ARBR vs Influencity: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ARBR and Influencity — 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.
Influencity
Influencity
All-in-one influencer marketing platform to discover, manage, analyze, and optimize influencer campaigns for brands and agencies.
Key features
- Influencer Discovery: Search and filter influencers by platform, audience metrics, and other criteria to identify suitable creators for campaigns.
- Campaign Management: Centralized tools to create, manage, and coordinate influencer campaigns and workflows across teams and creators.
- Audience & Performance Analytics: Measure influencer audience demographics, engagement metrics, and campaign performance for data-driven selection and optimization.
- Reporting & Dashboards: Generate campaign reports and visual dashboards to track KPIs, engagement, and ROI for stakeholders.
- Relationship Management: Store influencer contact information, communication history, and campaign agreements to manage long-term partnerships.
- Collaboration & Approvals: Team-oriented features to review creative content, approve deliverables, and coordinate campaign tasks across stakeholders.
- Benchmarking & Comparison: Compare influencer performance and campaign results to benchmarks and past campaigns for continuous improvement.
- Influencer search and discovery (find influencers and profile metrics)
- Campaign building and workflow management
- Influencer metrics analysis and audience insights
- Campaign performance measurement and reporting
- Collaboration tools for brand-influencer workflows
- Cross-platform social media support for measuring engagement and impact
- Exportable reports and analytics to evaluate ROI
Best for
- Product Launch Campaigns: Identify and engage a set of creators whose audiences match the product target to boost awareness and conversions during a launch.
- Agency Multi-Client Management: Run and monitor influencer programs for multiple clients from a single platform with separate workflows and reporting.
- Performance Optimization: Track campaign KPIs in real time, compare influencer performance, and reallocate budgets to higher-performing creators.
- Influencer Vetting: Analyze audience metrics and engagement data to vet creators and reduce the risk of low-quality audiences or poor-fit partnerships.
- Reporting to Stakeholders: Produce consolidated reports and dashboards summarizing campaign results, KPIs, and ROI for brand teams and executives.
- Long-term Partnership Building: Maintain CRM-style records of influencer relationships to streamline recurring collaborations and negotiations.
- Finding and vetting influencers for brand collaborations
- Designing and managing influencer marketing campaigns end-to-end
- Measuring campaign performance and calculating ROI
- Analyzing influencer audiences and engagement metrics
- Reporting campaign results to stakeholders and optimizing future campaigns