Guide
Long-form buyer and practitioner guides.
31 posts
Evaluation of LLM Applications: A Practical 2026 Guide
A vendor-neutral guide to evaluation of LLM applications: metric selection, dataset sizing math, judge calibration, cost models and a tool comparison.
BLEU vs ROUGE vs BERTScore - Which to Use and Why All Three Fail on Chat
BLEU counts precision, ROUGE counts recall, BERTScore compares embeddings. Here is how each one actually computes a score, a worked example on the same sentence, and why none of them can grade an open-ended LLM answer.
Context Precision vs Recall Explained - Diagnosing RAG Retrieval in 2026
Context precision punishes noise, context recall punishes gaps. Here is how each retrieval metric is computed, a worked example, and how the two scores together tell you whether your retriever is over-fetching or missing documents.
The Faithfulness Metric Explained - How to Catch RAG Hallucination in 2026
Faithfulness measures whether every claim in an answer is supported by the retrieved context. Here is exactly how the claim-extract-and-verify scoring works, a worked example, and why a faithful answer can still be wrong.
G-Eval Explained - How Chain-of-Thought LLM Scoring Works in 2026
G-Eval is an LLM-as-judge metric that writes its own evaluation steps, then scores against them. Here is how the chain-of-thought scoring and token-probability weighting actually work, and when it beats BLEU or a plain judge prompt.
Arize Pricing in 2026 - Phoenix Is Free, AX Is a Sales Call
Arize is really two products with two prices - Phoenix, the free open-source tracer, and Arize AX, whose pricing page returns a 403 and forces a sales call. Here is how to tell which one you are pricing, and the transparent alternatives if AX is the answer.
Braintrust Pricing Explained (2026) - The Processed-Data Trap
Braintrust meters "processed data" in GB - every byte of inputs, outputs, prompts and metadata - with no hard spending cap. Here is how the meter really works, a worked bill, and why verbose agents blow past the $249 floor.
Build vs Buy LLM Observability in 2026 - The Honest Decision Guide
Rolling your own LLM tracing, self-hosting open source, or buying a managed platform each has a hidden cost. Here's how to decide, with the real trade-offs of Langfuse, Opik and Braintrust laid out by scenario.
DeepEval Pricing Explained (2026) - What You Actually Pay
DeepEval the framework is free under Apache-2.0. The Confident AI cloud is where the money is, and it has a real 10x cliff from $200/mo Starter to $2,000/mo Team. Here is how the meter works, a worked bill, and cheaper picks.
Galileo Pricing Explained (2026) - What You Actually Pay
Galileo bills by traces per month, with a genuinely generous 5,000-trace free tier and a $100/mo Pro tier - then everything jumps to contact-sales. Here is how the meter works, a worked estimate, and two cheaper picks.
Helicone Pricing in 2026 - Decoded, and Why the Meter Is a Mystery
Helicone's Hobby tier is free and the Pro tier starts at $79/mo, but the overage rate above 10k requests is never published - and the whole product is in maintenance mode. Here is what the pricing actually means and where to go instead.
Is Langfuse Worth It in 2026? An Honest Verdict After the Hype
Langfuse is the open-source observability default for good reason, but it is not the right pick for everyone. Here's the honest case for and against, plus when Opik or LangSmith is the smarter buy.
Laminar Pricing Explained (2026) - What You Actually Pay
Laminar bills on two axes - data by the GB and "Signals" measured in tokens spent reading your traces, not tokens your agent spends. That makes a monthly forecast genuinely hard. Here is how the meter works and two easier-to-predict picks.
The Langfuse Free Tier Explained in 2026 - Limits, Cap and When to Leave
Langfuse has two free options, and they are not the same. Here's what the Hobby cloud tier's 50k-unit cap really counts, how the free MIT self-host differs, and when Opik's free tier beats both.
Langfuse Pricing Explained (2026) - What You Actually Pay
Langfuse bills "billable units" - traces plus observations plus scores - at $8 per 100k, and self-host is free under MIT. Here is how the meter really works, a worked bill estimate, and when a cheaper pick beats it.
