As ferramentas de observabilidade de LLM rastreiam fluxos de trabalho de IA, monitorizam o comportamento em produção e avaliam a qualidade dos modelos. Este guia compara as principais opções para depurar agentes com várias etapas, acompanhar prompts e versões de modelos, medir a latência e a utilização de tokens e detetar regressões antes da implementação. Encontrará plataformas de código aberto, autoalojadas, empresariais, baseadas em OpenTelemetry e direcionadas para programadores, para se adaptarem à sua arquitetura técnica, necessidades de governação e fluxo de entrega.
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Tabela comparativa: ferramentas de observabilidade de LLM
Compare lado a lado os preços e as especificações das principais plataformas da minha lista restrita.
| Tool | Best For | Trial Info | Price | ||
|---|---|---|---|---|---|
| 1 | Best for open-source OTel-native tracing | Free plan + free demo available | Pricing upon request | Website | |
| 2 | Best for open-source, self-hosted evaluation at scale | Free plan available | Pricing upon request | Website | |
| 3 | Best for full-lifecycle LLM evaluation with gateway ops | Free plan + free demo available | Pricing upon request | Website | |
| 4 | Best for open-source evaluation and tracing | Free plan + free demo available | From $19/month | Website | |
| 5 | Best for linking prompt versions to production traces | Free plan + free demo available | From $49/month | Website | |
| 6 | Best for tracing agents across complex pipelines | Free plan + free demo available | From $50/month | Website | |
| 7 | Best for linking LLM traces to product analytics | Free plan available | Pricing upon request | Website | |
| 8 | Best for open-source tracing with prompt versioning | Free plan + free demo available | From $29/month | Website | |
| 9 | Best for evaluation-to-production scoring in one loop | Free plan + free demo available | From $249/month | Website | |
| 10 | Best for LangChain and LangGraph tracing | Free plan + free demo available | From $39/user/month | Website |
Análises de ferramentas de observabilidade de LLM
Seguem-se resumos detalhados das principais escolhas da minha lista restrita. Cada análise aborda funcionalidades essenciais, casos de utilização e integrações para ajudar a encontrar a opção mais adequada.
Best for open-source OTel-native tracing- Free plan + free demo available
- Pricing upon request
Visit WebsiteCustomer Rating:5/5
Traceloop lets users inspect LLM trace details, prompts, and token usage. Traceloop by ServiceNow is built on OpenTelemetry, which gives you span-based trace logging, output quality evaluation, a prompt registry with environment-based deployments, production monitors, and CI/CD quality gates.
Who Is Traceloop Best For?
Traceloop suits platform and DevOps engineers who need compliant, self-hosted LLM observability on Kubernetes or air-gapped infrastructure.
Why I Picked Traceloop
I've included Traceloop in my top picks because it's built entirely on OpenTelemetry through its open-source OpenLLMetry SDK, which means every LLM call, vector DB query, and agent step is recorded as a native OTel span you can export to 25+ backends. I particularly like the evaluator pipeline: you can run the same quality checks as CI/CD gates on pull requests, inline guardrails in production, and real-time monitors on live traffic simultaneously. The prompt registry with environment-based deployments also lets you roll out prompt changes to dev before production, keeping version control tight across the full lifecycle.
Traceloop Key Features
- Built-in evaluators: Automatically check faithfulness, relevance, and safety on live traffic with no manual setup required.
- Agent-focused evaluation checks: Score agent outputs across correctness, tool usage, memory retention, task completion, and safety.
- Production monitors: Run evaluators in real time on every span matching a filter to detect hallucinations and quality regressions.
- Inline guardrails: Execute safety checks in real time during inference, built directly from your configured evaluators.
Traceloop Integrations
Traceloop supports 20+ LLM providers, including Axiom, Braintrust, BMC, Google Cloud, Highlight, Honeycomb, Instana, OpenTelemetry Collector, Datadog, Oracle Cloud, and Tenant Cloud. An API for creating custom LLM-as-a-judge evaluators is also available.
Pros and Cons
Pros:
- Self-hosting supports air-gapped deployments
- Evaluations span CI/CD through production
- Native tracing reduces vendor lock-in
Cons:
- Public cost attribution details remain limited
- ServiceNow acquisition creates roadmap uncertainty
Learn more about Traceloop:
Best for open-source, self-hosted evaluation at scale- Free plan available
- Pricing upon request
MLflow tracks LLM traces with correctness and groundedness scores. MLflow is an open-source AI observability platform that combines trace logging, output quality evaluation, prompt versioning, cost and token tracking, and production monitoring for LLM applications and AI agents.
Who Is MLflow Best For?
MLflow suits ML/AI engineers and data scientists who need full control over their observability infrastructure and want to avoid per-trace fees or vendor lock-in.
