Experiential Labs NEW

Experiential Labs is an open-source AI gateway that exposes a single API endpoint for routing requests across hosted providers, user-managed keys, and self-hosted GPUs.

It includes an intelligence layer that monitors traffic and selects or routes models per prompt while applying caching to increase repeated-call efficiency.

The gateway supports fine-tuning on captured traffic with validation before that model serves, and it enables per-request routing, failover, and streaming responses. A centralized console provides cataloging, usage and spend visibility, request logs, attribution, and organization-wide limits.

Key management enforces caps, roles, and model allowlists at request time. Integrations cover major cloud and local inference providers, allowing providers and self-hosted models to operate behind one consistent endpoint.

Key Features

• Single API gateway routing requests across hosted providers, user-managed keys, and self-hosted GPUs
• Intelligence layer that monitors traffic, selects or routes models per prompt, and applies caching for repeated calls
• Support for fine-tuning on captured traffic with validation before serving
• Per-request routing with failover and streaming response support
• Centralized console and key management: cataloging, request logs, attribution, usage/spend visibility, org-wide limits, caps, roles, and model allowlists at request time

Use Cases

• Create a resilient, low-latency inference layer for customer-facing AI features that routes requests across hosted providers, user keys, and self‑hosted GPUs with intelligent per-request routing, inference caching, and automatic failover to maintain uptime
• Develop an automated model-improvement pipeline that centralizes logging and usage data, triggers traffic-driven fine-tuning on self-hosted GPUs, and deploys updated models through a unified inference endpoint with centralized access controls and model governance
• Create a multi-tenant AI platform that serves tenant-specific, prompt-routed models, enforces centralized access and usage controls, streams inference responses, and optimizes cost and performance using provider routing and inference caching

Who is it for?

• Mlops engineers
• Machine learning engineers
• Platform engineers
• Devops/sre teams
• Data scientists
• Ai product managers
• Ctos and engineering leaders
• Security, compliance, and it administrators
• Startups and engineering teams building ai features
• Enterprise cloud architects and operations teams