Respan - Complete AI observability from tracing to deployment

What Is Respan

As AI applications scale in complexity, developers face a fundamental challenge: understanding what happens inside their AI systems. Traditional debugging tools fall short when dealing with multi-step AI agents, complex prompt chains, and production traffic that involves hundreds of thousands of LLM calls daily. The result is reactive troubleshooting, undetected quality drift, and escalating operational costs.

Respan (formerly Keywords AI) addresses these challenges by providing a complete AI observability platform that spans the entire AI development lifecycle. The platform enables teams to trace, evaluate, optimize, and deploy AI agents through a unified interface, transforming how organizations build and maintain production AI systems.

The platform has processed over 80 trillion tokens and serves as the infrastructure backbone for leading AI companies including Retell AI, Mem0, AlphaSense, and Gumloop. Backed by Y Combinator, Respan has demonstrated the scalability required to support enterprise-grade AI operations, with customers handling over 1 million requests per hour.

Respan's core value proposition lies in its unified approach. Rather than cobbling together multiple tools for logging, evaluation, and deployment, teams access a single platform that handles the complete workflow from development to production. The AI Gateway provides unified access to over 500 models, eliminating the complexity of managing multiple API integrations while providing built-in observability.

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Core Capabilities of Respan

Respan delivers a comprehensive suite of capabilities designed for AI engineering teams that need visibility, control, and reliability in production AI systems.

Tracing provides end-to-end execution path visualization for every prompt, tool call, and response. The system handles over 1 million logs per hour through asynchronous recording, enabling teams to search and filter by content, latency, cost, quality, tags, and custom metadata. Production traces can be replayed in the Playground for debugging, and converted into evaluation datasets for continuous quality assessment.

Evaluate integrates code-based checks, human review, and LLM-as-judge into a unified evaluation workflow. Teams define metrics and run assessments against versioned datasets built from production traces. The comparison feature enables A/B testing of prompts, models, and releases against baseline configurations.

Optimize provides complete version control for prompts, tools, models, and workflows. Teams test changes against real production baselines and can optimize across prompts, tools, and orchestration patterns. Every modification is tracked with full history for audit and compliance.

Deploy simplifies production release through the AI Gateway. Teams push prompt and workflow versions directly from the UI, with release gating, behavior comparison, and instant rollback capabilities. The gateway supports 500+ models with flexible routing and load balancing.

Monitor offers 80+ chart types for custom dashboards tracking quality, cost, and latency. Alerting supports Slack, email, and SMS with automated workflow triggers. Online evaluation samples production behavior for real-time quality monitoring.

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Who Uses Respan

Respan serves AI development teams across various scales and use cases, from early-stage startups to enterprise organizations.

Large-scale Voice AI Monitoring represents one of Respan's most demanding use cases. Retell AI, a voice AI platform, processes over 1 million requests per hour with each phone call involving dozens of LLM generations. Using Respan's thread_identifier feature, they link all LLM generations to specific phone calls, achieving complete call visibility. The platform's asynchronous logging handles this volume effortlessly while maintaining sub-second query performance for debugging.

Production Issue Resolution is transformed through real-time monitoring and alerting. Teams configure alerts for fallback events, retries, invalid JSON responses, and stream timeouts. When production issues arise, the detailed trace data enables 10x faster root cause analysis compared to traditional logging approaches.

Evaluation and Training Data Preparation becomes streamlined through batch export capabilities. Teams export production logs in JSONL or CSV format with custom attribute filtering, reducing dataset preparation time by 90%. This accelerates the feedback loop between production monitoring and model improvement.

Cross-model Comparison and Selection is enabled through the unified gateway. Teams run baseline comparison tests across multiple models, making data-driven decisions about model selection based on quality, cost, and latency trade-offs.

Prompt Version Management provides complete audit trails and instant rollback. When issues occur, teams restore previous versions with a single click, eliminating the risk of prolonged degradation from problematic prompt changes.

Enterprise Security Compliance is addressed through comprehensive certifications. Organizations requiring SOC 2, HIPAA, or GDPR compliance can leverage Respan's certified infrastructure with appropriate business associate agreements.

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Technical Architecture and Integration

Respan's architecture is built for AI engineering teams that require flexibility, reliability, and deep integration capabilities across their entire tech stack.

Model Integration spans over 500 models from major providers including OpenAI, Anthropic, OpenRouter, Groq, Fireworks, Together AI, Azure, AWS Bedrock, and Google Gemini. The unified AI Gateway aggregates these into a single API endpoint, eliminating the overhead of managing multiple provider credentials and SDKs. Built-in features include key vault (BYOK), request caching, automatic retry with fallback, load balancing, and spending/rate limits.

Framework Support covers over 40主流 frameworks, reflecting the diverse landscape of AI development tools. This includes LangChain, Vercel AI SDK, OpenAI Agents SDK, Mastra, LlamaIndex, Google GenAI, Mem0, Cognee, and Agno. The breadth of integration ensures teams can adopt Respan regardless of their architectural choices.

SDK Availability includes Python and JavaScript/TypeScript, covering the majority of AI development workflows. Both SDKs support OpenTelemetry for teams requiring custom instrumentation and detailed performance profiling.

Technical Capabilities extend to multi-modal support for images and voice, async logging for high-throughput scenarios, and flexible metadata handling for custom attributes. The platform's architecture supports elastic scaling to handle traffic spikes without degradation.

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Pricing Structure

Respan offers a tiered pricing model designed to support teams at every stage of their AI development journey, from initial prototyping to enterprise-scale production deployment.

Pro Plan provides an accessible entry point for developers establishing AI observability practices. The 100,000 log capacity and basic evaluation features enable teams to understand their AI behavior without initial investment. This plan suits early-stage projects and proof-of-concept development.

Team Plan at $199 per month (billed annually) unlocks the platform's full potential for production AI systems. Unlimited datasets, evaluators, and prompts remove constraints as AI applications grow. The private Slack channel provides direct access to the Respan team, while SOC 2 reports support security review processes. The 8,400 requests per minute gateway capacity handles substantial production traffic.

Enterprise Plan delivers customized arrangements for large-scale deployments. Custom SLAs provide contractual reliability guarantees, while dedicated support engineers ensure rapid issue resolution. The HIPAA Business Associate Agreement (BAA) enables covered entities to leverage Respan for protected health information workflows. Volume discounts recognize the economies of scale in large deployments.

Usage-based Billing applies beyond plan limits: $8 per 100,000 logs and $1 per 1,000 evaluation scores. This metering ensures predictable costs while allowing flexibility for variable workloads.

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Frequently Asked Questions