Why I Picked MLflow
I've included MLflow in my top picks because it's the only tool here built as Apache 2.0 open source from the ground up, meaning your trace data never leaves your own infrastructure. I like that the Prompt Registry links every prompt version directly to its evaluation results, so you can run A/B tests on variants and see quality, latency, and safety scores side by side before promoting anything to production. The asynchronous LLM judges also score sampled traces continuously without slowing down live traffic.
MLflow Key Features
- Prompt Registry: Version, test, and compare prompts side by side, with lineage tracking and automatic prompt optimization to tune prompts algorithmically.
- Guardrails and safety detection: Real-time scoring detects prompt injection, PII leakage, and jailbreaks using both deterministic and LLM-based detectors, with PII redaction available in the SDK.
- Evaluation dataset management: Build and store test case datasets from production traces, with structured feedback from users and domain experts feeding back into judge calibration.
- AI Gateway with budget policies: Set spending thresholds per day, week, or month across providers, with webhook alerts to tools like Slack or PagerDuty when limits are crossed.
MLflow Integrations
MLflow integrates with 40+ LLMs and AI agents, including OpenAI Agent, Anthropic, LlamaIndex, Agno, Amazon Bedrock, Groq, OpenTelemetry, and TypeScript. The REST API is also available for custom connections.
Pros and Cons
Pros:
- Prompt lineage connects changes with evaluations
- Production judges score hallucinations and drift
- Self-hosted traces keep data in-house
Cons:
- Alerting options remain less clearly documented
- Nested agent traces can become confusing
Learn more about MLflow:
Best for full-lifecycle LLM evaluation with gateway ops- Free plan + free demo available
- Pricing upon request
Bifrost tracks model rankings, costs, and latency across AI providers. Bifrost is an LLM observability platform that covers the full development lifecycle, combining distributed tracing, prompt versioning and experimentation, automated and human-in-the-loop evaluation, and an open-source LLM gateway.
Who Is Bifrost Best For?
Bifrost is a great fit for regulated enterprise teams that need in-VPC deployment, SOC 2 Type II, HIPAA compliance, and human-in-the-loop evaluation in one platform.
Why I Picked Bifrost
The full-lifecycle coverage is what keeps Bifrost on my shortlist. I use it to run A/B tests on prompt versions in the Prompt IDE, then carry those same datasets directly into production evaluation without any manual export step. What I find genuinely useful is that the open-source Bifrost gateway layers on budget tracking per team or virtual key alongside gateway-level rate limits, so I can manage cost governance and observability from one connected setup rather than two separate tools.
Bifrost Key Features
- Prompt IDE with version comparison: A multimodal prompt playground where you can run experiments across combinations of prompts, models, context, and tools, with side-by-side version diffing.
- Agent simulation: Test agent interactions across different scenarios and user personas, including voice agents, before pushing changes to production.
- CI/CD pipeline integration: Plug evaluation runs directly into your deployment pipeline, with scheduled runs available on the Business tier and above.
- OpenTelemetry compatibility: Export trace data to external platforms like New Relic and Snowflake using OTel-native forwarding, with SDKs in Python, TypeScript, Go, and Java.
Bifrost Integrations
Bifrost supports integration across AI SDKs, secret management platforms, and identity systems, including OpenAI, Anthropic, Google GenAI, AWS Bedrock, LiteLLM, AWS Secrets Manager, Google Secret Manager, and Okta. An API for custom connections is also available.
Pros and Cons
Pros:
- Combines automated scoring with human review
- Traces sessions, requests, and individual spans
- Connects experimentation, evaluation, and observability
Cons:
- Granular cost attribution remains unclear
- Short retention limits historical investigations
Learn more about Bifrost:
Opik lets users trace LLM calls with full span details and token usage. Opik is an LLM observability platform that covers trace logging, output quality evaluation, prompt versioning, and cost tracking across LLM applications, agents, and RAG pipelines.
Who Is Opik Best For?
Opik is a strong fit for ML/AI engineers and LLMOps teams who want open-source flexibility with the option to scale into a fully managed, enterprise-grade deployment.
Why I Picked Opik
I picked Opik as one of the best because its open-source core is genuinely full-featured, not a stripped-down teaser. I love that you can self-host it with unlimited users, spans, and retention using the same codebase as the cloud version. For evaluation depth specifically, Opik ships 30+ LLM-as-a-judge metrics alongside Test Suites that run pass/fail assertions, A/B tests, and regression tests against versioned prompts, which makes it easy to catch output quality regressions before they reach production.
Opik Key Features
- Expert annotation UI: Review individual traces and full multi-turn conversations with multiple team members using custom labels for human-in-the-loop quality feedback.
- Guardrails: Block content violations, PII exposure, and compliance risks in real time using built-in PII, Topic, and Custom guardrail types.
