Running large language models and autonomous agents in production without dedicated observability is like flying blind in a storm. Traditional application performance monitoring (APM) tools like Datadog or New Relic track server CPU and HTTP status codes, but fail to capture prompt versions, token consumption, retrieval context, agent tool calls, or semantic response quality. Codersarts implements turnkey, production-grade LLM observability and distributed tracing. Whether you need an air-gapped, self-hosted Langfuse deployment on Kubernetes, a managed LangSmith setup, or an open-source Arize Phoenix stack, we instrument your entire AI lifecycle with OpenTelemetry to provide real-time cost attribution, bottleneck diagnosis, prompt versioning, and user feedback telemetry.
When an LLM application behaves unexpectedly — whether an autonomous agent enters an infinite loop, a RAG system answers with irrelevant information, or an API call stalls for seven seconds — traditional error logs only tell you that an endpoint timed out or returned a 500 status code. They cannot show you what context was retrieved, which model parameters were active, what intermediate thoughts the agent generated, or which specific tool call failed.
Codersarts gives your engineering and product teams complete visibility into the latent space of your AI systems. We instrument your backend services with asynchronous, OpenTelemetry-compliant tracing that tracks every user query from entry to completion. We deploy centralized observability platforms — specializing in self-hosted, privacy-compliant deployments — that map token spend per user, pinpoint retrieval bottlenecks, version system prompts with instant rollbacks, and capture real-time end-user feedback.
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Is This You?
Operating generative AI systems without purpose-built observability creates severe engineering, operational, and financial blind spots:
"A user reported a hallucination or an agent freeze, but we have no way to reproduce or inspect what happened inside the chain."
Standard backend logging only stores the initial HTTP request and final response. Without nested trace spans, you cannot see intermediate tool arguments, retrieved chunks, or agent reasoning steps.
"Our monthly OpenAI and Anthropic bills are surging, but we don't know which specific tenant or feature is driving the cost."
LLM API providers provide aggregate billing, but lack the granular telemetry needed to allocate costs across specific enterprise customers, departments, or experimental features.
"We cannot use cloud-hosted monitoring platforms due to strict HIPAA, GDPR, or SOC 2 data residency constraints."
Your security and compliance teams prohibit sending prompts, customer conversations, and internal document chunks to third-party SaaS observability vendors.
"Engineers are editing system prompts directly in Git, making it impossible to correlate prompt changes with production quality regressions."
Hardcoded prompts prevent rapid iteration. When a prompt update degrades performance, rolling back requires an emergency code redeployment.
"Our streaming chatbot periodically hangs for 5+ seconds, and we cannot determine if the vector database, reranker, or model provider is at fault."
Without granular distributed timing metrics — such as Time-to-First-Token (TTFT) and vector search latency — diagnosing performance bottlenecks is pure guesswork.
"Our existing Datadog or New Relic APM doesn't understand tokens, embeddings, hallucinations, or agent tool calling."
Traditional APM tools are built for microservices and database queries. They lack the semantic data models required to track generative AI metrics, prompt hierarchies, and evaluation scores.
The Cost of Inaction: Inability to debug customer issues leads to user churn, untracked token usage results in budget shocks, and lack of prompt versioning paralyzes product release velocity.
What LLM Observability & Tracing Does for Your Business
Dedicated LLM observability replaces opaque API calls with transparent, actionable intelligence across your AI stack:
Distributed Spans Across Latent Space: Inspect every step of multi-step RAG pipelines and agent loops — input embeddings, vector similarity scores, model inference tokens, and tool payloads.
Per-Tenant & Per-User Cost Attribution: Break down token consumption and dollar spend by customer organization, internal department, or specific feature flag.
Zero-Downtime Prompt Versioning: Manage prompts in a centralized registry, conduct live A/B tests, and roll back regressions instantly without redeploying application code.
Granular Latency & TTFT Profiling: Isolate streaming bottlenecks and cold-start delays across model providers, embedding models, and database queries.
100% Private Cloud Deployment: Host Langfuse or Phoenix entirely within your private VPC with PostgreSQL and ClickHouse, keeping customer data strictly within your firewall.
