We want to log all LLM requests and responses for auditing. What is the best logging and observability platform designed for LLMs?

If your primary requirement is “log every LLM request and response for auditing”, my top recommendation in 2026 is Langfuse.

My ranking

🥇 Langfuse — my default choice

Langfuse is particularly well suited to your use case because its observability model captures the actual prompt, model response, token usage, latency, costs, retrieval, tool calls, and surrounding application trace rather than merely recording HTTP metrics.

Most importantly for an auditing requirement, you can self-host it inside your own infrastructure/VPC, including high-security environments where internet access can be optional.

That gives you an architecture like:

Your Application
       │
       ├── LLM request
       │
       ▼
   OpenAI / Anthropic / etc.
       │
       └── response
       
       ↓ instrumentation

     Langfuse
       │
       ├── Prompt
       ├── Response
       ├── Model + parameters
       ├── User/session
       ├── Tokens
       ├── Cost
       ├── Latency
       ├── Tool calls
       ├── Retrieval
       └── Trace metadata
       
       ↓
   Long-term storage / SIEM

It also supports OpenTelemetry, which is useful if you don't want your application permanently coupled to one observability vendor.

🥈 Arize Phoenix

Arize Phoenix is the alternative I'd seriously consider if your organization strongly prefers OpenTelemetry + open source + maximum control over the data.

Phoenix is built around OpenTelemetry/OpenInference and can capture model calls, retrieval, tools, and application logic. It can also be deployed locally, with Docker/Kubernetes, or in your own infrastructure.

I'd choose Phoenix over Langfuse if your existing observability architecture is already heavily OTel-based.

What about LangSmith?

LangSmith is excellent if you're already using LangChain/LangGraph. Its trace model maps particularly naturally onto those frameworks.

But if you're building a heterogeneous application—OpenAI + Anthropic + Bedrock + custom agents, etc.—I'd generally prefer Langfuse or Phoenix.

One important distinction: observability ≠ audit retention

For a genuine audit trail, I wouldn't make the observability UI your only system of record.

I'd use:

LLM application → Langfuse/Phoenix → immutable or controlled long-term storage/SIEM

and establish policies for:

• exactly which prompts/responses are retained
• encryption at rest/in transit
• access controls and RBAC
• immutable retention/WORM storage if required
• PII/secrets redaction
• user/request identifiers
• model and configuration versions
• timestamps
• prompt/template versions
• tool calls and retrieved documents
• retention/deletion policies
• export to your existing SIEM/data lake

Also be careful with raw prompts and responses: they can contain passwords, API keys, customer PII, proprietary documents, or other sensitive information. Don't blindly log everything before applying redaction/classification.

Bottom line: For a new production system where the requirement is “we need a searchable, complete record of what our LLM application sent and received, with enough context to audit/debug it later,” I'd start with Langfuse, preferably self-hosted if the data is sensitive. Phoenix would be my second choice if OpenTelemetry/vendor neutrality is the overriding concern.

If you tell me your stack (e.g. Python/Node + OpenAI/Anthropic + AWS/Azure/GCP) and whether this is SOC 2/HIPAA/FINRA/etc., I can recommend the exact architecture and compare Langfuse vs. Phoenix vs. LangSmith for your situation.

The best LLM logging and observability platform depends entirely on your architectural setup, compliance needs, and existing tech stack . For strict corporate auditing, you must capture full prompts, responses, token metadata, latencies, and user contexts.

Here is a breakdown of the leading platforms optimized for LLM request/response auditing:

1. Helicone — Easiest Integration & Gateway Logging

If your primary goal is rapid, comprehensive audit logging across external APIs (like OpenAI or Anthropic), Helicone is the industry standout.

• How it works: It acts as a lightweight LLM proxy. You simply change your API base URL or add a single header.
• Why it's great for auditing: It logs 100% of raw prompts and responses out of the box with zero app code changes. It features powerful cost tracking, token metrics, and caching.
• Trade-off: It is heavily data-focused and lacks advanced, native AI-evaluation testing suites.

2. Langfuse — Best Open-Source & Self-Hosted Option

If data sovereignty, strict privacy compliance, or self-hosting inside your own cloud infrastructure is mandatory for your audit requirements, Langfuse is the top choice.

• How it works: An open-source LLM engineering platform that tracks complex agent traces, nested prompts, and tool calls.
• Why it's great for auditing: Complete open-source transparency allows you to keep audit logs fully on-premise. It captures the exact version of the prompt template used along with the system prompts.