- Cost Intelligence dashboard: Track Claude Code and Codex spend in real time, broken down by developer and team, with configuration auditing and cost-saving recommendations.
- Agent Optimizer: Automate prompt engineering using one of six built-in algorithms, with results surfaced directly in the UI for comparison.
Opik Integrations
Opik supports 40+ AI framework and model-provider connections, including LlamaIndex, OpenAI, Google ADK, Haystack, CrewAI, and OpenTelemetry. It also supports REST API and webhooks for custom integrations.
Pros and Cons
Pros:
- Ollie connects trace diagnosis with regression fixes
- Deep evaluation covers 30+ quality metrics
- Open-source core supports unlimited self-hosted usage
Cons:
- Guardrails omit explicit jailbreak detection
- Production alerting remains relatively basic
Learn more about Opik:
Best for linking prompt versions to production traces- Free plan + free demo available
- From $49/month
PromptLayer lets users evaluate LLM outputs with scoring columns and batch runs. PromptLayer is an AI engineering platform that combines prompt version management, LLM observability and tracing, and an evaluation harness for testing prompt and model changes against production data.
Who Is PromptLayer Best For?
PromptLayer is a strong fit for ML/AI engineering teams that need to connect every production LLM request back to the exact prompt version that generated it.
Why I Picked PromptLayer
PromptLayer earns its spot on my shortlist because every production request is tied to the exact prompt version that generated it, so I can group cost, latency, and quality metrics by version and see immediately when a change moved the needle. I also like that A/B rollouts are built in: I can gradually shift traffic to a new prompt version and compare its metrics against the previous one in the same view. The trace replay into Playground is a practical addition that lets me reproduce a bad output and iterate on the prompt without guessing at the original context.
PromptLayer Key Features
- OpenTelemetry-native tracing: PromptLayer accepts traces via a native OTLP/HTTP endpoint that runs asynchronously, so it adds no latency to your LLM requests.
- Production data to versioned datasets: Filter production requests by score, metadata, or model and convert them directly into datasets for use in evaluation pipelines and backtests.
- Model and prompt comparisons: Run bulk comparison jobs to test different models or parameter settings against the same inputs in one evaluation run.
- No-code prompt editor: Lets non-technical teammates edit, review, and deploy prompt changes without touching application code.
PromptLayer Integrations
Native integrations include OpenTelemetry, LangChain, LlamaIndex, OpenAI, Cohere, Hugging Face, Mistral, Amazon Bedrock, and Gemini Enterprise Agent Platform. It also supports SDKs, REST API, and webhooks for custom integrations.
Pros and Cons
Pros:
- Backtests against real production data
- Replays failed traces in Playground
- Links prompts to production traces
Cons:
- Built-in safety detection is absent
- Native alerting remains limited
Learn more about PromptLayer:
Best for tracing agents across complex pipelines- Free plan + free demo available
- From $50/month
Visit WebsiteCustomer Rating:4.3/5
Arize AX surfaces fabricated financial claims in LLM agent outputs. Arize AX is an LLM observability tool built around OpenTelemetry-compliant tracing, agent trajectory visualization, online and offline evaluation, prompt versioning, and real-time monitoring across multi-step LLM pipelines and agent workflows.
Who Is Arize AX Best For?
Arize AX suits teams running multi-agent or RAG pipelines in production who need deep visibility across every span and decision point.
Why I Picked Arize AX
Arize AX earns its spot on my shortlist because of how it handles agent trajectory visualization across multi-step pipelines. I love that it renders agent runs as both path and graph views, so when a multi-agent workflow misfires, I can pinpoint exactly which span broke down. Swarm Observability layers on top of that, tracking status and performance across all agents in real time, including third-party ones.
Arize AX Key Features
- Signal issue detection: Signal automatically surfaces problems in your traces, identifies root causes, and generates suggested fixes for your review.
- Online and offline evaluations: Run evals on live traces as they arrive, or batch-evaluate against saved datasets and experiments before shipping changes.
- Prompt versioning and comparison: Version, serve, and diff prompts side by side, then run end-to-end experiments on agents to validate changes before deployment.
- Session evals: Score multi-turn conversations as complete sessions, giving you a trajectory-level quality assessment across the full interaction.
Arize AX Integrations
Arize AX integrates with OpenAI, Anthropic, Amazon Bedrock, Cohere, Gemini Enterprise Agent Platform, Ollama, OpenRouter, LlamaIndex, CrewAI, and OpenTelemetry. A REST API for custom connections is also available.
