AI Infrastructure & MLOps
AI Infrastructure & MLOps
AI / AI Infrastructure
Building an AI demo is easy. Running models at scale, with GPUs humming and endpoints up, is another story. Whether you're fine-tuning a custom model, running open-source LLMs, or just tired of burning cash on inefficient pipelines — we’ll help you build the infrastructure that lasts. We bring real MLOps, GPU orchestration, model lifecycle management, and cost-optimized deployment pipelines to your AI stack.
Scalable, secure, GPU-accelerated infrastructure for AI apps that actually run in production.
What We Build
MLOps Toolchains: CI/CD pipelines for training, testing, deploying, and monitoring models.
GPU Orchestration: On-demand and auto-scaling GPU clusters (NVIDIA, AMD, cloud-native or bare metal).
Model Serving Infrastructure: Real-time inference endpoints with load balancing, batching, and A/B testing.
LLM Hosting Platforms: Ollama, LM Studio, HuggingFace Accelerated Inference, vLLM, TGI — all supported.
Vector Databases & Embeddings: Pinecone, Weaviate, Qdrant, FAISS, or in-house setups.
Observability & Cost Controls: GPU usage tracking, autoscaling rules, monitoring, alerting, and logging.
Open-Source Model Optimization: Quantization, pruning, distillation, model packaging and rollout.
Custom AI DevOps Environments: Notebook infra, remote training clusters, secure sandboxed runtimes.
Use Cases We Support
- Internal LLM deployments — on-prem, in VPC, or cross-cloud.
- Scalable RAG apps with managed vector search and cost-efficient inference.
- Training and fine-tuning jobs on multi-GPU or TPU environments.
- Dev environments for research teams with notebooks and experiment tracking.
- AI-enabled SaaS platforms that need infrastructure built to scale.
Supported Clouds, Platforms, & Frameworks
- AWS SageMaker, Bedrock, ECS, EKS
- GCP Vertex AI, GKE, TPUs
- Azure ML
- Kubernetes, Docker, Terraform
- LangChain, PyTorch, TensorFlow, Ray, JAX
- Weights & Biases, MLflow, Comet, DVC
Why Conflict™?
- We’ve built AI infra that runs inside highly regulated industries — and on a single founder’s MacBook.
- We balance velocity, security, cost, and performance — no hand-wavy abstractions.
- We don’t treat MLOps as an afterthought. It’s built into our process.
Contact Us
Make It Real — And Make It Scale
Don’t let infra be your bottleneck. We’ll help you ship AI that doesn’t just work — it works under load.