Overview
Artificial intelligence and machine learning projects demand computing capacity far beyond what traditional CPU-based infrastructure can deliver. Training large language models (LLMs), training computer vision models and running real-time inference all require GPU clusters. Buying GPU hardware outright brings high upfront costs, the risk of technology obsolescence and idle capacity during periods of low utilization. AI Cloud makes this GPU capacity accessible on a pay-as-you-go model.
Indexline AI Cloud supports every stage of the model development lifecycle. Both infrastructure and MLOps tooling are provided for data preparation and feature engineering, model training and experimentation (MLflow, Weights & Biases integration), model registry and versioning, deployment and serving (Triton Inference Server, TorchServe) and model monitoring.
Security and compliance are critical constraints in enterprise AI projects. Organizations training models on sensitive business and customer data look for clear assurances on data sovereignty, access control and the auditability of model outputs. Indexline AI Cloud meets these requirements with a GPU cluster located in Turkey, isolated tenant environments and comprehensive audit logging.
Correctly sizing AI infrastructure and optimizing its cost determine the economic sustainability of an AI project. Training and inference workloads need different hardware profiles: training prioritizes high memory and GPU bandwidth, while inference prioritizes low latency. Using spot GPU instances can cut the cost of long training jobs by 60-70%. Indexline integrates this cost optimization into the MLOps pipeline.
In AI projects, the real challenge is often not writing model code but building an end-to-end, reproducible working environment. Data science teams may achieve success in one experimental environment, yet retraining the same model on new data, enabling another team member to reproduce the same result or promoting the model to production in a controlled way is a far bigger problem for most organizations. Here, AI Cloud does more than provide GPUs; it creates an operating foundation where experiments are standardized, artifacts are managed and the model lifecycle is traceable.
On the enterprise AI journey, data quality and infrastructure capacity cannot be considered separately. Even the most powerful GPU cluster cannot deliver the expected results on poorly labeled, scattered or poorly governed data. That is why, in Indexline AI Cloud projects, data source access patterns, the storage layer, data versioning and experiment reproducibility are assessed together. This increases the likelihood that the infrastructure investment translates into real business value.
Another critical aspect of AI Cloud architecture is production inference. Although many organizations treat training a model as success, business value emerges only when the model is integrated into the application flow in a controlled way. Deployments that ignore API-based serving, scalable inference, queue-based batch processing, model version transitions, A/B testing and rollback scenarios are not sustainable. For this reason, Indexline designs its AI Cloud service not just as a lab environment but as a production-ready platform.
From a security perspective, AI environments carry more complex risks than conventional application infrastructure. Sensitive training data, model outputs that influence decision-making and the widespread use of open-source model components all require additional layers of control. Access controls, data isolation, model artifact integrity, prompt security and usage logs are indispensable, especially in enterprise AI projects. When these control layers are built into the AI Cloud design from the outset, risks that would be hard to remedy later are reduced.
On the cost side, the most common problems are GPU resources sitting idle outside business hours or the wrong hardware being assigned to the wrong workload. Not every model needs the same accelerator profile. Some inference services perform well enough on optimized CPUs, while some training processes require distributed multi-GPU clusters. Indexline AI Cloud aims to turn this difference into operational efficiency through schedulers, capacity pooling, job scheduling and spot usage policies.
Ultimately, AI Cloud is not merely a technology layer that lets an organization get started quickly; it is a strategic working environment where data science, MLOps, security, compliance and cost management intersect. Particularly in regulated industries and in scenarios where data must remain within national borders, a local, manageable AI platform is one of the most effective ways to strike the right balance between speed of innovation and corporate control.
How It Works
Project Assessment: The AI use case, model type, data volume and performance requirements are analyzed.
Infrastructure Selection: High-GPU-memory clusters (A100, H100) are selected for training, and optimized GPU/CPU resources for inference.
Environment Setup: Jupyter Lab, VS Code Server, CUDA and the PyTorch/TensorFlow toolchain are provided as a ready-to-use environment.
Data Pipeline: Data storage, preprocessing and augmentation pipelines are set up.
MLOps Integration: Model tracking, versioning and experiment management tools are integrated.
Model Deployment: The trained model is deployed as an inference endpoint, and autoscaling is configured.
Monitoring: Model performance, data drift and system metrics are monitored.
Key Features
GPU Cluster Access
On-demand access to NVIDIA A100/H100 GPUs. Clusters for multi-GPU training are ready within minutes.
MLOps Platform
Model lifecycle management is automated through integration with MLflow, Kubeflow or Weights & Biases.
Jupyter & IDE Environment
Jupyter Lab, VS Code Server and preconfigured ML environments are delivered ready to use.
Inference Serving
Simultaneous serving of multiple models with NVIDIA Triton Inference Server. Dynamic batching and model optimization are supported.
Model Registry
Model versioning, metadata management and A/B testing infrastructure keep the promotion of models to production under control.