Pros and Cons
Pros:
- OpenTelemetry instrumentation spans complex pipelines
- Trace and session evaluations cover full interactions
- Agent trajectories show path and graph views
Cons:
- Custom code evaluators aren’t broadly available
- Lower tiers retain traces briefly
Learn more about Arize AX:
Best for linking LLM traces to product analytics- Free plan available
- Pricing upon request
Visit WebsiteCustomer Rating:4.5/5
PostHog tracks AI costs, traces, and users in one dashboard. PostHog offers an LLM monitoring solution that covers trace logging, multi-step agent tracing, output quality evaluations, token cost tracking, prompt management, and anomaly detection—all connected to PostHog's broader product analytics, session replay, and experimentation suite.
Who Is PostHog Best For?
PostHog is best fit for suits product-led teams building LLM features who want trace data and user behavior in one platform.
Why I Picked PostHog
I picked PostHog as one of the best because it's the only LLM observability tool I've seen that ties each trace directly to a user's session replay, product analytics, and error tracking in one place. When a generation fails or quality drops, you can pull up the exact session where it happened rather than cross-referencing separate tools. I also like that the Self-driving scout automatically files regression reports when cost, latency, or evaluation scores slip against a baseline, so you're not manually watching dashboards to catch degradation.
PostHog Key Features
- Cost and token attribution: Break down spend by model, feature, or individual customer across all LLM calls with historical trend tracking.
- Evaluation sampling controls: Set sampling rates from 0.1% to 100% with property filters so LLM-judge, code-based, and sentiment evaluations run continuously on live traffic.
- Prompt playground and versioning: Write, test, and version prompts directly in the UI, then roll them out gradually using built-in feature flags and A/B experiments.
- Anomaly detection and regression reports: Monitor cost, latency, errors, and evaluation scores against a baseline, with automated investigation reports filed when a slice regresses.
PostHog Integrations
PostHog has documented 40+ integrations with OpenAI, Anthropic, Gemini, Vercel, Convex, Groq, Ollama, Mastra, and Hugging Face. It also offers an API for custom connections.
Pros and Cons
Pros:
- Combines judge, code, and sentiment evaluations
- Files regression reports from anomaly detection
- Links LLM traces to session replays
Cons:
- Safety evaluations cannot block harmful outputs
- Self-hosting lacks Kubernetes and support
Learn more about PostHog:
Best for open-source tracing with prompt versioning- Free plan + free demo available
- From $29/month
Visit WebsiteCustomer Rating:4.5/5
Langfuse tracks traces, model costs, and eval scores in one dashboard. Langfuse is an open-source LLM observability platform that combines trace logging, prompt version control, output evaluation, and dataset management for AI engineering teams.
Who Is Langfuse Best For?
Langfuse is a strong fit for ML/AI and LLMOps engineers who need a single platform for tracing, prompt versioning, and evaluation—especially teams with data-residency requirements that make self-hosting a priority.
Why I Picked Langfuse
Langfuse earns its spot on my shortlist because it pairs full trace logging with built-in prompt version control, which is a combination I haven't found handled this well in other open-source tools. I especially like that prompt labels let you swap models or prompt versions per environment without touching your deployment code. On top of that, Langfuse links every prompt version directly to the traces that used it, so you can compare cost, latency, and evaluation scores across versions in one place.
Langfuse Key Features
- Annotation queues: Route production outputs to human reviewers for manual scoring and labeling directly inside the platform.
- Session and user tracking: Group multi-turn conversations into sessions and monitor traces and costs per individual user.
- Dataset management: Build structured test datasets from production traces and run experiments to compare prompt or model changes before release.
- OpenTelemetry-native tracing: Send trace data using OpenTelemetry alongside native Python and JavaScript SDKs, with support for 100+ framework integrations.
Langfuse Integrations
Langfuse offers 100+ integrations, including OpenAI Agents, LiteLLM, Koog, Cohere, Gemini, Ollama, Groq, Slack, and Deepseek. It also supports Python and JavaScript/TypeScript SDKs, OpenTelemetry, webhooks, and a public API.
Pros and Cons
Pros:
- Dataset experiments connect production feedback to evaluation
- Prompt versions link directly to traces
- Open-source tracing supports self-hosted deployments
Cons:
- Alerting lacks anomaly detection and incident workflows
- Self-hosting requires several infrastructure components
Learn more about Langfuse:
Best for evaluation-to-production scoring in one loop- Free plan + free demo available
- From $249/month
Braintrust tracks LLM request counts, latency, cost, and token usage over time. Braintrust is an LLM observability tool that combines production trace logging, output quality evaluation, prompt experimentation, and automated scoring into a single environment.
Who Is Braintrust Best For?
Braintrust is a strong fit for ML/AI and LLMOps engineers who need to close the loop between production trace data and evaluation workflows in a single platform.
