Managing multiple LLMs across distributed teams
Engineering teams are actively bypassing centralized infrastructure to ship AI features faster. Product pods procure their own API keys, support engineers write bespoke scripts for different providers, and internal tools run on unmanaged endpoints. This intentional decentralization speeds up initial development, but quickly breaks production visibility by creating overlapping, fragmented routing layers.
Platform teams absorbing fragmented architectures
As these independently built AI features graduate to production, platform engineering is forced to support multiple brittle, bespoke integrations.
- 01
Product engineering hardcodes provider API keys directly into core application logic to unblock a feature release.
- 02
Support teams write isolated backend scripts to orchestrate a completely different model provider.
- 03
Internal tools teams stand up autonomous agents reliant on unmanaged endpoints without notifying DevOps.
- 04
When an endpoint deprecates or latency spikes, platform teams have to manually hunt down and patch integration logic across multiple distinct codebases.
Consolidate AI operations into a unified platform layer
- Centralize API key management, rate limiting, and provider orchestration in a single control plane.
- Standardize traffic routing and fallback logic across the entire organization without requiring code rewrites.
- Give engineering leadership real-time observability into org-wide AI utilization, latency, and system health.
Considering a trial phase or evaluation?
Get in touch with our team to discuss your architecture.