For your business, this means:
Customer issues and hallucinations are diagnosed and reproduced in minutes.
Finance and engineering know exact unit economics and margins per customer.
Product teams test and iterate on system prompts safely with instant rollbacks.
Data residency and privacy compliance mandates (HIPAA, GDPR, SOC 2) are fully met.
Our LLM Observability Implementation Services
Whether deploying self-hosted infrastructure or configuring managed platforms, Codersarts provides complete telemetry engineering:
Self-Hosted Observability Deployment (Langfuse & Arize Phoenix)
We deploy production-grade, self-hosted instances of Langfuse or Arize Phoenix inside your private AWS VPC, Azure VNet, Google Cloud project, or Kubernetes cluster. We configure scalable backend infrastructure utilizing PostgreSQL, ClickHouse, and Redis, ensuring high throughput, data residency, and enterprise compliance.
Full-Stack Distributed Tracing & OpenTelemetry Instrumentation
We instrument your backend services using vendor-neutral OpenTelemetry standards or native Langfuse/LangSmith SDKs. We wrap every component of your AI pipeline — from query rewriting and embedding generation to vector search, reranking, LLM calls, and agent tool execution — into structured trace spans.
Granular Cost Allocation & Multi-Tenant Metering
We configure real-time token and USD cost tracking mapped to your organizational hierarchy. You can filter and visualize spend by customer tenant ID, application feature, environment, or model family, with automated budget alerts when spend spikes unexpectedly.
Centralized Prompt Management & Zero-Downtime Versioning
We migrate hardcoded prompts from application source code into a centralized prompt registry. We wire dynamic prompt fetching into your backend services, enabling your team to edit prompt instructions, test new variants, conduct production A/B tests, and roll back regressions in seconds.
Latency & Time-to-First-Token (TTFT) Performance Profiling
We build performance monitoring dashboards tracking Time-to-First-Token (TTFT), tokens-per-second throughput, vector search latencies, and third-party API response times, allowing engineers to immediately isolate bottlenecks.
User Feedback Loops & Continuous Online Quality Scoring
We connect frontend user feedback (thumbs up/down ratings, corrections) directly to the specific trace ID and prompt version that generated the response, with automated online evaluation triggers scoring responses for hallucination, sentiment, and toxicity.
Common Use Cases
Complex Multi-Agent Systems (LangGraph, CrewAI): Trace non-linear agent execution paths, state transitions, tool invocations, and multi-turn conversational loops to quickly diagnose infinite loops.
Enterprise RAG Applications: Profile end-to-end retrieval pipelines to determine whether low-quality answers are caused by poor vector chunk retrieval, reranker failures, or model synthesis.
Multi-Tenant B2B SaaS Platforms: Track exact token consumption and inference costs for every customer organization, enabling precise usage-based billing and margin optimization.
Regulated Healthcare & Financial AI: Provide full audit trails of every model interaction while enforcing strict on-premise data residency and automated PII masking on all trace payloads.
Production Model Migrations: A/B test new frontier models (e.g., swapping GPT-4o with Claude 3.5 Sonnet or Gemini 1.5 Flash) with side-by-side comparisons of latency, cost, and user satisfaction.
What You Get
When you engage Codersarts to implement your LLM observability stack, you receive:
A production-ready observability platform (self-hosted Langfuse or Phoenix on Kubernetes/Docker, or managed LangSmith).
Complete backend tracing instrumentation across your Python, TypeScript, or Node.js codebase using asynchronous OpenTelemetry wrappers.
A centralized prompt registry with dynamic runtime fetching and instant rollback logic.
Multi-tenant cost and token analytics dashboards displaying spend per user, tenant, model, and feature.
Latency and performance profiling views tracking TTFT, tokens per second, and database latencies.
Frontend user feedback integration mapping user ratings directly to trace IDs.
Automated alerting rules for Slack, Teams, or PagerDuty on budget breaches and error surges.
Infrastructure as Code (Terraform / Helm) and operating runbooks for cluster maintenance.