Why I Picked Braintrust
Braintrust earns its spot on my shortlist because it closes the gap between production observability and evaluation in one continuous loop. I love how online production scoring lets you apply LLM-as-judge or code-based scorers directly to live traces, then trigger alerts when scores drop below a threshold, like
factuality scores < 0.8. You can also convert those flagged traces into versioned eval datasets with one click, so your next experiment is grounded in real failure cases rather than synthetic data.Braintrust Key Features
- OpenTelemetry ingestion: Accept traces via an OTLP exporter, Braintrust's own span processor, or an OTel Collector, with automatic LLM span conversion.
- Brainstore and Nitro search: Query millions of trace logs at speed using Braintrust's purpose-built AI data storage and query engine.
- SQL-based alerting: Define log, time-window, and environment alerts using SQL filters, with routing to Slack or webhooks for tools like PagerDuty and Opsgenie.
- Versioned prompt and dataset management: Tag prompts and datasets across production, staging, and development environments, with alerts that fire when environment assignments change.
Braintrust Integrations
Braintrust offers 80+ integrations, including OpenAI, Anthropic, AgentScope, AWS Lambda, Agno, AWS Bedrock, AND Azure AI Foundry. It also offers an MCP, webhooks, an API, and Braintrust Gateway for custom connections.
Pros and Cons
Pros:
- Nitro searches millions of long trace logs
- LLM-as-judge and code scoring run online
- Production traces become versioned evaluation datasets
Cons:
- No native PII or prompt-injection detection
- Alerts batch notifications instead of single events
Learn more about Braintrust:
Best for LangChain and LangGraph tracing- Free plan + free demo available
- From $39/user/month
LangSmith tracks LLM latency and token throughput across multiple models over time. LangSmith is an LLM observability platform that combines trace logging, prompt management, output evaluation, and real-time monitoring dashboards in a single tool, with native support for OpenTelemetry and SDKs across Python, TypeScript, Go, and Java.
Who Is LangSmith Best For?
LangSmith is a strong fit for ML/AI and LLMOps engineers building or scaling production applications with LangChain or LangGraph.
Why I Picked LangSmith
LangSmith earns its spot on my shortlist because its tracing is built specifically for LangChain and LangGraph workflows, making it the most natural fit if you're already in that ecosystem. I love how SmithDB-backed nested trace views let you step through every node of a LangGraph agent run, with full-text search returning results across millions of traces in under a second. Pair that with live dashboards breaking down latency at P50/P99, token usage, and cost per run, and you get a clear picture of exactly where your pipeline is struggling.
LangSmith Key Features
- Output evaluation pipeline: Run LLM-as-judge or code-based evaluations directly in production, with results feeding into your monitoring dashboards automatically.
- Annotation queues: Route outputs to human reviewers for labeling and feedback collection, with scores surfaced across your live monitoring views.
- Alert threshold configuration: Set custom alert thresholds across latency, error rate, and cost metrics, with delivery via webhook or PagerDuty.
- Flexible deployment options: Run LangSmith on managed cloud (US or EU), bring-your-own-cloud, hybrid, or fully self-hosted on Kubernetes in AWS, GCP, or Azure.
LangSmith Integrations
LangSmith integrates with OpenAI Agent, Anthropic, LlamaIndex, Agno, Amazon Bedrock, Groq, OpenTelemetry, and TypeScript. The REST API is also available for custom connections.
Pros and Cons
Pros:
- P50/P99 dashboards expose latency and cost
- Human annotations feed monitoring feedback scores
- LangChain and LangGraph tracing feels native
Cons:
- No built-in prompt-injection detection
- Base traces expire after fourteen days
Learn more about LangSmith:
Outras ferramentas de observabilidade de LLM
Eis algumas plataformas adicionais que não entraram na minha lista restrita, mas que ainda vale a pena conhecer:
- 11HoneyHiveBest for enterprise agent observability at scale
- 12Confident AIBest for eval-gated prompt merges in CI/CD
- 13New RelicBest for APM-native LLM and infra monitoring
- 14PortkeyBest for gateway-native LLMOps with VPC hosting
- 15Evidently AIBest for hallucination and drift checks
- 16LangWatchBest for multi-turn agent simulation and tracing
- 17HeliconeBest for cost and token tracking via AI gateway
- 18TruLensBest for RAG and agent evaluation depth
- 19LunaryBest for self-hosted LLM tracing with SOC 2 compliance
How I Evaluate LLM Observability Tools
I split my evaluation into baseline criteria every tool must meet—like trace logging and output quality evaluation—and differentiators that separate the best from the rest.
Core Functionality (Table Stakes for This List)
When I'm selecting tools for my list, I rank each one on a scale from 0 (does not offer the functionality) to 5 (excels in this area) for each core functionality listed below. I then calculate the tool's total score into a percentage, and use that to help me assess its overall fit for the list.
- Trace logging and visualization: I check whether the tool can render nested, multi-step traces across agent calls, RAG retrievals, and tool use—not just flat request logs.
