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Syntropylabs vs Confident AI
Their strengths as we found them in their documentation; ours as the flow book states them. Nine rows, three states.
Confident AI, the platform behind the open-source DeepEval framework, has the cleanest evaluation model in this comparison: goldens with a finalized flag, dataset versions pinned in code, hyperparameters recorded on every run so “did the prompt change or the model?” is answerable, a broad metric catalogue, and real red-teaming through DeepTeam with OWASP, NIST and MITRE ATLAS mappings. Its tracing is the thinnest of the vendors we studied, with no services, endpoints or cost analytics, and its synthesizer and code metrics are Python-only. Syntropylabs is stronger where the request meets the model call, traces coding agents, and simulates against your real agent in a sandbox.
Feature table
Cells about Syntropylabs cite the flow book that specifies the product (the F- and C- ids are its flow numbers); a partial is stated with its limit. Cells about the other product come only from our documented teardown of its public documentation, dated 2026-09-06, with the source linked below. Where that teardown is silent, the cell says “not documented here”, never “no”.
| Feature | Syntropylabs | Confident AI |
|---|---|---|
| Tracing depth | yes Trace waterfall, sessions, users, error groups, logs, and services → endpoint → request in one tool (F-TR-02, F-TR-04 to F-TR-07). Span cost is computed from the price sheet in the browser and shown as approximate (F-TR-02). | partial Spans typed llm / retriever / tool / agent, Threads, tags, users and environment, masking, sampling, Triage to Linear or GitHub, Signals and Monitors. The teardown calls it the thinnest tracing of the three: no APM-style services, endpoints or service map, and no cost analytics page. |
| Coding-agent tracing | partial Claude Code, Codex, Gemini CLI, Cursor, Windsurf and OpenCode in one command; one trace per turn with subagents nested (C-INS-01). Cost arrives on a separate record merged onto the model call, so a lost record leaves a turn priced by the catalogue estimate; tool output for non-shell tools needs the CLI hook (F-TR-17). | not documented here The teardown mentions a Claude Code plugin without documenting what it captures. |
| Evaluation runs | yes Runs from a dataset with existing or generated outputs, per-row tri-state scores with judge reasons, compare against a baseline, gates, and SDK-created runs that open like any other (F-EV-01 to F-EV-07). | yes Test runs with hyperparameters (model, prompt version, temperature), an --official baseline, Regression test A|B, and Experiments with a statistical winner; CLI flags for cache, parallel, repeat and re-run failing. Component-level evals are single-turn only; CI supports end-to-end only. |
| Online rules | yes Per-environment rules with evaluators, judge, interval, sampling, filters and a daily budget that pauses the rule (F-EV-08, F-EV-09). Deterministic assertion rules attached to an online rule still run through the judge path (F-EVL-04). | yes Metric collections with per-metric threshold, strictness, include-reason and sample rate. Online evals run only referenceless metrics and silently skip the rest. |
| Datasets and snapshots | yes CSV and JSONL import, rows from traces, snapshots that runs pin to, and runs over time on the dataset (F-DS-01 to F-DS-03, C-DS-01). The snapshot diff shows counts and row references, not before/after values, and rows cannot be restored from a snapshot (F-DS-14). | yes Goldens with a finalized flag, dataset versions pinned in pull(), multi-turn datasets, and a Synthesizer from documents with a critic-model quality filter. Synthesizer and code metrics are Python-only; annotation export is CSV only. |
| Simulation | yes No-code, code and connector agents; generated or hand-written scenarios; live runs; LLM-judge scoring; run comparison (F-SIM-01 to F-SIM-09). Deterministic scores carried on SDK-run spans are not rendered yet (F-SIM-10). | yes ConversationSimulator generates turns from a golden’s scenario, expected_outcome and persona; chat simulations are on Starter and up. Sandboxed agent runs are not documented here. |
| Red-team | yes Vulnerabilities × attack strategies on the sandbox runner, a risk report, and every materialized attack kept as a trace that can become a regression row (C-SIM-01). | yes DeepTeam: vulnerabilities × attack strategies (prompt injection, linear jailbreaking, crescendo), a CVSS risk score, OWASP / NIST / MITRE ATLAS frameworks, a PDF risk profile, and trace-level detections (materialized / mitigated / attempted). Red teaming and governance are Enterprise. |
| Self-hosting | Hosted at syntropylabs.ai. On-premise or VPC deployment is an Enterprise-plan line; there is no community self-hosted edition. | On-prem and data residency on Enterprise; the DeepEval framework itself is open source. |
| Pricing model | Free: tracing for one project, 7-day retention, 10,000 spans a month soft cap, coding-agent traces included. Pro $20 a month: evaluation, datasets, simulation. Enterprise: custom. Upgrades are requested in-app; self-serve billing is not built (F-ORG-06). | Free $0: 2 seats, 1 project, 5 test runs a week, 1 GB-month spans. Starter $200/mo: 5 projects, custom metrics, online evals, annotation queues, chat simulations, alerting, API. Team $2,000/mo: unlimited projects, metric and dataset versioning, git-based prompt workflows, custom RBAC, SSO. Enterprise: on-prem, data residency, red teaming and governance. |
fig. 1 · nine rows, three states · reviewed 2026-09-15
Choose Confident AI if…
- You want the DeepEval metric catalogue and G-Eval in an open-source Python framework, with the platform optional.
- You want red-team results mapped to OWASP, NIST and MITRE ATLAS in a PDF risk profile.
- You want trace-version Monitors with anomaly and regression detection, and Signals from classifiers; our quality monitors by version are not built (C-AL-01).
- You want multi-turn datasets with conversational evaluators today; ours are partial (C-DS-03).
Choose Syntropylabs if…
- You need APM-grade tracing next to the LLM spans; the teardown calls Confident AI’s tracing the thinnest of the three.
- You want TypeScript parity: their synthesizer and code metrics are Python-only.
- You want to generate scenarios, run them against your real agent in a sandbox, score the run with a judge, and red-team the same agent.
- You want tracing free on every plan; Confident AI’s free tier allows 5 test runs a week and online evals start at $200/mo.
Next steps
Try the tracing side first
Tracing is free for one project on every plan, coding-agent traces included. Evaluations, datasets and simulation are Pro and up.