A 30-minute handover call and post-delivery support for configuration tuning.
What We Need From You
To begin setting up your LLM observability and tracing system, we'll need:
Architecture overview: A description of your AI stack (frameworks used: LangChain, LlamaIndex, LiteLLM, native SDKs; backend languages).
Hosting preference: Self-hosted in your private cloud (AWS, Azure, GCP, Kubernetes) or managed SaaS platform.
Infrastructure access: Access to a staging Kubernetes cluster, Docker environment, or cloud IAM role to deploy the observability server.
Target metrics and pain points: Specific visibility goals (e.g., multi-agent tracing, per-tenant cost attribution, RAG latency).
Data privacy requirements: Any compliance mandates regarding PII retention, data residency, or encryption standards (HIPAA, GDPR, SOC 2).
How Our LLM Observability Service Works
Free scoping call (30 minutes): We review your AI application architecture, hosting preferences, tracing schema, and compliance requirements.
Observability plan and fixed quote (within 1 business day): You receive a written technical plan covering infrastructure architecture, OpenTelemetry spans, dashboards, and a fixed price.
Infrastructure deployment & tracing instrumentation: We provision the self-hosted cluster (or configure managed tenants) and instrument your backend code with non-blocking OpenTelemetry spans.
Testing against agreed checks: We run load tests to verify zero latency impact on user requests, test prompt rollbacks, and verify per-tenant cost attribution accuracy.
Deployment and handover: We deploy to production, configure automated alerting, hand over complete documentation, and train your engineering team.
Security, Privacy & Data Compliance
We build observability architectures with enterprise privacy and data isolation as the primary priority:
100% private cloud deployment: We deploy self-hosted Langfuse or Phoenix inside your private AWS VPC, Azure VNet, or Kubernetes cluster. No telemetry data leaves your network.
Pre-trace PII sanitization: Automated redaction filters scrub names, emails, credit cards, and sensitive identifiers before traces are written to the database.
Enterprise SSO & RBAC: Role-based access control and SSO (Okta, Google Workspace, Azure AD) ensure only authorized team members can inspect production traces.
Mutual Non-Disclosure Agreement (NDA): We sign strict bilateral NDAs before reviewing your application architecture, source code, or infrastructure.
How Much Does LLM Observability Setup Cost?
Pricing depends on whether you use a managed cloud platform or a high-availability self-hosted Kubernetes cluster with ClickHouse and PostgreSQL. Every project receives a fixed quote before work begins.
Starter Tracing & SDK Setup
Best for: Single LLM application or early-stage RAG app
Production Self-Hosted Observability
Best for: High-traffic applications & teams with data residency requirements
Includes: High-availability self-hosted Langfuse on Kubernetes (Postgres + ClickHouse), full distributed tracing (RAG + tools), per-tenant cost attribution, and prompt registry
Enterprise Multi-Tenant & PromptOps
Best for: Complex multi-agent architectures & multi-tenant SaaS platforms
Includes: Everything in Production, plus custom OpenTelemetry exporters, frontend user feedback integration, automated online evaluation pipelines, SSO/RBAC, and 24/7 alerting
What affects the cost: Deployment architecture (managed SaaS vs. high-availability Kubernetes with ClickHouse), codebase complexity (single call vs. multi-agent graph), and custom PII redaction requirements.
Engagement Models
Fixed-price project: Best for a defined observability deployment with clear deliverables, timeline, and guaranteed pricing.
Dedicated MLOps engineer: Best for ongoing platform work, embedding an experienced telemetry engineer into your sprint cycle. See Hire Developers.
Managed hosting & maintenance: Best after launch. We monitor, patch, and scale your self-hosted observability cluster over time. See our Cloud Support plans.
Tech Stack
Observability platforms: Langfuse (Self-Hosted & Cloud), LangSmith, Arize Phoenix, Helicone.
Standards & protocols: OpenTelemetry (OTel), OpenInference, Prometheus, Grafana.
Databases & analytical storage: ClickHouse, PostgreSQL, Redis, Amazon S3.
