AI tool comparison
Kampala vs Metrics SQL by Rill
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Developer Tools
Kampala
MITM proxy that reverse-engineers any app into a stable, callable API
75%
Panel ship
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Community
Free
Entry
Kampala, built by Zatanna AI (YC W26), is a macOS proxy tool that sits between your applications and the internet, intercepts every HTTP/HTTPS request, and automatically reverse-engineers the underlying API. It traces authentication chains — tracking tokens, cookies, and session state — and replays flows on demand, preserving original TLS fingerprints so services can't distinguish API calls from the real app. The key insight is that almost every app that lacks a public API still has a private one — and it's usually more stable than the UI. Kampala targets automation engineers, QA teams, and AI agent builders who need reliable machine-readable access to apps that haven't opened their APIs. Setup is a local MITM cert install; no cloud proxy involved. Currently macOS-only with a Windows waitlist. The team emerged from YC's Winter 2026 batch with backing from Y Combinator. Pricing is in early access, with a free tier planned for solo developers and paid plans for teams building production automations.
Developer Tools
Metrics SQL by Rill
One SQL semantic layer so AI agents stop hallucinating your KPIs
75%
Panel ship
—
Community
Paid
Entry
Metrics SQL is a SQL-based semantic layer from Rill Data that solves a specific and painful problem: AI agents that query your data warehouse tend to hallucinate aggregation logic, producing metrics that look plausible but are mathematically wrong. Metrics SQL lets analysts define business metrics once — revenue, MAU, conversion rate, ROAS — in a governed definition layer, and then exposes those definitions as queryable SQL tables. Every dashboard, notebook, and AI agent resolves from the same source. The technical approach is elegant: rather than inventing a new DSL, Metrics SQL extends SQL itself. An agent that knows SQL can query `SELECT * FROM metrics.weekly_revenue` and get correctly computed numbers without needing to know how revenue is defined, which tables it joins, or how edge cases like refunds are handled. The semantic layer intercepts the query, applies the governed definition, and returns correct results. The implications for AI-native data stacks are significant. Currently, one of the biggest failure modes for AI analysts and BI agents is inconsistent metric computation — different agents or dashboards produce different numbers for 'revenue' because they implement aggregation logic differently. Metrics SQL addresses this at the infrastructure level, not by improving agent prompting.
Reviewer scorecard
“This is the tool I've been building in-house at three different companies and never had time to productize properly. The auth chain tracing alone — tracking token refresh flows and session state automatically — would have saved me hundreds of hours. If it works as advertised, it's an instant ship for anyone doing integration work.”
“We've been burned by data agents that invent their own GROUP BY logic and produce wrong numbers that look right. Metrics SQL solves this at the infrastructure level — define revenue once, have every agent query the same definition. The SQL-native interface means no new tools for agents to learn; they just use the tables.”
“Terms of service violations are a real concern here. Most apps explicitly prohibit automated access through their private APIs, and companies like LinkedIn and Instagram have sued over exactly this pattern. The MITM cert requirement also opens a broad attack surface. Wait for a clearer legal stance before building production systems on this.”
“The value here is only as good as how well-maintained your metric definitions are — if analysts don't keep them updated, agents query stale or wrong definitions and you've added a layer of false confidence. Adopting a semantic layer also creates vendor dependency; migrating away from Rill's cloud later is a real switching cost. For smaller teams without dedicated data engineering, maintaining a semantic layer is overhead.”
“The long-term story here is about AI agents needing reliable access to every app humans use. We can't wait for every SaaS to ship an official API. Tools like Kampala are how AI agents will integrate with the existing software ecosystem for the next five years, until MCP-style universal interfaces catch up.”
“Data governance and AI agents are on a collision course. As more business decisions are delegated to AI, the correctness of KPI computation becomes load-bearing — a hallucinated revenue figure that influences a product decision is a serious failure mode. Metrics SQL represents a class of infrastructure that will become mandatory as AI takes on more analytical work.”
“For social media automation and cross-platform content workflows this is a game-changer. Building automations for platforms with limited or expensive APIs has always required fragile browser scraping — having a stable API layer extracted from the real app traffic is a much better foundation.”
“I rely on AI to pull weekly performance data, and the number of times it's given me different 'correct' answers for the same metric is maddening. Having a single governed source that every AI query resolves against means I can trust the numbers I'm making decisions on. That trust is worth a lot.”
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