- Output quality evaluation: I look for built-in scoring, custom evaluators, and human review options that catch hallucinations or relevance drops in production outputs.
- Prompt and model versioning: Each tool should link prompt or model changes to shifts in latency, cost, or quality so you can pinpoint what caused a regression.
- Real-time alerting and monitoring: I evaluate whether the platform surfaces anomalies in live traffic and routes alerts to tools like Slack or PagerDuty without manual polling.
- Cost and token tracking: Granular breakdowns per call, model, or project matter here—aggregate token consumption counts alone aren't enough to manage spend across teams.
- Dataset and feedback management: I look for the ability to curate test sets from production traces and feed user feedback back into evaluation loops for continuous improvement.
Once I have a list of tools that meet the criteria, I consider what sets each platform apart.
Differentiating Factors (What Sets Vendors Apart)
Here's how I compare and contrast different vendors:
Standout Features
I look for guardrails and safety detection that flag prompt injection, PII leakage, and toxic content in real time—especially when a team runs customer-facing agents where a single jailbreak can cause real damage. Self-hosted and on-prem deployment options matter just as much, since teams in regulated industries need full control over where prompts and completions are stored. I also evaluate prompt playground and diffing capabilities, checking whether you can compare prompt versions side by side with live outputs before pushing changes to production.
Beyond Features
I check whether a tool can run in a VPC or on-prem setup, since teams handling healthcare or financial data often can't send prompts to a shared cloud. Pricing model matters just as much—usage-based billing tied to trace volume can spiral fast once you move past prototyping, so I look for transparent tiers or open-source options that let you forecast costs before committing. I also evaluate how well each platform fits into existing stacks, checking for native SDK support across Python and JS and pre-built connectors for frameworks like LangChain or LlamaIndex.
Como escolher a ferramenta de observabilidade de LLM adequada
Compare as principais capacidades abaixo para encontrar a ferramenta adequada às suas prioridades.
| Se a sua prioridade for | Procure |
|---|---|
| Se a sua prioridade forReconstruir falhas de agentes | ProcureRastreios aninhados exportáveis com eventos de recuperação, ferramentas e modelos |
| Se a sua prioridade forComprovar a qualidade dos resultados | ProcureRegistos de avaliação reproduzíveis com critérios de pontuação e notas dos avaliadores |
| Se a sua prioridade forControlar alterações aos prompts | ProcureHistórico de versões que associe prompts, modelos, resultados e resultados de avaliações |
| Se a sua prioridade forGerir o risco operacional | ProcureTermos escritos sobre retenção, controlo de acesso, eliminação e implementação |
| Se a sua prioridade forPrever os custos | ProcureRegistos por pedido que indiquem tokens, modelos e unidades de utilização aplicáveis |
Como avaliar a sua lista restrita
- Execute uma tarefa de avaliação definida: Envie um fluxo de trabalho de agente com várias etapas, incluindo uma chamada de recuperação e um erro de ferramenta, e depois analise o rastreio exportado no prazo de 30 minutos.
- Solicite um documento de avaliação: Peça um relatório de exemplo que apresente os critérios do avaliador, os cálculos da pontuação, as anotações humanas e o rastreio de origem de 10 resultados.
- Abra um pedido de suporte: Envie uma questão de rastreio reproduzível e exija uma resposta escrita, a identificação do responsável pelo escalamento e um prazo-alvo de resolução no prazo de cinco dias úteis.
- Solicite a redação contratual: Obtenha cláusulas escritas que abranjam a retenção de dados, os prazos de eliminação, o acesso dos subprocessadores e se os prompts são utilizados para treinar os modelos do fornecedor.
- Escolha o seu compromisso: Opte pelo controlo proporcionado por uma solução de código aberto e autoalojada em detrimento da conveniência de uma solução gerida, ou aceite uma infraestrutura gerida pelo fornecedor para reduzir a responsabilidade operacional.
O que são as ferramentas de observabilidade de LLM?
As ferramentas de observabilidade de LLM são plataformas que rastreiam, monitorizam e avaliam aplicações de modelos de linguagem de grande dimensão. Capturam prompts, respostas, chamadas ao modelo, etapas de recuperação, utilização de ferramentas, latência, tokens, custos e erros em fluxos de trabalho com várias etapas. As equipas utilizam estes dados para investigar falhas, medir a qualidade dos resultados, detetar problemas em produção e associar alterações aos prompts ou aos modelos aos resultados operacionais.
Funcionalidades
Ao avaliar diferentes plataformas, esteja atento às seguintes funcionalidades fundamentais:
- Registo e visualização de rastreios: Regista eventos aninhados em chamadas ao modelo, etapas de recuperação, prompts, ferramentas e lógica da aplicação. As linhas cronológicas visuais ajudam a localizar falhas e a compreender como cada etapa afetou a resposta final.
