Octopod+

What is Octopod+?

Octopod+ is our enterprise-grade Container-as-a-Service platform built specifically for AI and machine learning workloads. From self-hosted LLMs to vector search pipelines and GPU-accelerated training clusters — Octopod+ gives your team production-ready infrastructure without the months of setup. Designed for organizations that need performance, compliance, and scale.

What Octopod+ Offers

🧠 LLM & Model Serving

Run large language models on dedicated infrastructure with GPU acceleration, model versioning, and auto-scaling.

• Ollama — Run Llama 3, Mistral, Gemma, Phi, and dozens of open-source models with one command
• vLLM — High-throughput, low-latency serving engine with PagedAttention and continuous batching
• Text Generation Inference (TGI) — Hugging Face’s production-grade LLM inference server
• LocalAI — OpenAI-compatible API for self-hosted models, including vision and audio
• Triton Inference Server — NVIDIA’s multi-framework serving for maximum GPU utilization

🗄️ Vector Databases

Store, index, and query billions of embeddings for RAG, semantic search, and recommendation systems.

• Qdrant — High-performance vector similarity search with advanced filtering
• Milvus — Cloud-native vector database built for billion-scale workloads
• Weaviate — AI-native database with built-in vectorizers and hybrid search
• ChromaDB — Developer-friendly embedding store for LLM apps and RAG pipelines
• PostgreSQL + pgvector — Familiar relational database with native vector search

🔬 MLOps & Experimentation

Full lifecycle tools for training, evaluating, and deploying machine learning models at scale.

• MLflow — Experiment tracking, model registry, and deployment for any ML framework
• Kubeflow — End-to-end ML platform for building and managing ML workflows on Kubernetes
• Label Studio — Open-source data labeling for text, image, audio, and video datasets
• Weights & Biases (self-hosted) — Experiment tracking, dataset versioning, and model evaluation
• Ray — Distributed computing framework for scaling AI workloads across GPU clusters

🔗 AI Application Frameworks

Deploy the tools your team uses to build, chain, and orchestrate AI-powered applications.

• Langflow — Visual framework for building multi-agent and RAG applications
• Flowise — Drag-and-drop builder for LLM chains, chatbots, and AI agents
• Dify — LLMOps platform for prompt engineering, agent orchestration, and app deployment
• Haystack — Production-ready framework for building NLP and retrieval-augmented pipelines
• LiteLLM Proxy — Unified API gateway to route requests across 100+ LLM providers

📊 Observability & Data Infrastructure

Monitor, trace, and feed your AI systems with production-grade data tooling.

• Langfuse — Open-source LLM observability with tracing, evaluation, and cost tracking
• Apache Kafka — Distributed event streaming for real-time AI data pipelines
• MinIO — S3-compatible object storage for datasets, model weights, and artifacts
• Redis — In-memory caching and message broker for low-latency inference
• Grafana + Prometheus — Full-stack monitoring and alerting for all running containers

Who is Octopod+ For?

• AI/ML engineering teams who need managed infrastructure for model serving and experimentation
• Enterprises running self-hosted LLMs for data privacy, regulatory compliance, or cost control
• Research labs that need GPU clusters and experiment tracking without managing bare metal
• Startups scaling from prototype to production and need reliable MLOps from day one
• Data platforms building search, recommendation, or conversational AI products

How It Works

1. Work with our team or self-serve to select containers for your AI stack
2. Choose your compute tier — CPU, GPU (A100, H100), or high-memory instances
3. Octopod+ provisions your environment with private networking and IAM in under 5 minutes
4. Access everything via dashboard, CLI, or API with full observability built in
5. Scale horizontally, swap models, or add new services — zero downtime, no lock-in

Enterprise Features

• GPU scheduling — Dedicated and shared GPU pools with priority queues for training and inference
• Private networking — All containers communicate over an encrypted mesh with no public exposure
• SOC 2 & GDPR ready — Audit logs, role-based access control, and data residency options
• SLA guarantees — 99.95% uptime with dedicated support and incident response
• Managed updates — Zero-downtime rolling updates with automatic rollback on failure

Pricing

Custom pricing based on your workload. Starts at $99/month for dedicated CPU containers. GPU instances, multi-node clusters, and enterprise support plans are available — contact our team for a tailored quote.