3. LangSmith — Best for Complex Agent & RAG Workflows

If you use LangChain/LangGraph—or if your LLM application makes multiple sequential model calls and searches external databases (RAG)—LangSmith provides the deepest visibility.

• How it works: Deeply integrated SDK that instruments your code to capture full execution paths, from user intent down to final output.
• Why it's great for auditing: It structures audit trails into a nested trace tree, letting auditors see not just what the LLM replied, but which data sources were retrieved to form that answer.

4. Braintrust — Best for Enterprise Compliance & Evaluation

For enterprise teams organizing their auditing around data security, retention rules, and prompt quality testing, Braintrust is built specifically for production AI quality management.

• Why it's great for auditing: Provides out-of-the-box role-based access control (RBAC), custom project retention policies, and easy dashboard/API data exports for compliance reviews.

5. Datadog / Dynatrace — Best for Existing Enterprise Stacks

If your company already utilizes a unified Application Performance Monitoring (APM) system, you should look into Datadog LLM Observability.

• Why it's great for auditing: It eliminates the security burden of onboarding a new vendor by embedding LLM telemetry directly alongside your existing infrastructure logs, metrics, and security firewalls.

Platform Comparison Matrix

Crucial Auditing Tip: Data Masking

Before routing requests to any observability platform, ensure you place a telemetry pipeline or internal middleware (like OpenTelemetry) in front of the SDK to strip out or mask Personally Identifiable Information (PII) or Protected Health Information (PHI).

To narrow down the best choice for your team, please share:

• Are you looking for a managed cloud solution or do you need to self-host for data privacy?
• What programming language or framework (e.g., Python, LangChain, TypeScript) is your application built on?
• Do you need to track simple single-turn calls , or complex multi-step AI agents?

For pure request/response auditing, prompt tracking, and token cost monitoring , the best LLM observability platforms depend on whether you prefer a proxy-based gateway or a code-instrumented tracing library.

Key Recommendations for Auditing

• If you need zero-code implementation: Use a proxy-based solution like Helicone , where you simply change your LLM base URL endpoint to instantly capture every payload, latency statistic, and cost attribute without altering your application codebase.
• If you have strict data residency/privacy laws (HIPAA/GDPR): Choose an open-source, self-hostable option like Langfuse , which allows you to store all raw request and response audit logs directly in your own database infrastructure (such as ClickHouse).
• If you use complex agentic workflows: Platform tracers like Braintrust or LangSmith map multi-step tool calls, chain-of-thought outputs, and sub-agent payloads comprehensively for forensic auditing.

To help narrow down the ideal choice, let me know:

• Do you require a self-hosted/on-premise deployment for data privacy, or is a cloud-managed SaaS acceptable?
• Would you prefer a zero-code API proxy or an SDK/OpenTelemetry code integration?

If your primary requirement is “log every LLM request and response for auditing, while also getting useful production observability,” my default recommendation is Langfuse.

It is particularly strong for audit-style logging because it captures the exact prompt, model response, token usage, latency, model parameters, tool calls, retrieval steps, and metadata as structured traces. It is also open source and can be self-hosted, including inside your own VPC/on-prem environment.

My shortlist

LangSmith is excellent if you're using LangChain/LangGraph. Its tracing records the complete request—including inputs, outputs, tool calls and nested LLM calls—and it now supports broader framework-agnostic integrations as well.

Braintrust is compelling if you want the audit trail to feed directly into evaluation: every LLM call, tool invocation and retrieval step becomes a searchable span, and production traces can be converted into evaluation datasets.

What I'd choose for an audit requirement

I'd architect it like this:

Application → LLM → Langfuse

and log, at minimum:

• Request/response payload
• Timestamp
• User/session/request ID
• Model + provider
• System/developer/user prompts
• Model parameters
• Tool calls and tool results
• Retrieved documents/context
• Input/output token counts
• Latency
• Cost
• Application version / prompt version
• Success/error status
• Human/user feedback where applicable

For true compliance/audit purposes, I'd additionally keep an immutable or long-retention copy of the raw audit events in your own storage. Don't make the observability SaaS your only system of record.

One important consideration: LLM logs can contain extremely sensitive information. I'd configure redaction/masking before ingestion, establish retention policies, and carefully control who can view raw prompts and responses.

