Experiential Labs vs Progress AI Observability: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Progress AI Observability — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.
Progress AI Observability
Progress Software (Telerik)
Progress AI Observability traces, debugs, cost-tracks and evaluates AI agents in production for .NET, Python and JavaScript.
Key features
- AI Trace Explorer: Capture every span across prompts, model calls, tool calls and retrieval steps, with latency, tokens and outputs.
- Workflow Debugging: Diagnose failed spans, skipped tools, retries and cascading failures with agent-specific debugging context.
- Cost Analysis: Attribute LLM spend to specific models, providers, agents and workflows so teams can optimize before it scales.
- LLM-as-a-Judge Evaluations: Run quality, usefulness and policy-alignment scoring on captured traces and compare prompt/model changes.
- Multi-Language SDK: Instrument .NET, Python and JavaScript apps with a few lines of code — first trace in under 5 minutes.
- Datasets & Experiments: Curate real traces into datasets and run repeatable experiments against new prompts or models.
- Enterprise Governance: SSO, retention controls, data residency options and audit trails for regulated teams.
Best for
- Agent Failure Debugging: Cut root-cause analysis from hours to minutes by tracing where a run broke across prompts, retrieval and tools.
- LLM Cost Governance: Identify token-hungry patterns, expensive models and retry loops so finance and engineering can budget accurately.
- Quality Regression Testing: Score outputs with LLM judges before and after prompt/model changes to catch quality drops pre-release.
- RAG Pipeline Tuning: Spot bad retrieval or stale context inside multi-step RAG workflows and iterate with real production evidence.
- Enterprise AI Governance: Maintain trace history, evaluation records and access controls needed to scale AI to regulated business lines.
- Multi-Agent Observability: Compare behavior, cost and quality across agents, environments and providers from a single dashboard.