FAQs
Accelerate Your AI Journey with Confidence.
A: Public clouds provide “raw compute,” while WhaleFlux provides “production readiness.” Beyond eliminating complex driver configurations, our Intelligent Scheduling Engine boosts GPU utilization from the industry average of 30% to over 85% by optimizing fine-tuning and inference workloads.
A: Minutes. WhaleFlux provides an OpenAI-compatible API gateway. You can keep your existing prompt logic and simply update the Base URL to point to your private Llama 3 or DeepSeek models fine-tuned and hosted on WhaleFlux.
A: We use a Hybrid Inference Architecture. For real-time tasks, we utilize optimized quantized models on edge nodes. For high-precision tasks, the system automatically routes requests to H100 clusters for FP16 full-precision inference.
A: Yes. WhaleFlux is built for Hybrid & Multi-Cloud Orchestration. You can perform domain-specific fine-tuning where your data resides (on-site) while utilizing our automated scaling to push optimized models to global edge nodes for low-latency access.
A: WhaleFlux users typically move from “Model Selection” to a “Production-ready Agent” in under 48 hours by using our pre-configured MCP toolkits and automated RAG pipelines.
A: No. WhaleFlux provides Pre-emptive Self-healing. The system triggers an automated checkpoint and live-migrates the container to a healthy node. Your fine-tuning resumes from the last save point rather than starting from zero.
A: We feature a built-in Fair-share Scheduler. You can define priorities for different projects. The system intelligently partitions GPUs (using NVIDIA MIG) to ensure mission-critical inference services maintain stable latency even during compute-heavy fine-tuning tasks.
A: Yes. WhaleFlux includes Semantic Drift Detection. If the confidence score of a model output drops or hallucination frequency increases, the system triggers an alert and suggests a model version switch.
A: We use Predictive Pre-loading & Model Caching. Our Observability layer anticipates spikes and utilizes high-speed NVMe storage to load model weights into VRAM in seconds, eliminating the “first-request lag.”
A: Absolutely. WhaleFlux supports OpenTelemetry (OTel) standards. You can stream all infrastructure metrics and application traces to platforms like Datadog or Splunk for centralized IT governance.
A: Yes. WhaleFlux features an integrated ETL Data Pipeline. It automatically parses and labels your unstructured data into formats ready for SFT (Supervised Fine-Tuning) without needing a dedicated data labeling team.
A: We employ a Dual-layer Validation Mechanism. Before an agent executes a tool, it passes through a Policy Filter. Post-execution, the system performs an output alignment check to prevent unintended automated actions.
A: Through our Dynamic RAG engine. Simply mount your data sources to the Knowledge Base. Agents retrieve the latest context in real-time, which is 100x faster and more cost-effective than frequent fine-tuning.
A: We offer Automated Shadow Testing. When you update a model, you can run it in “Shadow Mode” alongside your production version to compare accuracy and safety metrics before switching live traffic.
A: Yes. You can configure Policy-based Approval Gates. For sensitive actions, the agent will pause and request explicit authorization from a human supervisor via the dashboard or API.
A: Strictly NO. WhaleFlux is a platform provider, not a model builder. We operate on a Zero-Access Architecture. Your data is encrypted, and all fine-tuned model weights belong exclusively to you.
A: Yes. For extreme compliance, we support Air-gapped Deployment. You can install the entire platform on internal private servers, managing GPU clusters and agents without any public internet connection.
A: Yes. Within the AI Observability module, you can export full-stack audit reports covering every action—from GPU access and fine-tuning logs to agent interaction histories.
A: We leverage Hardware-level Encrypted Memory (Confidential Computing). This ensures that data being processed inside the GPU is encrypted, preventing other tenants on the same physical machine from accessing your VRAM data.
Absolutely. Customers typically achieve 90% faster problem identification thanks to our correlated monitoring that connects infrastructure issues to AI workflow impacts, plus instant multi-channel alerts.