Langfuse vs Braintrust vs Cekura (Agency Evaluation Stack Fit)
These three solve different halves of the same problem: tracing tells you what an agent did, evaluation tells you whether it was good, and voice simulation tells you what breaks before a client hears it. An agency running one stack across every account will overpay on simple builds and under-test the risky ones, so match the tool to the failure mode the client actually fears. The premium on production-ready AI work comes from being able to show a client the evidence, not from owning the most features.
By InnovaAI ResearchPublished
Which should an agency choose?
Langfuse vs Braintrust vs Cekura (Agency Evaluation Stack Fit)
Langfuse
Best for: Agencies with in-house engineering who need data residency and are willing to trade managed convenience for control.- Open-source core means client data can stay inside your own infrastructure, which matters when a retainer contract forbids third-party training pools
- Hierarchical traces capture every LLM call, tool invocation, and retrieval step in one view, so debugging a broken client workflow does not require stitching logs from three systems
- Prompt versioning with rollback lets a delivery team revert a regression in minutes rather than re-prompting from scratch
- Self-hosting shifts uptime, upgrades, and storage cost onto your own engineers
- Scoring and experiment tooling is thinner than dedicated eval platforms, so quality gates often need custom code
Braintrust
Best for: Agencies shipping frequent prompt changes across several client accounts who need experiment discipline more than infrastructure control.- Evaluation framework supports LLM-as-judge scoring, which lets a small team grade hundreds of client outputs without hiring annotators
- Real-time tracing pairs prompt, response, and tool-call inspection with latency and cost monitoring in the same view
- Experiment management makes it practical to compare two prompt variants before shipping a change to a live client agent
- Managed-only deployment limits options for clients with strict data residency clauses
- Cost scales with trace volume, so high-volume voice or chat agents can push spend past a fixed-fee retainer
Cekura
Best for: Agencies running voice agents on client phone lines where a single bad call carries reputational cost.- Simulates thousands of scenarios with diverse personas before launch, which surfaces failure modes that a five-call manual QA pass misses
- Voice-specific signals such as gibberish detection, interruption tracking, and latency catch problems text-only evaluators cannot see
- Judge tuning against real recordings keeps scoring aligned with how actual callers behave
- Narrow focus on voice and chat agents leaves text-heavy RAG or back-office pipelines uncovered
- Pre-launch simulation adds a testing stage that compresses the timeline on fast-turnaround builds
These three solve different halves of the same problem: tracing tells you what an agent did, evaluation tells you whether it was good, and voice simulation tells you what breaks before a client hears it. An agency running one stack across every account will overpay on simple builds and under-test the risky ones, so match the tool to the failure mode the client actually fears. The premium on production-ready AI work comes from being able to show a client the evidence, not from owning the most features.