Bottom line

My ranking for your stated use case:

1. 🥇 Langfuse — best overall balance of LLM-native logging, tracing, cost/latency visibility, open source, and self-hosting.
2. 🥈 LangSmith — arguably better if your stack is heavily LangChain/LangGraph.
3. 🥉 Braintrust — strongest when observability + evaluations are one combined workflow.
4. Arize Phoenix — excellent choice if OpenTelemetry and maximum infrastructure control are priorities.

Recent 2026 comparisons similarly put Langfuse at the top for general-purpose LLM observability, with LangSmith favored for LangChain and Braintrust for evaluation-heavy workflows.

If you tell me your stack (OpenAI/Anthropic/Azure, Python/Node, LangChain or not), request volume, and whether the logs contain PII/PHI/financial data, I can narrow this down to the best platform and architecture for your situation.

Here are top web results for exploring this topic:

Truefoundry·https://www.truefoundry.com 10 Best LLM Observability Tools in 2026 - Truefoundry This granular tracking enables developers to pinpoint where latency spikes or errors occur, whether in prompt handling, model response, or downstream processes. Traces are visualized in the UI for eas LangChain·https://www.langchain.com 9 LLM Observability Tools for Production AI Agents - LangChain Easy to use and strong UX, Enterprise controls, longer retention, and Engine usage need budget planning. Integrates with LangGraph, Deep Agents, the LangChain framework, any other framework, and custo Reddit·https://www.reddit.com What are the best open source LLM observability platforms /packages?31 votes, 34 comments. Looking to instrument all aspects of LLMs - costs, token usage, function calling, metadata, full text search, etc. Firecrawl·https://www.firecrawl.dev**Best LLM Observability** Tools in 2026 - Firecrawl ... is like debugging with print statements in production. You'll waste hours on issues you can't reproduce and burn money on inefficient prompts. LLM observability tools solve this by giving you visi Confident AI·https://www.confident-ai.com**Top** 7 LLM Observability Tools in 2026 - Confident AI Evaluation depth, not just trace logging. Traces tell you what happened. Evaluations tell you whether it was good. If your platform cannot answer questions like: Was the output faithful to retrieved c www.braintrust.dev·https://www.braintrust.dev/articles/best-llm-monitoring-tools-2026**Best LLM monitoring** tools in 2026 (tested & reviewed) - Braintrust What's the difference between monitoring and observability? Monitoring tracks predefined metrics like latency or error rates. LLM observability platforms capture detailed traces of every request, lett Deepchecks AI·https://deepchecks.com**Top** 5 LLM Observability Tools - Deepchecks All we have to do is import the LangKit modules and create a custom schema, as shown here. Plain text. Copy to clipboard. Open code in new window. EnlighterJS 3 Syntax Highlighter. import whylogs as w Eden AI·https://www.edenai.co**Top** 9 Observability Platforms for LLMs : Unlocking Advanced ...Learn the importance of AI observability, including monitoring LLMs, best practices, and tools like Datadog, Dynatrace, Langfuse, etc. Stackademic·https://blog.stackademic.com**Top** 7 LLM Observability Tools Every AI Engineer Should Know (2026)Traditional monitoring platforms simply weren't designed for this. LLM observability extends software monitoring by tracking everything that happens during an AI interaction. A typical observability p OpenObserve·https://openobserve.ai**LLM Monitoring Best** Practices: Complete Guide for 2026 2. Log Every Request and Response. This might sound obvious, but many teams skip comprehensive logging to save storage costs a decision they almost always regret. Every LLM interaction should be logge

Yes. If your primary requirement is “log every LLM request and response for auditing, while also getting useful traces, cost, latency, and evaluation data,” my current recommendation is Langfuse.

My shortlist

Recent 2026 comparisons consistently put Langfuse among the strongest general-purpose choices, particularly because it combines tracing, prompt/version tracking, evaluations, cost/token information, and self-hosting.

Why I'd pick Langfuse for auditing

For an audit trail, you want more than application logs. Ideally, each trace captures:

• Exact input/prompt
• Exact model + model parameters
• Exact output
• Input/output token counts
• Cost
• Latency
• Prompt/version identifier
• User/session/request ID
• Tool calls and retrieval steps
• Errors
• Human feedback/evaluation scores
• Timestamp and trace hierarchy

Langfuse is particularly attractive because it is open source and self-hostable, while also offering a managed cloud option. That gives you the option of keeping sensitive prompts/responses inside your own infrastructure.

When I'd choose something else

Choose LangSmith if you're heavily invested in LangChain or LangGraph. Its tracing and agent visualization are particularly well integrated with that ecosystem.

