Solutions
Start from the problem, not the product
RootInference is applied to the problems that actually break AI products in production — lock-in, outages, runaway spend and un-comparable models.
AI startups
“Betting the product on one provider's models, pricing and rate limits.”
Ship on frontier models without provider lock-in. One OpenAI-compatible integration reaches 500+ models, so you can adopt whatever tops the leaderboard this week, negotiate with real alternatives, and never rewrite your inference layer again.
How it works
Enterprise AI platforms
“Every team signing up for a different model vendor with no governance, no shared spend view and five invoices.”
Give platform teams one gateway for the whole organisation: centrally managed keys, per-team budgets and spend attribution, provider redundancy by default, and one bill covering OpenAI, Anthropic, Google, Meta and the rest.
How it works
Production reliability
“Mission-critical LLM features going down whenever a single provider has an incident or throttles you.”
RootInference health-checks a pool of providers continuously and reroutes automatically when one degrades — errors, rate limits or latency spikes. Your uptime becomes the union of many providers instead of the weakest one.
How it works
AI agents
“Long-running agent workflows that die mid-run on a rate limit or provider error.”
Agentic workloads hammer APIs with bursty, tool-heavy traffic. RootInference normalises tool calling across providers, arms fallback chains so a step retries on another provider instead of failing the run, and routes each step to the cheapest model that can handle it.
How it works
Cost optimisation
“Inference bills growing faster than usage, with no idea which requests actually need frontier pricing.”
Route economy models by default and escalate to frontier only when needed — routing policies do it per request, and unified spend attribution shows exactly what each team and feature costs. And unlike gateways that quietly expire balances in 30–90 days, every RootInference credit stays valid a full 12 months, so prepaid volume discounts never turn into breakage.
How it works
RAG & search
“High-volume, long-context workloads that are ruinously expensive on frontier models.”
Retrieval pipelines generate millions of large-context requests where economical models perform nearly as well as frontier ones. Run embedding, reranking and synthesis on the cheapest capable model per step, through one API, and swap models as the price-performance frontier moves.
How it works
Chatbots & copilots
“Conversational UX that lives or dies on time-to-first-token — and dies whenever the provider does.”
Route every message to the fastest healthy provider serving your model class, with live latency telemetry deciding per request. If a provider degrades mid-conversation, traffic shifts automatically and your users never see a spinner turn into an error.
How it works
Model evaluation
“Comparing models means five SDKs, five API keys, five billing accounts and un-comparable logs.”
Evaluate 500+ models through one interface: identical request shapes, unified latency and cost metrics, and A/B comparison on real production traffic. Pick models on your data and your numbers, not vendor benchmarks.
How it works