ARBR vs TrueFoundry AI Gateway: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ARBR and TrueFoundry AI Gateway — 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.
TrueFoundry AI Gateway
TrueFoundry
A gateway for deploying, routing, governing and monitoring GenAI workloads with unified access, cost controls and observability.
Key features
- Unified Access Control: Centralized authentication and role-based policy enforcement for model access and API usage across teams and environments, enabling consistent governance.
- Cost-aware DevOps and Budgeting: Per-user and per-team budgeting, usage tracking and cost allocation tools to enforce spend limits and surface cost anomalies for GenAI workloads.
- Provider-agnostic Model Routing: Route requests to multiple model providers or on-prem models via a single gateway layer, with configurable routing rules and fallback strategies.
- Observability and Telemetry: Request-level logging, metrics, traces and dashboards that capture latency, token usage, error rates and model performance for troubleshooting and optimization.
- Developer APIs and UI: RESTful APIs and an interface to integrate coding assistants, RAG pipelines and applications easily while exposing governance and telemetry controls.
- Auditing and Compliance: Persistent audit logs of requests, model choices and policy decisions to support compliance, review and post-hoc analysis.
- Request Orchestration and Enrichment: Support for common RAG workflows where inputs are embedded, retrievers queried, and final answers composed through the gateway with optional enrichment of metadata.
- Unified access control and routing for model and assistant requests
- Developer-friendly REST APIs and web UI for management and governance
- Observability: request logging, metrics, tracing and feedback capture
- Cost-aware DevOps: budgeting, usage tracking and cost controls per user/team
- Integrations with RAG frameworks and retrieval workflows (embeddings, vector DBs)
- Plugs into agentic deployments and MCP/FastAPI servers for production agents
- Infrastructure automation support via Terraform and Kubernetes (EKS) modules
- Documentation and example integrations (Cline, Cognita, Prisma AIRS guides)
Best for
- Routing requests from coding assistants (e.g., in-editor tools) through a centralized gateway to apply access controls, budgeting and observability for developer-facing AI features.
- Running RAG pipelines where user queries are embedded, vector DB retrievers are invoked and LLMs are called via the gateway to capture logs, metrics and feedback.
- Enforcing enterprise governance and compliance by centralizing policy enforcement, audit trails and model selection across multiple teams and environments.
- Cost control and chargeback for GenAI experiments by applying per-team budgets, usage limits and visibility into token/compute consumption.
- Provider-agnostic deployment where applications can switch between cloud-hosted models and on-premise models without code changes by updating gateway routing.
- Integrating security and policy scanning (e.g., Prisma AIRS) into AI workflows to enforce runtime checks and threat detection at the gateway layer.
- Observability-driven optimization: analyze gateway telemetry to reduce latency, detect failing model providers and implement caching or fallback strategies.
- Routing and governing LLM requests from coding assistants (e.g., Cline) with per-user budgeting and observability
- Production RAG pipelines where embeddings/retrievers fetch documents and LLM calls are routed through a monitored gateway
- Deploying and scaling agentic AI services behind a gateway with centralized access control and logging
- Integrating security and policy enforcement into AI workflows via third-party integrations (e.g., Prisma AIRS)
- Embedding TrueFoundry Gateway into microservices stacks using Python SDKs, FastAPI endpoints, or MCP servers