Automatically route every AI request to the right model at the right cost — without sacrificing quality or rebuilding your stack.
Most organizations deploy one LLM and pay premium prices for every request — whether it's a complex reasoning task or a simple FAQ lookup. That's like running every workload on your most expensive server.
The ibl.ai Model Router is a core infrastructure component of the AI Operating System. It sits between your applications and every LLM provider, dynamically selecting the optimal model for each request based on task complexity, latency requirements, and cost thresholds.
The result: 40–70% reduction in AI inference costs with no degradation in output quality. This isn't a standalone app — it's the routing layer that every agent, workflow, and AI application on the ibl.ai OS runs through automatically.
Enterprises scaling AI face a hidden cost crisis. Every request — from a one-word autocomplete to a multi-step legal analysis — gets routed to the same premium model. Teams have no visibility into per-request cost, no control over model selection, and no mechanism to match model capability to task complexity.
Without intelligent routing infrastructure, AI budgets balloon unpredictably, procurement teams lose confidence, and engineering teams are forced to manually hardcode model choices per use case. This creates brittle, expensive systems that can't adapt as new models emerge or pricing shifts — and it locks organizations into a single vendor with no fallback.
Teams default to a single flagship LLM for all tasks, regardless of whether the request requires deep reasoning or a simple classification.
Organizations overpay by 3–10x on routine tasks that could be handled by smaller, cheaper models with equivalent output quality.AI inference costs are opaque. There's no per-request attribution, no budget guardrails, and no alerting when spend spikes unexpectedly.
Finance and engineering teams operate blind, leading to budget overruns, project cancellations, and loss of executive confidence in AI initiatives.Hardcoding a single LLM provider into application logic creates deep dependency. Any pricing change, outage, or capability gap requires expensive re-engineering.
Organizations lose negotiating leverage, face availability risk, and can't adopt superior models as the market evolves.As AI use cases multiply, engineering teams manually configure model choices per workflow, per agent, and per application — a process that doesn't scale.
Engineering velocity slows, configuration debt accumulates, and model selection decisions become inconsistent across teams and products.Teams make model selection decisions based on intuition rather than real-time performance data, latency benchmarks, or task-specific quality metrics.
User-facing applications suffer from either unnecessary latency (over-powered models) or poor output quality (under-powered models) — both eroding trust.Every AI request — from any agent, workflow, or application running on the ibl.ai OS — passes through the Model Router before reaching any LLM. No application-level changes required.
The router analyzes each request in real time: prompt length, task type (reasoning, generation, retrieval, classification), required output format, and context window needs.
Routing policies — defined by your platform team — map task profiles to model tiers. Complex reasoning routes to Claude or GPT-4. Generation tasks to GPT-3.5 or Gemini. Simple lookups to Llama or Mistral.
Before dispatching, the router scores candidate models on current pricing, observed latency, and availability. It selects the optimal model that satisfies quality requirements at minimum cost.
If the primary model is unavailable or exceeds latency thresholds, the router automatically fails over to the next best option — maintaining uptime without manual intervention.
Every routed request is logged with model used, tokens consumed, latency, and cost. Data flows into the ibl.ai observability dashboard for per-tenant, per-agent, and per-workflow cost reporting.
Native connectors to OpenAI, Anthropic, Google Gemini, Meta Llama, Mistral, Cohere, and custom self-hosted models. Add new providers without changing application code.
Define routing logic using declarative policies: route by task type, cost ceiling, latency SLA, compliance requirement, or tenant-specific preferences. No hardcoding required.
Configurable fallback chains ensure continuity when a provider is degraded or unavailable. The router retries with the next best model transparently, preserving user experience.
Every inference call is tagged with tenant, agent, workflow, and user identifiers. Finance and engineering teams get granular cost breakdowns — not just aggregate spend.
Real-time latency monitoring per provider informs routing decisions. Time-sensitive user-facing requests are routed to the fastest available model within quality constraints.
Set hard spending limits per tenant, per agent, or per time period. The router enforces caps and triggers alerts before budgets are breached — not after.
Route a percentage of traffic to a new model for quality benchmarking before full rollout. Compare output quality, latency, and cost across models using production traffic.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Model Selection | Hardcoded to a single LLM provider across all use cases — no flexibility | Dynamic, policy-driven selection from 10+ providers per request based on task profile |
| AI Inference Cost | Premium model pricing applied to every request regardless of complexity | 40–70% cost reduction by matching model tier to actual task requirements |
| Cost Visibility | Aggregate monthly bills with no per-request, per-agent, or per-tenant attribution | Granular cost attribution by tenant, agent, workflow, and user in real time |
| Vendor Risk | Deep lock-in to a single provider — outages or price changes cause immediate disruption | Automatic failover across providers — no single point of failure, full negotiating leverage |
| Engineering Overhead | Manual model configuration per application, per workflow, per team — doesn't scale | Centralized routing policies managed by platform team — zero per-application configuration |
| New Model Adoption | Adopting a new LLM requires re-engineering application integrations across the stack | Add a new model provider via connector — routing policies automatically leverage it |
| Budget Control | No guardrails — AI spend can spike without warning until the invoice arrives | Hard budget caps and real-time alerts enforced at the infrastructure layer before overruns occur |
Hardcoded to a single LLM provider across all use cases — no flexibility
Dynamic, policy-driven selection from 10+ providers per request based on task profile
Premium model pricing applied to every request regardless of complexity
40–70% cost reduction by matching model tier to actual task requirements
Aggregate monthly bills with no per-request, per-agent, or per-tenant attribution
Granular cost attribution by tenant, agent, workflow, and user in real time
Deep lock-in to a single provider — outages or price changes cause immediate disruption
Automatic failover across providers — no single point of failure, full negotiating leverage
Manual model configuration per application, per workflow, per team — doesn't scale
Centralized routing policies managed by platform team — zero per-application configuration
Adopting a new LLM requires re-engineering application integrations across the stack
Add a new model provider via connector — routing policies automatically leverage it
No guardrails — AI spend can spike without warning until the invoice arrives
Hard budget caps and real-time alerts enforced at the infrastructure layer before overruns occur
Institutions like learn.nvidia.com serve millions of learners at scale while keeping per-session AI costs within budget — without degrading learning outcomes.
Platform teams enforce cost policies centrally while individual product teams retain flexibility. AI spend becomes predictable and attributable to business units.
Healthcare organizations reduce AI infrastructure costs while ensuring that patient-facing and clinical workflows always use models that meet accuracy and compliance standards.
Financial institutions maintain SOX and regulatory compliance on sensitive workflows while dramatically reducing costs on high-volume, lower-stakes interactions.
Government organizations meet data sovereignty and compliance requirements without sacrificing the cost efficiency benefits of multi-model routing.
Startups extend their AI runway by 40–70% and can compete with larger organizations by accessing enterprise-grade routing infrastructure from day one.
Retailers handle millions of daily AI interactions cost-effectively, with premium model capacity reserved for high-value personalization and conversion-critical workflows.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.