Choose Arize Phoenix if your organization is strongly committed to OpenTelemetry/self-hosting or has stringent data-isolation requirements. Phoenix is designed around OpenTelemetry/OpenInference and can be run entirely within your infrastructure.

Choose Braintrust if the bigger requirement is “prove that our models/prompts are getting better” rather than simply maintaining an audit log. Its strength is connecting traces to datasets, evaluations, and regression testing.

I would not start a new project with Helicone unless its very simple proxy-based architecture is specifically what you need; recent 2026 comparisons report that its product development has slowed following its acquisition by Mintlify.

One important architectural point

For true auditing/compliance, I wouldn't make the observability SaaS your only record.

I'd use:

Application → LLM → Langfuse/OTel → durable audit storage

and retain an immutable/minimally mutable copy of the relevant request/response metadata according to your retention policy. That protects you if you later switch observability vendors and gives compliance/security teams a system of record separate from the debugging UI.

Also be careful about logging PII, secrets, credentials, authentication tokens, and regulated data in raw prompts/responses. Redaction should happen before data leaves your controlled boundary where possible.

Bottom line:
Langfuse is my default recommendation for a new, framework-agnostic LLM application that needs comprehensive request/response logging plus observability and auditing. If you're already all-in on LangChain/LangGraph, I'd lean LangSmith instead.

If you tell me your stack (OpenAI/Anthropic, Python/Node, LangChain or not) and whether prompts may contain PII, I can narrow this down to the best architecture and compare Langfuse vs. LangSmith vs. Datadog vs. Phoenix for your specific audit requirements.

Selecting the "best" LLM logging and observability platform for auditing depends heavily on your data governance requirements (self-hosted vs. SaaS) and whether you need pure request-and-response logging or multi-step agent tracing.

The leading platforms categorized by their primary strengths for auditing and logging include:

• Langfuse (Best Open-Source & Self-Hosted): An MIT-licensed platform (backed by ClickHouse) that handles end-to-end tracing, prompt management, and cost tracking. Ideal if data sovereignty and on-premise/self-hosted compliance are non-negotiable for your audit logs.
• Arize Phoenix / Arize AX (Best for OpenTelemetry & Deep Evaluation): OpenTelemetry-native and built with deep roots in ML monitoring. It provides robust span- and trace-level logging alongside semantic evaluation, making it powerful if you need to audit retrieval-augmented generation (RAG) accuracy and drift.
• Helicone (Best Proxy-Based Logging): A lightweight, Rust-based AI gateway and proxy. If your auditing goal is purely capturing every request, response, latency metric, and token cost with a simple base-URL change (zero code refactoring), this is the fastest implementation.
• LangSmith (Best for LangChain/LangGraph Ecosystems): Built by the LangChain team, it offers deep, granular tracing for complex multi-agent setups and prompt chains, though it is a closed-source commercial tool.
• Datadog LLM Observability (Best for Enterprise APM Integration): If your organization already routes traditional infrastructure and application logs through Datadog , turning on their LLM monitoring module allows you to centralize AI requests alongside standard microservice telemetry without onboarding a new vendor.

To help narrow down the ideal choice for your audit requirements, could you let me know:

• Do you require a self-hosted/on-premise solution, or is a SaaS cloud acceptable?
• Are you logging simple direct API calls (like standard OpenAI/Anthropic endpoints) or complex multi-step agents/RAG pipelines?

If your primary requirement is “log every LLM request and response for auditing, then make those logs useful for debugging and production monitoring,” my recommendation is LangSmith for most teams.

LangSmith Observability is particularly strong because it captures the full execution trace, not just the raw API call: prompts, responses, tool calls, retrieval steps, metadata, latency, token usage, and costs. It also supports OpenAI, Anthropic, custom applications, and OpenTelemetry rather than requiring you to use LangChain.

My shortlist

For your auditing use case, I'd choose between two

1. LangSmith — best turnkey option

It gives you a searchable record of individual traces and supports metadata/tags, monitoring dashboards, costs, latency, errors, evaluations, and alerts. You can also self-host LangSmith if keeping trace data inside your environment is a requirement.

Importantly, it can capture the exact input sent to the model and output returned, which is the core of what you're asking for.

2. Langfuse — my choice if “audit log” means maximum data ownership

Langfuse is open source and can be self-hosted, making it attractive if LLM prompts/responses contain sensitive customer or proprietary information. Independent 2026 comparisons also put Langfuse and LangSmith among the leading choices, with Langfuse particularly strong for self-hosting.

One important architectural point

I wouldn't treat the LLM observability tool as your sole compliance/audit system.

