Langfuse vs Braintrust vs Cekura (Agency Evaluation Stack Fit)
The choice is not which platform scores highest, it is which failure mode your client contract cannot absorb. An agency holding sensitive client data should weight deployment model first, while a team shipping voice agents should weight modality coverage, because a text-only evaluation stack will miss the interruptions and latency that generate complaint calls. Most agencies end up pairing a tracing layer with a modality-specific tester rather than standardizing on one vendor, and that pairing decision is what justifies a production-ready AI line item on the retainer.
By InnovaAI ResearchPublished
Which should an agency choose?
Langfuse vs Braintrust vs Cekura (Agency Evaluation Stack Fit)
Langfuse
Best for: Agencies with an in-house engineer and clients who ask where their data lives before signing.- Open-source core means client data can stay inside your own infrastructure, which matters when a retainer contract includes a data residency clause
- Hierarchical traces capture every LLM call, tool invocation, and retrieval step, so a failed client deliverable can be replayed step by step
- Prompt versioning with rollback lets a delivery team revert a regression in minutes rather than rebuilding a prompt from memory
- Self-hosting shifts uptime and upgrade work onto your own engineers, a real cost for a 10-person agency
- Scoring and drift detection require you to define the criteria yourself, so a junior operator can ship a dashboard that measures nothing useful
Braintrust
Best for: Agencies running repeated A/B prompt experiments across several client accounts and needing a defensible quality number.- Experiment management lets two prompt variants run against the same scored dataset, which turns a subjective creative debate into a number a client can read
- LLM-as-judge scoring criteria are configurable, so a QA lead can encode a client's tone rules once and reuse them across every campaign
- Real-time tracing of prompts, responses, and tool calls gives account managers a shared view without asking engineers for a status update
- Managed pricing scales with evaluation volume, and a high-traffic client agent can push monthly spend past what the retainer supports
- Judge-based scoring inherits the judge model's blind spots, so a poorly written rubric produces confident but wrong quality scores
Cekura
Best for: Agencies deploying phone or chat agents for clients where a single bad call is a visible brand problem.- Simulates thousands of scenarios with diverse personas before launch, which surfaces voice agent failures while they are still cheap to fix
- Voice-specific signals such as gibberish detection, interruption tracking, and latency catch problems that text-only evaluation never sees
- Judge tuning against real recordings closes the gap between what a rubric assumes and how callers actually behave
- Narrow focus on voice and chat agents means it will not cover a client's retrieval pipeline or back-office automation
- Scenario libraries need periodic refresh as client scripts change, adding a maintenance task most agencies underestimate
The choice is not which platform scores highest, it is which failure mode your client contract cannot absorb. An agency holding sensitive client data should weight deployment model first, while a team shipping voice agents should weight modality coverage, because a text-only evaluation stack will miss the interruptions and latency that generate complaint calls. Most agencies end up pairing a tracing layer with a modality-specific tester rather than standardizing on one vendor, and that pairing decision is what justifies a production-ready AI line item on the retainer.