Data Security
Isolated tenant environments, data encryption and access logging keep sensitive data secure in the AI environment.
Spot GPU
Using spot GPUs for long training jobs cuts costs by 60-70%. Interruptions are handled through checkpointing.
Located in Turkey
A domestic GPU cluster compliant with KVKK (Turkish Personal Data Protection Law). AI projects run without data leaving the country.
Benefits & Advantages
A pay-as-you-go model instead of buying GPU hardware dramatically lowers the upfront cost of AI projects.
The technology refresh cycle is managed by Indexline; your organization always has access to the latest GPU generation.
Training, inference and development environments are unified on a single platform, reducing tooling complexity.
The MLOps pipeline accelerates the transition from model development to production.
A domestic GPU cluster meets data sovereignty requirements, so regulatory compliance comes built in.
Spot GPU usage optimizes the cost of long training runs and lowers the economic threshold for experimental projects.
Standardized experiment management improves reproducibility across teams and allows model quality to be compared more reliably.
Model registry, versioning and controlled deployment processes reduce production risk.
Local, isolated infrastructure raises the level of trust and auditability in projects that use sensitive data.
Separating training and inference workloads optimizes resource utilization and reduces unnecessary GPU consumption.
Industry Use Cases
Fintech
A credit scoring model was trained on the Indexline GPU cluster, meeting local data and BDDK (Banking Regulation and Supervision Agency) compliance requirements.
Healthcare
A medical image analysis model was developed on the GPU cluster, satisfying the requirement for KVKK-compliant local infrastructure.
Retail
A recommendation engine model was continuously retrained; prediction accuracy for customer purchase journeys increased by 35%.
Manufacturing
A production line quality control vision model was trained on the domestic GPU cluster; defect detection reached 95% accuracy.
Public Sector
A natural language processing model was developed for the automatic classification of public sector documents; no data left the country.
Insurance
A claims image assessment model was trained on the GPU cluster; claims assessment time was reduced by 70%.
The Indexline Approach
The biggest cost of AI infrastructure is not the GPUs themselves but using them inefficiently. By monitoring GPU utilization for training jobs, idle time is minimized. Spot instances and a scheduled workload strategy increase cost efficiency.
MLOps maturity determines the long-term value of an AI project. Training a model is only the beginning; deploying it to production, monitoring it, detecting drift and retraining it are the stages where an MLOps program delivers its real value.
AI security is a new and rapidly evolving field. Manipulation of model outputs (prompt injection, adversarial attacks), poisoning of training data and model theft are security risks that enterprise AI projects must not overlook.
Indexline's approach positions AI Cloud not as an isolated GPU rental service but as a platform where data science teams can work in a controlled, reproducible and production-like way.
Every AI project has different success criteria. Some aim to shorten training time, some to ensure data security and others to optimize the cost of live inference. That is why we tailor the architecture to the project type rather than falling back on one-size-fits-all infrastructure.
In enterprise AI, controlled governance is just as critical as rapid experimentation. We prioritize designing an operating model that brings freedom to experiment together with security and compliance requirements on common ground.
Frequently Asked Questions
Which frameworks do you support?
PyTorch, TensorFlow, JAX, Hugging Face Transformers, scikit-learn and other Python-based ML frameworks are supported. CUDA and cuDNN versions can be selected as needed.
How many parameters can a model have?
It depends on the hardware size and the multi-GPU configuration. Multi-A100/H100 clusters can be configured for large language models with billions of parameters. The number of GPUs and memory capacity are determined according to your needs.
Where is training data stored?
Training data is stored on a storage system located in Turkey. Customer tenants are isolated from one another. Data encryption is enabled by default.
How does the model development process work?
GPUs are accessed via Jupyter Lab or VS Code Server. Experiment results are tracked with MLflow. Model checkpoints are saved automatically to object storage. For production, the best model passes through an approved deployment pipeline.
How fast is inference?
Models optimized with Triton Inference Server can serve with millisecond latency. Model size, batch size and GPU choice affect latency. Models optimized with TensorRT or ONNX can reduce latency by 2-5x.
Can we fine-tune our existing model?
Yes, open-source foundation models (Llama, Mistral, Falcon) or models you have developed yourself can be fine-tuned on the Indexline GPU cluster. With efficient fine-tuning techniques such as PEFT and LoRA, large models can be fine-tuned with limited GPU resources.
What is the difference between batch and real-time inference?
Batch inference is cost-effective for high-volume, latency-tolerant workloads (overnight processing, reporting). Real-time inference is for user interaction scenarios that require low latency. Different inference endpoint configurations are offered for each.
How long does LLM fine-tuning take?
It depends on dataset size, model size and the number of GPUs. Fine-tuning a 7B-parameter model with 10K examples can be completed in a few hours. For larger models and datasets, training times on the order of days should be expected.