ARBR vs Fullstory: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of ARBR and Fullstory — 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.
Fullstory
FullStory
Behavioral data platform that records user sessions and surfaces sentiment and insights to improve product experience.
Key features
- Session Replay: Continuously records user sessions (clicks, scrolls, page transitions, input) into replayable, timestamped videos with direct links to review exact user interactions for debugging and UX analysis.
- Behavioral Event Capture: Captures rich event streams and structured custom events with properties and schemas, enabling teams to track actions, attributes, and conversion steps across web and mobile.
- SDKs & APIs: Provides official Browser, NodeJS, and mobile SDKs plus HTTP/server APIs for initializing capture, tracking custom events, creating users/events, and programmatic access to session and segment data.
- Searchable Segments & Event Querying: Lets users search and filter sessions by custom event properties, user attributes, and behavior to build segments of users who experienced specific flows or issues.
- Warehouse & Data Integration: Offers tooling and packages (including an official dbt package and export utilities) to move FullStory events into data warehouses and integrate behavioral data into BI workflows.
- Privacy Controls & Data Governance: Includes privacy and masking controls, allowlists/deny-lists, and per-session privacy settings to protect sensitive user data while still collecting actionable behavioral signals.
- Developer Tooling & Middleware: Supplies middleware (e.g., Segment middleware), SPA-friendly browser SDKs, and client/server libraries to simplify integration into single-page apps, mobile apps, and analytics stacks.
- Session replay capturing full user interactions (clicks, scrolls, inputs) for playback
- Custom event tracking with schema overrides and searchable events
- Browser SDK (@fullstory/browser) with init() and FullStory global/API (FS) usage
- Server-side NodeJS SDK for server-to-server HTTP API calls (users, events, batch import jobs)
- Mobile SDKs and mobile-specific APIs (iOS, Android, React Native)
- HTTP APIs and developer documentation for capturing data, retrieving sessions, and managing privacy
- Integrations and middleware (e.g., Segment middleware for Android) to forward events and embed replay links
- Data export and warehouse integrations (Anywhere: Warehouse) and a dbt package for downstream analytics
- Real-time activation integrations (Anywhere: Activation) for driving experiences
- Privacy controls and mobile-only methods (setting/removing attributes, resetting idle timer, class management)
Best for
- Bug reproduction and triage: Engineers and QA replay exact user sessions to reproduce errors, observe the sequence of actions leading to a bug, and correlate with console or custom event data.
- Conversion funnel optimization: Product teams identify where users drop off in multi-step flows by filtering sessions and events, then iterating on UX changes informed by real behavior.
- Product research and UX validation: Researchers observe real user interactions and sentiment patterns to validate hypotheses, discover friction points, and prioritize features or design changes.
- Analytics enrichment and BI: Data teams export FullStory events to warehouses using the dbt package and export utilities to join behavioral data with product and transactional datasets for deeper analysis.
- SPA and mobile instrumentation: Developers integrate the FullStory Browser SDK or mobile SDKs (and Segment middleware) to ensure accurate session capture in single-page apps and native mobile applications.
- Customer support augmentation: Support teams attach session replay links to tickets, quickly see what a user experienced, and provide faster, more context-aware resolutions.
- Feature rollout monitoring: Product and engineering monitor adoption and unexpected behaviors after releases by tracking custom events and segmenting affected sessions or cohorts.
- Product analytics and feature usage measurement via custom events and properties
- UX research and usability testing using session replay to reproduce user flows and issues
- Customer support and troubleshooting by locating sessions that match search criteria and viewing replays
- Conversion funnel analysis by correlating events and session behaviors
- Data engineering and analytics: exporting FullStory events to data warehouses and modeling with dbt
- Real-time personalization and activation by integrating FullStory session context into downstream systems
- Server-to-server automation for user and event management using the NodeJS SDK and HTTP APIs