Braintrust vs QuantProbe

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	Braintrust Evaluation	QuantProbe Evaluation
Tagline	Eval, monitor, and improve AI products end-to-end.	Physics-based calculator that predicts LLM decode speed, memory fit, and quantization quality on any hardware.
Category	Evaluation	Evaluation
Pricing	Freemium· Starter: $0 · Pro: $249 · Enterprise: Custom pricing	Free· Free and open source; install with `pip install quantprobe` or use the hosted web calculator.
Model	Platform (any LLM)	—
Editorial score	8.9 / 10	—
Use cases	evalsmonitoringprompt management	GPU and workstation sizing for local LLM inferenceQuantization tier selection (Q4/Q5/Q8) under a latency budgetPredicting tokens-per-second for a candidate model on target hardwareComparing dense vs. Mixture-of-Experts checkpoints on the same boxEstimating KV-cache memory at long context lengthsCalibrating a machine's real memory bandwidth for tighter forecastsSanity-checking vendor or community throughput claims
Pros	Full eval + observability in one tool Excellent UX Strong dataset/experiment tracking Closed loop dev → prod	Predicts decode speed and memory fit before you download or deploy a model, saving hours of trial-and-error Validated against real measurements across 7B to 753B parameter models, including MoE architectures Quantifies the perplexity cost of quantization, letting you trade speed against quality with numbers rather than intuition Fully open source — laws, probes, recipes, and raw logs live in the repo and can be audited or extended Ships both a browser calculator and a `pip install`-able Python package with hardware calibration probes Explicit density coefficients for GPU dense (~0.62) vs. MoE (~0.38) tiers make assumptions transparent
Cons	Team pricing is steep Smaller than LangSmith ecosystem-wise	Prediction envelope is roughly plus or minus 25% for off-VRAM scenarios, so it is a planning tool rather than a benchmark replacement Coverage is centered on llama.cpp-style decode; training, prefill, and batched serving throughput are out of scope Small, single-maintainer project — no SLA, support channel, or guarantee of long-term upkeep The web UI assumes familiarity with quantization schemes, KV cache, and bandwidth concepts, so newcomers face a learning curve Not itself an LLM — practitioners looking for a generative or agentic tool will find only a calculator here
Website	www.braintrust.dev	federicots.github.io

Pick Braintrust if

✅ Full eval + observability in one tool
✅ Excellent UX
✅ Strong dataset/experiment tracking
✅ Closed loop dev → prod

Pick QuantProbe if

✅ Predicts decode speed and memory fit before you download or deploy a model, saving hours of trial-and-error
✅ Validated against real measurements across 7B to 753B parameter models, including MoE architectures
✅ Quantifies the perplexity cost of quantization, letting you trade speed against quality with numbers rather than intuition
✅ Fully open source — laws, probes, recipes, and raw logs live in the repo and can be audited or extended

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