- Avaliação da qualidade dos resultados: Aplica critérios de pontuação predefinidos ou personalizados às respostas geradas. As equipas podem analisar a precisão, a relevância, a segurança e outras medidas de qualidade em conjuntos de dados de teste e exemplos de produção.
- Controlo de versões de prompts e modelos: Armazena em conjunto modelos de prompts, configurações de modelos e históricos de alterações. Isto permite comparar versões e identificar se uma alteração afetou a qualidade, a latência, os erros ou os custos de utilização.
- Monitorização e alertas em tempo real: Acompanha o comportamento da aplicação em funcionamento e assinala problemas como o aumento das taxas de erro, picos de latência ou utilização invulgar. O encaminhamento de alertas pode enviar notificações para ferramentas de resposta a incidentes e de colaboração.
- Contagem de tokens e acompanhamento de custos: Regista os tokens de entrada, os tokens de saída, a utilização do modelo e os custos associados a pedidos individuais. Os filtros por projeto, ambiente, utilizador ou modelo ajudam as equipas a investigar padrões de despesa.
- Gestão de conjuntos de dados e feedback: Converte rastreios de produção selecionados em conjuntos de dados de avaliação e armazena o feedback dos revisores com cada exemplo. Isto cria um registo repetível para testar alterações e acompanhar a qualidade ao longo do tempo.
- Pesquisa e filtragem: Permite encontrar rastreios por ID do pedido, utilizador, modelo, versão do prompt, estado, latência, etiquetas ou metadados. A filtragem detalhada reduz o tempo necessário para isolar um incidente específico.
- Controlos de acesso e retenção de dados: Define quem pode visualizar, exportar, eliminar ou conservar prompts e respostas. Estes controlos ajudam as equipas a aplicar políticas internas de segurança e a gerir registos de produção sensíveis.
- Suporte para integrações: Liga-se a estruturas de aplicações, SDKs de linguagens, normas de telemetria e sistemas de colaboração. O amplo suporte para integrações permite às equipas adicionar observabilidade sem reconstruir os fluxos de trabalho existentes.
Funcionalidades comuns de IA
Para além das funcionalidades padrão das ferramentas de observabilidade de LLM acima indicadas, muitas soluções incorporam IA através de funcionalidades como:
- Pesquisa de rastreios em linguagem natural: Permite consultar dados de rastreios utilizando linguagem corrente, em vez de escrever filtros ou consultas a bases de dados. A IA interpreta o pedido e devolve pedidos, erros, utilizadores ou etapas de fluxos de trabalho relevantes.
- Análise automatizada da causa principal: Examina eventos relacionados em chamadas ao modelo, etapas de recuperação, ferramentas e erros da aplicação. Identifica causas prováveis, como uma chamada de recuperação falhada, uma alteração ao prompt ou o tempo e padrão de resposta do modelo.
- Resumos de rastreios gerados por IA: A plataforma converte rastreios longos e com várias etapas em explicações breves do que aconteceu. Estes resumos ajudam as equipas de suporte e de infraestrutura a analisar incidentes sem ler manualmente todos os eventos.
- Agrupamento semântico: Agrupa rastreios pelo seu significado, em vez de depender apenas de etiquetas, códigos de estado ou correspondências exatas de texto. As equipas podem utilizar estes grupos para encontrar tipos de pedidos recorrentes, padrões de falhas ou perguntas inesperadas dos utilizadores.
- Deteção de injeção de prompts: Analisa as entradas dos utilizadores e as interações com o modelo à procura de instruções concebidas para substituir os prompts do sistema ou manipular o comportamento do agente. Os resultados da deteção podem apoiar a investigação, as regras de bloqueio e as revisões de segurança.
- Deteção de dados sensíveis: Identifica informações pessoais, financeiras, de saúde ou confidenciais nos prompts e nas respostas. As equipas podem utilizar os resultados para ocultar registos, analisar violações de políticas e limitar a exposição nos rastreios armazenados.
Vantagens
Uma plataforma de observabilidade de LLM oferece várias vantagens à sua equipa e à sua empresa. Eis algumas das que pode esperar:
- Investigação mais rápida de incidentes: Os rastreios aninhados ligam os prompts, as chamadas ao modelo, os passos de recuperação, as ferramentas, a latência e os erros numa única linha temporal.
- Maior qualidade dos resultados: Os avaliadores integrados, os critérios de pontuação personalizados, os conjuntos de dados e as revisões humanas ajudam a identificar alucinações, problemas de relevância e questões de segurança.
- Fluxos de trabalho de produção mais seguros: A deteção de injeção de prompts e de dados sensíveis pode assinalar entradas, respostas e interações de agentes arriscadas para revisão.
- Impacto das alterações mais claro: Os históricos das versões dos prompts e dos modelos ligam as alterações de configuração às variações na qualidade, latência, erros e utilização de tokens.