LangSmith Pricing Explained (2026) - Why the Bill Explodes at Scale
LangSmith bills base traces at $2.50 per 1,000 plus $39 per seat, and you cannot self-host below Enterprise. Here is how the trace meter really works, a worked bill at 1M traces, and two picks that cost a fraction.
LLM Evaluation Metrics Explained - A Practical 2026 Guide
What LLM evaluation metrics actually measure, how reference-based, statistical and LLM-as-judge scores differ, and which metric to reach for first. Grounded in the tools that ship these metrics out of the box.
LLM Observability vs Monitoring - What's the Difference in 2026?
Monitoring tells you something is wrong. Observability tells you why. For AI apps the distinction matters more than usual, because the failures are silent. Here is the real difference, when you need each, and where the tools fit.
LLM Tracing vs Logging - What's the Difference? (2026 Guide)
Logging records isolated events. Tracing connects them into the full execution tree of a request. For multi-step LLM agents that difference is everything. Here is what each is, when you need tracing, and the tools that do it.
Maxim Pricing Explained (2026) - What You Actually Pay
Maxim charges per seat AND caps logs, so both meters run at once - a five-engineer team pays $145/mo in seats before a single log. Here is how the double meter works, a worked bill, and cheaper picks.
Opik Pricing in 2026 - The Cheapest Cloud, Decoded
Opik's Pro cloud is $19/mo for 100k spans - the cheapest managed tier of the major eval platforms - and the self-hosted build is free under Apache-2.0 with no gates. Here is how the meter works, the per-seat trap, and when a different tool is worth more.
Portkey Pricing Explained (2026) - What You Actually Pay
Portkey's meter caps logs, not requests, so your traffic keeps flowing while your observability quietly goes dark past the limit. Here is how the $49/mo Production tier really works, a worked bill, and cheaper picks for real tracing.
Promptfoo Pricing in 2026 - What's Actually Free and When You Pay
Promptfoo is MIT-licensed and free forever, with one hard cap - 10k red-team probes a month. Here's how the pricing really works, the contact-sales gap above the free tier, and two eval tools with public pricing when you outgrow it.
RAG Evaluation Metrics Explained - The 2026 Practical Guide
RAG breaks in two places - retrieval and generation - so you measure both. Here are the metrics that matter (context relevancy, faithfulness, answer relevancy), why the RAG triad works, and the tools that ship these scores.
What Are LLM Evals? A Plain-English 2026 Guide
LLM evals are automated tests for AI outputs - a dataset, a set of metrics, and a runner that scores them. Here is what they are, how offline and online evals differ, when you need them, and the tools that run them.
What Is LLM-as-a-Judge? How AI Grades AI Output in 2026
LLM-as-a-judge uses one model to score another model's output on qualities that have no single right answer - faithfulness, helpfulness, relevance. Here is how it works, where it is reliable, where it is not, and which tools do it well.
What Is LLM Evaluation? How to Measure AI Output Quality in 2026
LLM evaluation is how you measure whether your AI app's output is actually good - at scale, repeatably, in CI - instead of eyeballing transcripts and hoping. Here is what it means, the methods that matter, and where the tools fit.
What Is LLM Observability? A Plain-English Guide for 2026
LLM observability is how you see inside an AI app in production - the traces, evals and metrics that tell you why an answer was wrong, not just that a user complained. Here is what it means, how it works, and where the tools fit.
What Is LLM Tracing? How to See Inside an AI Request in 2026
LLM tracing records every step of a single AI request - each model call, retrieved document, tool call and agent step - as a tree you can read. Here is what it means, how it works with OpenTelemetry, and where the tools fit.
What Is Prompt Management? A Practical 2026 Guide
Prompt management means versioning your prompts, decoupling them from code, and knowing which version produced which output. Here is what it is, why it matters, and the tools that do it - grounded in their real features and gotchas.
The 2026 LLM Observability Consolidation Map - Who Got Bought, Who Stayed Free
Three LLM observability tools got acquired in early 2026 - Langfuse by ClickHouse, Helicone by Mintlify, Promptfoo by OpenAI. Here is what each deal means for buyers, who is still independent, and how to choose a tool that will not get sunset under you.