ARBR vs FastRouter.ai: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ARBR and FastRouter.ai — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
ARBR
Gyde & Domkundwar Foundation
Open-source, MIT-licensed AI gateway and control plane that routes, governs and observes every LLM request behind one OpenAI-compatible endpoint.
Key features
- OpenAI-Compatible Routing: A single drop-in endpoint over every major provider, with rules, difficulty-aware selection, cost guardrails and automatic fallback choosing the model per request.
- In-Path Governance: Budgets, rate limits, output guardrails, prompt-injection checks and kill switches enforce policy before inference rather than auditing it afterwards.
- Structured Observability: Cost, latency, tokens and routing decisions are emitted as structured events attributed by application, team, model and user, viewable in local dashboards or exported to OpenTelemetry backends such as Datadog, Grafana and Prometheus.
- LLM-Judge Evaluation: A sample of live traffic is scored for quality so requests can be routed to the cheapest model that provably clears the bar, rather than optimising on price alone.
- Safe Model Deployment: Canary and shadow new models against real traffic with regression gates that block promotion until evaluations pass, plus instant rollback.
- Broad Provider Coverage: One layer over Anthropic, OpenAI, Google Gemini, Amazon Bedrock, Azure OpenAI, Vertex AI, Groq, DeepSeek, Moonshot, xAI and Mistral, plus LiteLLM and NVIDIA NIM, with pricing and benchmark data for over 3,000 models.
- Drop-In SDK Compatibility: Change only the base URL and existing OpenAI SDKs, agent frameworks and chat UIs keep working, gaining streaming chat completions, embeddings, a realtime voice proxy and JavaScript and Python SDKs.
- Self-Hosted and MIT Licensed: The full control plane runs inside your own infrastructure under an MIT licence, with a hosted option available for teams that do not want to operate it.
Best for
- LLM Cost Reduction: Route summarisation and extraction traffic to cheap small models while reserving frontier models for analysis, cutting spend without hand-editing every call site.
- AI Spend Attribution: Give finance and engineering a per-application, per-team and per-user breakdown of token spend so AI budgets can be owned by the groups that generate them.
- Enterprise AI Governance: Enforce departmental budgets, rate limits and kill switches in the request path so a runaway agent cannot exhaust a quarter's inference budget.
- Provider Risk Mitigation: Keep applications provider-neutral behind one endpoint with automatic fallback, so a single vendor outage or price change does not require a code change.
- Model Migration Testing: Shadow or canary a newly released model against production traffic and let regression gates decide whether it is promoted.
- Prompt-Injection Defence: Apply output guardrails and prompt-injection checks centrally for every application instead of reimplementing them per service.
FastRouter.ai
FastRouter
Enterprise LLM gateway that routes, compares and monitors 200+ models through a single OpenAI-compatible API with zero markup.
Key features
- Unified OpenAI-Compatible API: Access 200+ models for text, image, video, embeddings and speech from one endpoint and swap models without code changes.
- Smart Auto Router: Routes each request to the best model based on cost, latency, throughput or output quality.
- Automatic Failover: Retries across providers, fallback model lists and virtual model aliases keep applications running during failures.
- Cost Control and Governance: Project and API key limits, member roles and access controls prevent spend spikes.
- Model Council and Playground: Compare latency, quality and cost side by side and combine models to cross-check outputs.
- Observability and Insights: Unified dashboards, activity logs, alerts and weekly Insights that rank cost-saving changes.
- Self-Hosting: Run the full gateway in your own cloud so prompts, responses, logs and provider keys stay in your network.
Best for
- An engineering team replaces several provider SDKs with one OpenAI-compatible endpoint to simplify integration.
- A product team cuts LLM spend by routing simple requests to cheaper models and reserving premium models for hard ones.
- A platform team keeps a customer-facing app online by defining fallback model lists across providers.
- A finance-conscious leader sets per-project and per-key limits to avoid surprise bills.
- A regulated enterprise self-hosts the gateway so prompts and provider keys never leave its network.
- A developer compares candidate models for latency, quality and cost in the playground before shipping.