I'd architect it roughly as:

Application → OpenTelemetry/LLM instrumentation → LangSmith/Langfuse
** ↘ immutable audit storage**

That gives engineers a great interactive observability UI while maintaining a separate, controlled audit record with defined retention, access controls, encryption, and deletion policies.

Also decide explicitly whether you're allowed to store raw prompts and responses. They can contain PII, credentials, customer data, or secrets. Redaction before ingestion and strict role-based access are more important than which observability vendor you pick.

Bottom line:

• Want the best polished production experience? → LangSmith
• Want open-source/self-hosted and maximum control over audit data? → Langfuse
• Already standardized on OpenTelemetry/enterprise observability? → Arize Phoenix
• Evaluation is as important as logging? → Braintrust

If you tell me your stack (e.g. Python/Node, OpenAI/Anthropic, Kubernetes/AWS, LangChain or custom) and whether the logs contain PII/PHI/financial data, I can narrow this down to a specific architecture and vendor.

If your primary requirement is “log every LLM request and response for auditing”, my default recommendation in 2026 is Langfuse.

It is purpose-built for LLM observability rather than generic application logging: traces can capture prompts, model responses, token usage, latency, tool calls, retrieval steps, costs, and metadata. It also adds evaluations and prompt/version management, so the same data becomes useful beyond audit logs.

My shortlist

Braintrust, for example, records model calls, retrieval, tools, prompts, outputs, latency, tokens and errors as structured traces and connects those traces directly to evaluation workflows.

For an auditing requirement, I'd architect it this way

Application → OpenTelemetry/LLM instrumentation → Langfuse → durable storage

Don't make the observability SaaS your only audit record. For serious compliance/audit requirements, I'd retain an immutable or append-only copy of the relevant events in your own object storage/database, with appropriate encryption, access controls and retention policies.

Also decide upfront whether you're allowed to retain raw prompts and responses. Those can contain PII, secrets, customer data, or proprietary information. Ideally, implement redaction/tokenization before the data reaches the observability system where appropriate.

One particularly good property of Langfuse here is that it is self-hostable, so you can keep the observability data inside your infrastructure if that's important.

Bottom line:

• Need auditability + self-hosting: Langfuse
• Need best-in-class eval/release workflow: Braintrust
• Deeply invested in LangChain: LangSmith
• Already standardized on Datadog: Datadog LLM Observability
• Want maximum portability: instrument with OpenTelemetry and keep the backend replaceable.

If you tell me your stack (e.g. OpenAI/Anthropic + Python/Node + AWS/Azure/GCP) and whether self-hosting is required, I can narrow this to the best 1–2 choices and show the recommended architecture.

Choosing the "best" LLM logging and observability platform for auditing depends heavily on your requirements around data privacy, infrastructure layout , and compliance (e.g., SOC 2, GDPR, HIPAA). Because an audit trail requires capturing exact prompt inputs, completions, token counts, latency, and tool/function calls, the market has split into a few distinct leaders based on your specific needs:

• Langfuse : Best overall open-source and self-hostable platform. It is MIT-licensed, features granular production-grade tracing, tracks costs per user/model natively, and allows you to keep all audit logs completely within your own infrastructure (via Docker) so sensitive data never leaves your network.
• LangSmith : Best if your stack is built on LangChain or LangGraph. It offers deep, step-by-step visibility into complex multi-agent execution paths, metadata tagging, and evaluation loops. However, full self-hosting options are restricted to upper-tier enterprise plans.
• Helicone : Best for lightweight, proxy-based logging. If you want an audit trail instantly without rewriting application code or installing heavy SDKs, you simply route your LLM requests through their proxy (or self-host their proxy) to automatically capture usage, costs, and raw payloads.
• Arize Phoenix / Arize AX : Best for evaluation-heavy and RAG (Retrieval-Augmented Generation) pipelines. Phoenix is open-source and OpenTelemetry-native, making it exceptionally strong if you need to audit not just the prompt/response pair, but also what context chunks were retrieved by your vector database.
• Datadog LLM Observability : Best if your engineering organization already standardizes on Datadog for APM and infrastructure logging. It correlates your LLM traces, token metrics, and safety evaluations directly alongside your standard application logs.

To help narrow down the best fit for your audit requirements, let me know:

• Do you require a self-hosted/on-premise solution for data privacy, or is a SaaS cloud acceptable?
• Are you using any specific orchestration frameworks like LangChain, LlamaIndex, or raw API calls?