

from openai import OpenAI
client = OpenAI(api_key="YOUR_KEY", base_url="https://api.kluster.ai/v1")

file = client.files.create(
  file=open("training_data.jsonl", "rb"),
  purpose="fine-tune"
)

Upload

dataset

Start fine

tuning job

Monitor

progress

Use

model

from openai import OpenAI
client = OpenAI(api_key="YOUR_KEY", base_url="https://api.kluster.ai/v1")

file = client.files.create(
  file=open("training_data.jsonl", "rb"),
  purpose="fine-tune"
)

Dedicated Deployments

Dedicated Deployments

Dedicated Deployments

Lightning-fast, production-grade inference - no infra required.

Lightning-fast, production-grade inference - no infra required.

Lightning-fast, production-grade inference - no infra required.

Zero infrastructure management

No containers, no Kubernetes - just your model, deployed, and ready

Private, isolated, and secure

Tenant-isolation, in-transit encryption, and no prompt logging keep customers (and auditors) happy

Bring any model

Use public models from Hugging Face or your own repo, and turn them into production ready endpoints without managing servers

from openai import OpenAI
# OpenAI compatible API
client = OpenAI(
base_url="https://api.kluster.ai/v1",
api_key="my_klusterai_api_key"
)
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8:my-dedicated-model-name",
    messages=[
        {
            "role": "user",
            "content": "Provide an analysis of market trends in AI."
        }
    ]
)
print(response.choices[0].message.content)

Why developers love kluster.ai

Why developers love kluster.ai

Why developers love kluster.ai

High volume by design

Experience seamless scalability and best-in-class rate limits, ensuring uninterrupted performance.

Predictable completion windows

Choose a timeframe that suits your needs—whether it’s an hour or a full day, we’ve got you covered.

Unmatched value


Achieve top-tier performance and reliability at half the cost of leading providers.

See what developers
have to say

See what developers
have to say

See what developers
have to say