ARBR vs Pylar: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ARBR and Pylar — 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.
Pylar
Pylar
Governed data access layer that lets AI agents query controlled SQL views and MCP tools without exposing raw databases.
Key features
- Governed SQL Views: Create and manage curated SQL views that expose only authorized subsets or transformations of underlying tables, preventing agents from accessing raw database rows or schemas directly.
- MCP Tool Publishing: Package governed views and query endpoints as MCP tools that can be published and deployed to any agent builder, simplifying distribution of controlled data capabilities to agents.
- Fine-Grained Access Control: Enforce policies and permissions at the view or tool level so different agents or agent roles can only run allowed queries and receive permitted fields.
- Secure Query Execution: Route agent queries through a managed execution layer that sanitizes inputs, applies limits and quotas, and prevents unauthorized SQL execution patterns.
- Auditing and Logging: Capture detailed logs of agent queries and access events for compliance, forensics, and monitoring of data usage by agents and tools.
- Database Integrations and Connectors: Connect to existing relational data stores and map schemas into governed views, enabling rapid adoption without migrating source data.
- Create governed SQL views for safe data access
- AI-powered MCP tool creation
- Connect 100+ business tools and databases
- Managed ingestion, ETL, and hosted warehouse
- Cross-database joins and multi-database integration
- Publish and deploy tools to any agent builder
- Built-in observability and control pane
- Create governed SQL views to expose controlled subsets of structured data to agents
- Build MCP-compatible tools that can be deployed into agent builders
- Deployable to any agent builder / agent framework (platform-agnostic integration)
- Secure, scalable access controls for agent-driven queries against databases
- Governance and policy enforcement for data access in agent workflows
- GitHub presence for project assets and workstation tooling (PylarAI organization)
Best for
- Safe Agent Access to CRM Data: Expose a limited, governed view of a customer database so conversational agents can answer customer-specific questions without full DB access or PII exposure.
- Publishable MCP Tools for Agent Platforms: Package analytics or lookup queries as MCP tools and deploy them to multiple agent builders so agents can access standardized data functions.
- Compliance-Focused Data Access: Maintain audit trails and enforce view-level permissions for regulated environments (finance, healthcare) where agent queries must be restricted and logged.
- Operational Dashboards for Agents: Provide agents with curated operational metrics and KPIs from production databases without risking query patterns that could impact performance or reveal sensitive schema.
- Multi-tenant SaaS Data Isolation: Create per-tenant governed views so agents serving different customers can query only their tenant data while using the same underlying infrastructure.
- Prototype and Test Agent Workflows: Rapidly define safe SQL views to let agents prototype data-driven workflows without waiting for heavy engineering changes or database refactors.
- Let AI agents query CRM, billing, and product data without direct DB access
- Build and deploy MCP tools for customer support or sales assistants
- Provide governed data access for agent-driven analytics and reporting
- Host synced business data in a managed warehouse for secure agent usage
- Provide AI/agent workflows safe, governed query access to enterprise SQL databases
- Expose tightly scoped, auditable data views to third-party or internal agents
- Build and deploy MCP connector tools for multi-agent platforms and agent builders
- Enable controlled retrieval for retrieval-augmented-generation (RAG) systems using SQL-backed knowledge sources
- Operationalize data access governance for agent-based automation and assistants