Respan Gateway

Key Features

• •One API for 500+ Models: Route OpenAI-style calls to over 500 models through a single endpoint, simplifying integration and reducing dependency on multiple provider SDKs. This allows teams to switch models without code changes and access a wide variety of models from one place.
• •Fallback and Retry Logic: Automatically try the next model in a fallback list if the primary provider errors or rate-limits, with retry backoff. This ensures high availability and resilience, keeping applications responsive even during provider outages.
• •Spend Control with Alerts: Set soft warnings or hard caps per API key, and receive Slack or email alerts when thresholds are crossed. This gives teams granular control over costs and prevents unexpected bills.
• •Trace Every Call: Each gateway call becomes a trace tree with latency on every span, including customer_identifier and metadata. This enables deep debugging and root cause analysis of individual requests in production.
• •Metrics Dashboard by Model/User: See requests, tokens, errors, latency, and cost in one dashboard, sliceable by model or user. This helps identify traffic patterns, compare performance, and monitor usage across different segments.
• •Evaluation Workflows: Compose flows combining rule checks, LLM judges, and human review in one system. This allows systematic quality assessment and prevents regressions when making changes.
• •Online Evals on Production Traffic: Run evaluators on sampled production logs automatically, so scores like faithfulness and toxicity appear on real spans. This surfaces quality issues in live traffic before users report them.
• •Alerting on Evaluator Scores: Watch evaluator scores over a rolling window and trigger alerts when they fall below thresholds. This enables proactive quality management and rapid response to degradation.

Use Cases

• •Debugging Production Issues Faster: A team running voice agents can use Respan Gateway's traces to pinpoint latency spikes and errors. With thread views linking messages to spans, they resolved issues 10x faster than before, improving uptime and user experience.
• •Scaling API Calls from Millions to Billions: A startup growing from 5M to 500M monthly API calls used Respan's observability layer to monitor performance and control costs. They maintained reliability at scale without adding operational burden.
• •Testing Prompt Variants Before Deployment: A product manager building a customer support agent uses Respan's experiment feature. They create datasets from production traces, run experiments across prompt and model variants, and compare scores to select the best configuration before merging.
• •Monitoring Multi-Model Agent Behavior: An engineering team running an AI agent with multiple model calls uses Respan's online evals on sampled traffic. They detect faithfulness drops in production and get alerted before users notice, allowing rapid remediation.
• •Ensuring Compliance in Regulated Industries: A healthcare startup uses Respan Gateway's HIPAA compliance and audit trails to log all LLM calls. They can reproduce any session for compliance reviews and ensure data privacy while building AI features.

Who is this for?

Software engineers building and maintaining LLM-powered applications, ML engineers evaluating model performance and quality, product managers overseeing AI feature launches, CTOs ensuring reliability and cost efficiency at scale, and AI agent developers needing deep observability into multi-step agent workflows. Respan Gateway is also used by technical founders in startups who need a quick-to-integrate solution for routing, monitoring, and evaluation without building in-house infrastructure. Teams in regulated industries such as healthcare and finance benefit from its SOC 2 and HIPAA compliance.