Frameworks instrumented: LangChain, LlamaIndex, LangGraph, CrewAI, AutoGen, LiteLLM, FastAPI.
Infrastructure: Docker, Kubernetes (EKS, GKE, AKS), Helm, Terraform, AWS, Azure, GCP.
Why Choose Codersarts for LLM Observability
Specialists in self-hosted setups: While others only plug in SaaS API keys, we deploy and scale high-throughput, self-hosted Langfuse clusters on Kubernetes.
Zero latency overhead: We configure asynchronous, non-blocking trace exporters that ensure logging never degrades user-facing response times.
AI-native engineering: We understand the unique nuances of streaming responses, token chunking, agent tool parameters, and semantic evaluations.
Works with your cloud: We deploy into your existing AWS, Azure, or GCP accounts with full Infrastructure-as-Code.
Fixed, transparent pricing: You know the full cost before work begins.
You own everything: All Kubernetes manifests, Terraform scripts, and custom instrumentation code belong 100% to you.
Where This Fits in Your AI Project
Observability is the central monitoring layer that powers every other stage of AI development. Related Codersarts services:
Make an AI Agent Production-Ready: Combine observability with latency optimization and rate limiting for complete production hardening.
Cut LLM API Costs: Use observability traces to pinpoint token waste and implement prompt caching and model routing.
Set Up an LLM Evaluation Suite: Link production trace data to automated CI/CD benchmarks and golden test sets.
Write & Optimize Production Prompts: Use real production traces to drive prompt optimization and token compression.
Deploy AI Agents on AWS, Azure or Google Cloud: Host your observability infrastructure reliably on enterprise cloud platforms.
Launching an agent soon? Pair tracing with Test & Evaluate an AI Agent Before Launch. Tracing a complex agent graph? See Build a Multi-Agent System.
Frequently Asked Questions
How much does an LLM observability setup project cost?
Cost depends on your application architecture and whether you choose managed cloud telemetry or a high-availability self-hosted Kubernetes cluster with ClickHouse. Every project receives a transparent, fixed-price quote within one business day after a free scoping call.
How long does it take to implement LLM observability?
A standard application tracing setup typically takes one to two weeks. A complete self-hosted enterprise deployment on Kubernetes with full RAG and agent tracing, custom dashboards, and team training is completed in two to three weeks.
Is our customer and prompt data safe?
Yes. With a self-hosted Langfuse or Phoenix deployment, all data remains inside your private VPC. We configure automated pre-trace PII redaction to strip sensitive personal data, and we work under a bilateral NDA.
Who owns the telemetry infrastructure and trace data?
You own 100% of all deliverables. You retain complete ownership of your self-hosted databases, Terraform scripts, Kubernetes manifests, instrumentation code, and all stored customer trace data.
What if something breaks or our traffic scales after delivery?
Every project includes a post-delivery support window for adjustments. Our Kubernetes deployments feature automated horizontal pod autoscaling (HPA) and ClickHouse partitioning to handle massive traffic surges smoothly.
How does self-hosted Langfuse compare to LangSmith and Arize Phoenix?
Langfuse is open-source, highly scalable, and ideal for self-hosting in private clouds with granular cost tracking and prompt management. LangSmith is a managed SaaS deeply integrated with LangChain and LangGraph. Arize Phoenix specializes in embedding drift visualization and local prototyping. We help you choose the best fit for your stack.
How do OpenTelemetry tracing and prompt versioning work in production?
OpenTelemetry records nested spans for every step of a query (retrieval, model inference, tool execution) asynchronously without adding latency. A prompt registry decouples prompts from application code, allowing your team to update, A/B test, and roll back prompts in seconds without deploying new code.
Want to build it yourself?
Read our community guide: How to set up self-hosted Langfuse and OpenTelemetry LLM tracing on the Codersarts Discuss forum.
Bring Complete Visibility to Your Production AI Applications
Stop operating your LLMs in the dark. Inspect every trace, track every token, version every prompt, and catch performance bottlenecks in real time. Share your architecture with Codersarts, and our observability engineers will deliver an implementation plan and fixed quote within 24 hours.
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