As AI applications move from prototypes to production, developers need more than basic logs. They need to understand model costs, latency, tokens, errors, prompts, responses, and multi-step agent workflows. This is where open source LLM observability becomes essential.

Spanlens is designed for teams that want an open-source, self-hostable observability platform without rebuilding their applications around a complex instrumentation system. It uses a proxy-first approach, allowing developers to change the provider base URL while keeping their existing SDK calls intact.

What Is Open Source LLM Observability?

Open source LLM observability refers to tools that help developers monitor and understand how AI applications behave while providing access to the underlying source code and, depending on the project, the ability to self-host the platform.

A production LLM application can generate thousands of requests every day. Without observability, it can be difficult to determine why costs increased, why responses became slower, or which step caused an AI agent to fail.

An effective observability platform can track:

• LLM requests and responses

• Token usage

• Model costs

• Request latency

• Errors and anomalies

• Prompt versions

• Agent traces

• Evaluation results

• Security and PII signals

Spanlens combines these capabilities in an MIT-licensed repository and can be self-hosted with Docker. Its platform supports request logging, cost tracking, agent tracing, evaluations, and anomaly detection.

Why Developers Need LLM Observability

Traditional application monitoring does not always provide enough context for AI systems. An LLM request can involve prompts, retrieved documents, tools, multiple models, and several intermediate steps.

For example, an AI customer-support agent might first classify a request, retrieve information from a database, check a policy, and then generate a response. A simple application log may show only the final response.

LLM observability provides visibility across these individual operations. Spanlens can display nested spans for tools, retrieval systems, and model calls, making it easier to identify slow or problematic parts of an agent workflow.

Spanlens as a Langfuse Alternative

If you are searching for a Langfuse alternative, Spanlens offers a different approach to instrumentation.

Langfuse is a popular open-source observability platform, but its primary approach involves SDK instrumentation or OpenTelemetry. Spanlens focuses heavily on a proxy-based workflow, allowing teams to integrate observability by changing the API base URL rather than wrapping every individual model call.

This distinction can matter for teams that want minimal application changes.

Spanlens provides:

• One-line baseURL integration

• Request and response logging

• Token and cost tracking

• Agent tracing

• Prompt version tracking

• Evals and experiments

• Anomaly detection

• Self-hosting

• MIT licensing

It also supports OpenTelemetry, so teams with an existing OTel pipeline can send LLM spans into the same observability environment.

For organizations comparing Langfuse and Spanlens, the key question is whether they prefer SDK-first instrumentation or a simpler proxy-first integration model.

Spanlens as a Helicone Alternative

For developers looking for a Helicone alternative, Spanlens is particularly relevant because both platforms use a proxy-based architecture.

With this model, developers can route model requests through an observability layer rather than extensively modifying application code. Spanlens states that its proxy supports provider-native functionality including streaming, tools, and structured output.

The main difference is the broader observability feature set Spanlens is building around its proxy. Its platform includes agent tracing, statistical prompt A/B experiments, evaluations, anomaly detection, and cost optimization features.

This makes Spanlens worth considering when a development team wants proxy-based integration but also needs deeper visibility into agent workflows and experiments.

Braintrust Alternatives for AI Teams

When researching Braintrust alternatives, it is important to understand the difference between evaluation-focused platforms and full observability systems.

Braintrust is strongly focused on evaluations, scoring, regression detection, and developer workflows around AI quality. Spanlens takes a broader approach by combining evaluations with request logging, cost tracking, tracing, anomaly detection, and model optimization.

This can be useful for teams that do not want a separate system for every part of their LLM monitoring stack.

With Spanlens, teams can use real application traffic for evaluations and experiments while also monitoring cost, latency, and model usage. Prompt versions can be compared based on quality, cost, and latency before changes are deployed.

Self-Hosted LLM Observability

Data privacy is another major reason companies investigate open-source observability solutions.

Self-hosting allows organizations to operate their observability infrastructure on their own servers or cloud environment. Spanlens provides a Docker-based self-hosting option, with the platform designed to keep prompts, completions, and provider keys within the organization's infrastructure.

This approach can be especially useful for:

• AI startups handling customer data

• Enterprises with strict data policies

• Developers working with sensitive prompts

• Agencies managing multiple AI applications

• Teams that want greater infrastructure control

Self-hosting also provides an alternative to being dependent on a hosted observability provider.

Track LLM Costs and Performance

One of the biggest advantages of LLM observability is understanding where money is being spent.

AI applications can make thousands or millions of model requests. A small increase in token usage or the use of a more expensive model can significantly affect monthly costs.

Spanlens provides model-level cost and token tracking. It also includes caching capabilities that can prevent identical requests from reaching the provider when exact-match caching is enabled.

Developers can therefore investigate questions such as:

• Which model is costing the most?

• Which application generates the most requests?

• Where are latency spikes occurring?

• Which prompts consume the most tokens?

• Can a cheaper model produce similar results?

• Did a new prompt increase costs?

These insights can help engineering teams optimize both performance and AI spending.

Monitor AI Agents With Detailed Traces

AI agents are becoming increasingly complex. A single user request may trigger multiple model calls, database queries, retrieval operations, and external tools.

Spanlens provides nested traces that show these operations as part of a single agent workflow. This makes it easier to identify which operation is responsible for latency or failures.

For example, an agent trace might show:

1. Planning

2. Tool execution

3. Vector database retrieval

4. Policy verification

5. Final model response

6. Evaluation or guardrail

Instead of looking through disconnected logs, developers can inspect the complete workflow in one place.

OpenTelemetry Support

Teams already using OpenTelemetry do not necessarily need to replace their existing tracing infrastructure.

Spanlens supports OTLP/HTTP ingestion and export, allowing existing OpenTelemetry tracing flows to connect with its LLM-specific observability features.

This gives engineering teams flexibility. They can use direct proxy integration for quick setup or connect existing OTel instrumentation when that fits their architecture better.

Choosing the Right LLM Observability Platform

There is no universal observability platform for every AI application. The right choice depends on your architecture, instrumentation preferences, data requirements, and evaluation workflow.

Consider these questions before selecting a platform:

• Is the software genuinely open source?

• Can it be self-hosted?

• Does it support your LLM providers?

• Can you integrate it without major code changes?

• Does it track cost and token usage?

• Does it support agent tracing?

• Can you run evaluations?

• Does it support OpenTelemetry?

• How does it handle sensitive information?

• Can you export your observability data?

Spanlens is positioned around a simple integration model, open-source licensing, self-hosting, and a broad observability feature set.

Final Thoughts

Modern AI applications require visibility across more than just model responses. Developers need to understand cost, latency, quality, security, prompts, and complex agent workflows.

For teams researching open source LLM observability, Spanlens provides an approach that combines proxy-based monitoring, agent tracing, evaluations, cost tracking, and self-hosting in an MIT-licensed platform.

It can also be considered when evaluating a Langfuse alternative, Helicone alternative, or Braintrust alternatives, particularly when simple integration, self-hosting, and broader observability capabilities are important.

By adding observability early, AI teams can move from simply knowing that an application works to understanding exactly how it works, what it costs, and where it can be improved.

Must visit us: https://www.spanlens.io