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💡 Pro tip: Start with the free tier to test if Braintrust fits your workflow before upgrading to a paid plan.
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If Braintrust's pricing doesn't fit your budget, consider these ai evaluation alternatives:
Open-source LLM engineering platform that captures traces, runs evaluations, and helps teams analyze cost, latency, and quality across production AI apps.
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DeepEval is the open-source, Pytest-style LLM evaluation framework from Confident AI. Ship it in your test suite the same way you ship pytest — it runs 50+ research-backed metrics (hallucination, tool-use correctness, contextual precision/recall, conversational quality) offline in dev and inline in CI/CD, then optionally streams the results to Confident AI's paid cloud for team dashboards and prompt versioning.
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Open-source LLM observability and gateway platform offering one-line integration, request logging, prompt management, evals, and a multi-provider proxy.
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Manual optimization typically costs 10-20 engineering hours monthly at $100/hour, or $1,000-2,000 in burdened cost. The Loop agent analyzes production traces and automatically generates 12 prompt variations targeting specific issues you describe in plain English. Most teams see ROI within 2-3 months on the Pro tier at $25/seat. The agent also learns from your evaluation results, so improvements compound over time rather than starting from scratch each cycle.
Choose Braintrust ($25/seat) for automated optimization plus monitoring when you have a production LLM app generating revenue. Choose Langfuse (free, self-hosted) for budget-conscious teams that want full data control and only need monitoring. Choose Helicone (~$20/month) for simple OpenAI usage tracking without evaluation needs. The decision hinges on whether you need automated improvement (Braintrust) or just visibility (Langfuse/Helicone). Braintrust is the only one of the three with a Loop agent for automated prompt generation.
It works for small apps with under 1K eval rows per month and 14-day retention windows. The free tier includes the full Loop agent, so you can validate the optimization workflow before paying. Most production teams quickly hit limits on team members (2 max) or eval volume and upgrade to Pro within the first month. For experimentation, prototypes, or solo developers shipping low-traffic apps, the free tier is genuinely usable rather than a stripped-down trial.
DIY observability typically runs $9K+ in initial setup: monitoring infrastructure costs, custom evaluation scripts (40+ engineering hours), and optimization consulting ($5K+ for a contractor). Ongoing maintenance adds another $500-1,000/month in engineering time. Braintrust Pro at $25/seat/month includes everything: traces, evaluations, the Loop agent, datasets, and scorers. For a 5-person team, that's $125/month versus $1,500+/month DIY — a 12x cost reduction.
Yes, Braintrust is model-agnostic and integrates with OpenAI, Anthropic Claude, Google Gemini, open-source models via Hugging Face, and 20+ other LLM providers. This is a key differentiator versus LangSmith, which is optimized for the LangChain ecosystem. You can run side-by-side evaluations across multiple providers in a single dashboard, which is useful for cost optimization or vendor risk reduction. Custom model endpoints are supported through the SDK.
Start with the free tier and upgrade when you need more features
Get Started with Braintrust →Listed prices may change. Confirm current prices and offer terms with the vendor.