- Controlo de custos melhorado: Os registos de tokens e custos por pedido mostram como a utilização varia consoante o modelo, projeto, ambiente ou utilizador.
- Melhor resposta operacional: A monitorização em tempo real, os rastreios pesquisáveis e os alertas ajudam as equipas a detetar anomalias e a encaminhar incidentes para os canais de resposta.
Preços
Para selecionar a ferramenta certa, é necessário compreender os modelos de preços e os planos disponíveis. Os custos variam consoante as funcionalidades, a dimensão da equipa, os complementos e o volume de utilização. A tabela abaixo resume os planos comuns, os preços médios e as funcionalidades normalmente incluídas no software de observabilidade de LLM.
Tabela de comparação de planos para ferramentas de observabilidade de LLM
| Tipo de plano | Preço médio | Funcionalidades comuns |
|---|---|---|
| Tipo de planoPlano gratuito | Preço médio$0/month | Funcionalidades comunsRegisto básico de rastreios, retenção limitada, apoio da comunidade e limites de utilização. |
| Tipo de planoPlano pessoal | Preço médio$10-$50/user/month | Funcionalidades comunsVisualização de rastreios, acompanhamento de tokens, monitorização de prompts, gestão de conjuntos de dados e apoio por e-mail. |
| Tipo de planoPlano empresarial | Preço médio$100-$500/month | Funcionalidades comunsAvaliações avançadas, alertas em tempo real, permissões de equipa, integrações e retenção de dados prolongada. |
| Tipo de planoPlano empresarial | Preço médio$500-$5,000/month | Funcionalidades comunsLimites de utilização personalizados, início de sessão único, registos de auditoria, opções de implementação privada, apoio dedicado e controlos de retenção baseados em contrato. |
Perguntas frequentes
Eis algumas respostas a perguntas comuns sobre ferramentas de observabilidade de LLM:
As ferramentas de observabilidade de LLM substituem a monitorização tradicional de aplicações?
Não, as ferramentas de observabilidade complementam a monitorização tradicional de aplicações ao captarem prompts, respostas, definições do modelo, passos de recuperação e pontuações dos avaliadores. Ligue ambas ao investigar incidentes em produção. Por exemplo, um rastreio de LLM pode revelar uma consulta de recuperação malformada, enquanto os registos da aplicação mostram o tempo limite da base de dados que ocorreu em seguida. Em conjunto, estas informações ajudam a distinguir o comportamento do modelo dos problemas da aplicação ou da infraestrutura.
As ferramentas de observabilidade de LLM conseguem processar dados de produção sensíveis?
Sim, algumas ferramentas de observabilidade de LLM disponibilizam controlos para dados de produção sensíveis. Verifique os períodos de retenção, os fluxos de trabalho de eliminação, os detalhes da encriptação, as permissões de acesso e os controlos de exportação antes da implementação. Pergunte se o fornecedor utiliza prompts ou respostas para treinar os seus modelos. Deve também confirmar o acesso dos subprocessadores e os modelos de implementação disponíveis. Uma implementação autónoma ou privada pode limitar o local para onde os dados dos rastreios são enviados. Durante a avaliação, envie registos de teste anonimizados e verifique como o mascaramento aparece nos rastreios armazenados, nas exportações e nos resultados dos avaliadores.
Deve escolher uma ferramenta de observabilidade de LLM de código aberto ou gerida?
Escolha software de código aberto quando o controlo da implementação, o acesso ao código-fonte e uma gestão previsível da infraestrutura forem os fatores mais importantes. Escolha uma plataforma gerida quando a sua equipa precisar de armazenamento alojado pelo fornecedor, atualizações e apoio. Compare mais do que o preço da subscrição. Inclua o tempo de engenharia necessário para a implementação, as atualizações, os controlos de acesso, as cópias de segurança e as políticas de retenção. Em seguida, teste o mesmo fluxo de trabalho em ambas as opções. Analise o nível de detalhe dos rastreios, a configuração dos avaliadores, a entrega dos alertas e os formatos de exportação. A melhor escolha é a que corresponde aos seus requisitos de segurança e à capacidade operacional disponível.
Como testar uma ferramenta de observabilidade de LLM antes de a comprar?
Teste-a com um fluxo de trabalho composto por várias etapas, com recuperação, chamadas a ferramentas e falhas. Confirme se o rastreio preserva a ordem dos eventos, as entradas, as saídas, a latência, os tokens e os detalhes dos erros. Em seguida, reveja as pontuações dos avaliadores e o acompanhamento das versões dos prompts. Por fim, exporte os registos e analise os nomes dos campos, os carimbos de data e hora e o comportamento da redação. Este teste revela lacunas que as demonstrações de produtos frequentemente ocultam.