Loomal vs Progress AI Observability: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Loomal and Progress AI Observability — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Loomal
Loomal
Payments layer for agentic commerce — paywall any API, MCP tool, or store so AI agents can pay in USDC on Base per request.
Key features
- Five-Line Paywall SDK: Wrap any Express, Hono, Next.js, or FastAPI handler with requirePayment to charge agents per call.
- x402 Protocol Support: Uses HTTP 402 Payment Required as a real payment rail, so auth and payment happen in one round trip with no API keys.
- USDC on Base Settlement: Payments settle on-chain in seconds; sellers keep custody of funds in their own wallet.
- Per-Request Micropayments: Charge anywhere from a tenth of a cent to a dollar per call, enabling models the card networks cannot serve.
- Signed Receipts: Every sale returns an Ed25519 receipt sellers can verify offline for provable, auditable revenue.
- Hosted Endpoint Option: Paste JSON or upload a file and Loomal will host and paywall it at a URL agents can pay to hit.
- Marketplace Discovery: A public marketplace lets agents discover paid APIs and MCP tools hosted through Loomal.
- Broad Agent Compatibility: Works with any agent runtime that speaks x402 — Claude, GPT, Gemini, LangChain, CrewAI, MCP clients.
Best for
- Monetizing an API: Turn a paid tier of a REST API into per-call micropayments that agents can buy without human onboarding.
- Selling MCP Tools: Charge for premium MCP tools that agents in Claude Code, Cursor, or Windsurf install.
- Data Vendor Distribution: Let agents buy scraped or curated datasets per query with no contracts or seats.
- Hosted Content Paywalls: Sell access to a hosted JSON endpoint or uploaded file to agents that discover it in Loomal's marketplace.
- Storefront Access (Coming): Add agentic checkout to a Shopify or WooCommerce store so AI shopping agents can transact directly.
- SaaS Usage Billing: Bill agent traffic per action instead of per seat, aligning revenue with actual agent consumption.
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.