Langfuse vs Braintrust vs Cekura (Agency Eval Stack Fit by Delivery Type)
The choice tracks the delivery type, not a feature checklist: tracing-first platforms suit agencies that need prompt control and data residency, experiment-first platforms suit teams shipping frequent prompt changes across many accounts, and simulation-first platforms suit voice deployments where pre-launch scenario coverage prevents reputational damage. Agencies that pick one axis and standardize on it can quote evaluation as a line item on the retainer instead of absorbing it as overhead. Mixing two platforms without a defined owner usually produces duplicate instrumentation and no single source of truth when a client asks what changed.
By InnovaAI ResearchPublished
Which should an agency choose?
Langfuse vs Braintrust vs Cekura (Agency Eval Stack Fit by Delivery Type)
Langfuse
Best for: Agencies that already run infrastructure and want tracing plus prompt control without per-seat vendor lock.- Open-source core means client data can stay inside your own infrastructure, which shortens security review on retainer work
- Hierarchical traces capture every LLM call, tool invocation, and retrieval step in one view
- Prompt versioning with rollback lets a delivery team revert a bad client prompt without a redeploy
- Self-hosting shifts uptime and upgrade work onto your own engineers
- Evaluation scoring is lighter out of the box than dedicated eval suites, so custom scorers take build time
Braintrust
Best for: Agencies shipping frequent prompt iterations across several client accounts and needing regression evidence per release.- Real-time tracing pairs with an experiment framework, so a prompt change can be scored against a baseline before it ships
- Automated pattern discovery surfaces failure clusters without an engineer reading raw logs
- LLM-as-judge scoring criteria are configurable per client, which suits multi-account delivery
- Managed-only model means client conversation data leaves your environment, a blocker for regulated accounts
- Cost scales with traced volume, so high-traffic client agents need budget modeling up front
Cekura
Best for: Agencies deploying voice agents for clients where call quality and interruption handling drive retention.- Pre-production simulation runs thousands of persona scenarios before a voice or chat agent goes live
- Voice-specific signals such as gibberish detection, interruption tracking, and latency catch failures text-only evals miss
- Native hooks into orchestration platforms like Vapi, Retell, and Synthflow reduce integration work
- Narrow fit: value drops sharply for text-only or non-conversational client workloads
- Judge tuning against real recordings is a manual step that consumes delivery hours early on
The choice tracks the delivery type, not a feature checklist: tracing-first platforms suit agencies that need prompt control and data residency, experiment-first platforms suit teams shipping frequent prompt changes across many accounts, and simulation-first platforms suit voice deployments where pre-launch scenario coverage prevents reputational damage. Agencies that pick one axis and standardize on it can quote evaluation as a line item on the retainer instead of absorbing it as overhead. Mixing two platforms without a defined owner usually produces duplicate instrumentation and no single source of truth when a client asks what changed.