TestRelic vs Confident AI for DeepEval users
TestRelic Team
If you write LLM evals with DeepEval, you have two hosted destinations for the results: Confident AI, the cloud platform built by DeepEval's own maintainers, or TestRelic, via the testrelic-deepeval bridge. Neither requires rewriting a single test case — every LLMTestCase, Golden, and Metric works as-is on both. The real difference is what sits around your evals once they land.
Competitor details reflect their public docs and pricing as of August 2026 — check their sites for the latest.
Credit where it's due
Confident AI is the first-party platform. The same team maintains DeepEval and the cloud product, so new DeepEval features tend to have a platform surface from day one, and deepeval login is all the wiring you need.
It is also considerably more than an eval dashboard. Per its public docs, Confident AI covers production LLM tracing and observability (inputs, outputs, tool calls, latency, token costs, real-time alerting), online evals on production traffic, red teaming against adversarial attacks using the OWASP Top 10 for Agentic Applications, chat simulations that generate multi-turn conversations before deployment, git-based prompt versioning, and dataset curation from production traces. It ships Python and TypeScript SDKs, is SOC 2 Type II / HIPAA / GDPR compliant, offers US or EU data residency, and supports self-hosting on its Enterprise plan.
If your product is the LLM application — a chatbot, an agent, a RAG API — and you need to trace and stress-test it in production, Confident AI is a strong choice, and TestRelic does not attempt most of that surface. We have no production LLM tracing, no red teaming, no prompt management.
The workflow difference
Confident AI is an LLM-application platform. Everything in it — evals, traces, simulations — is scoped to LLM systems.
But for a lot of teams, the LLM feature is one feature inside a product that also has a checkout flow, a settings page, and a mobile app — all covered by Playwright, Appium, Maestro, or pytest suites. With Confident AI, those suites report somewhere else. You end up with two platforms, two orgs, two notions of "is the release healthy," and no shared history between the eval that guards your AI summarizer and the E2E test that clicks the button which triggers it.
TestRelic's bet is that evals are test data. The testrelic-deepeval package reads DeepEval's in-memory TestRun — no monkey-patching, no HTTP redirect — and uploads it to your TestRelic org, where eval runs render through the same Run Detail and Session Workspace as your browser, API, and mobile tests. Concretely, that unification means:
- One feed. The org-wide Test Runs dashboard can include eval rows via an opt-in toggle, mixed with your functional runs. Each repository gets an Evaluations tab next to Test Cases and Test Runs.
- One stability model. Eval Stability tracks how consistently eval cases pass across runs — the same way flakiness surfaces an unreliable E2E test. A metric that flips run-to-run is telling you something about your prompt or your judge.
- One AI over everything. Ask AI answers questions across your whole test history — evals and functional runs together — in the web app, the terminal CLI, Slack, and Teams.
- Text-to-image evals too. If your product generates images, the CLI's
augurcommand uploads text-to-image runs into Augur, a ship-gate workspace with a prompts × models triage matrix and prompt-phrase attribution — a category Confident AI's text-centric metrics don't target.
Datasets carry over as well: testrelic.datasets pushes versioned, labeled goldens and pulls them back as a DeepEval EvaluationDataset, so CI can pin a fixed label while latest moves.
What migration actually looks like
The migration guide is three small changes; your eval code stays identical:
pip install "testrelic-deepeval[deepeval]"
testrelic login --api-key tr_yourkey
deepeval logout # stop the duplicate upload to Confident AIThen run deepeval test run tests/ exactly as before — the pytest plugin captures the run at session finish. Failed uploads (a provider hiccup mid-CI, say) queue locally and replay with testrelic drain, and CI context — branch, commit, CI URL — is attached automatically. One honest caveat: your historical comparisons start fresh from your first TestRelic run; prior Confident AI history is not copied over.
Side by side
| Confident AI | TestRelic | |
|---|---|---|
| Built by the DeepEval maintainers | ✓ | — (independent bridge reading DeepEval's TestRun) |
| DeepEval code changes required | None | None |
| LLM eval runs, dashboards, datasets | ✓ | ✓ |
| Production LLM tracing, online evals, alerting | ✓ | — |
| Red teaming & chat simulations | ✓ | — |
| Functional test analytics (Playwright, Appium, Maestro, pytest) | — | ✓ |
| Evals + E2E runs in one org, one feed, one AI | — | ✓ |
| Text-to-image ship gate (Augur) | — | ✓ |
| First paid tier | Starter, $200/mo | Growth, $19/mo |
On pricing, compare carefully rather than by the headline number. Confident AI's free tier includes cloud features (2 seats, 1 project, 5 test runs/week per its pricing page), and its $200/month Starter buys LLM-observability workflows TestRelic simply doesn't have. TestRelic's Free plan is local SDK reporting only; Growth at $19/month (with a 14-day trial, no card) adds cloud upload, Ask AI, and the CLI. Cheaper, but for a different scope.
Who should pick which
Pick Confident AI if your LLM application is the product, you want production tracing and red teaming in the same place as your evals, or you value first-party support from the DeepEval team above all else.
Pick TestRelic if your LLM features live inside a product you also E2E-test, and you want evals, browser runs, API runs, and mobile runs in one org with one history and one AI to ask about all of it — at a lower entry price.
Running both during a transition works too: the bridge reads DeepEval's in-memory results, so it coexists with whatever else is configured.
Related documentation
FAQ: Do my DeepEval metrics score differently on TestRelic?
No. TestRelic never recomputes DeepEval metrics — G-Eval, AnswerRelevancy, Faithfulness, and the rest produce the same scores; TestRelic captures and stores what DeepEval computed.
FAQ: Can I send eval runs to both platforms at once?
Yes. testrelic-deepeval reads DeepEval's in-memory TestRun, so if CONFIDENT_API_KEY is still set, DeepEval uploads to Confident AI and TestRelic captures the same run. Unset the key (deepeval logout) when you want TestRelic only.