OpenLIT vs Spanlens: Which is Better in 2026?
A comprehensive comparison of OpenLIT and Spanlens covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose OpenLIT if:
- →You need opentelemetry-native tracing — spans go to your existing collector and backend or instruments gpus, llms, mcp servers, vector dbs and coding agents
Choose Spanlens if:
- →You want more affordable paid plans (from $29/mo)
- →You need a broader feature set (6 features vs 5)
- →You need one-line instrumentation via a cli that rewrites your call sites or request logging with cost, latency and token counts for openai, anthropic and gemini
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OpenLIT vs Spanlens: At a Glance
Pricing Comparison: OpenLIT vs Spanlens
Understanding the pricing differences between OpenLIT and Spanlens is crucial for making the right choice. Here's how their plans compare side by side.
OpenLIT Pricing
Spanlens Pricing
💡 Pricing takeaway: Both OpenLIT and Spanlens offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from OpenLIT and Spanlens stacks up.
What Makes Each Tool Unique
🔵 Unique to OpenLIT
Features available in OpenLIT but not in Spanlens:
- ✓OpenTelemetry-native tracing — spans go to your existing collector and backend
- ✓Instruments GPUs, LLMs, MCP servers, vector DBs and coding agents
- ✓Built-in LLM evaluations and model comparison
- ✓Prompt management and an API key vault
- ✓Apache 2.0, self-hosted via Helm chart or Docker with OAuth sign-in
🟣 Unique to Spanlens
Features available in Spanlens but not in OpenLIT:
- ✓One-line instrumentation via a CLI that rewrites your call sites
- ✓Request logging with cost, latency and token counts for OpenAI, Anthropic and Gemini
- ✓Agent workflow tracing in a Gantt view
- ✓PII and prompt-injection detection on request content
- ✓Three-sigma anomaly detection and cheaper-model recommendations with dollar savings
- ✓Fully MIT self-host with no feature gating, plus Ollama and LangGraph tracing
Use Case Recommendations
Best for: OpenLIT
OpenLIT is an Apache 2.0 open-source observability and engineering platform for LLM and agent workloads, built on OpenTelemetry rather than on a proprietary tracing format. That choice is the substance of the product: because instrumentation emits standard OTel spans, traces can go to your existing collector and backend instead of being locked into a vendor's storage, and the same pipeline that carries your service traces carries your agent traces. Coverage runs wider than the usual LLM-call span — it instruments GPUs, LLMs, MCP servers, vector databases and coding agents, which means an agent's slow step can be attributed to the retrieval layer or the GPU rather than assumed to be the model. Around tracing it adds the adjacent pieces teams otherwise assemble separately: running LLM evaluations, managing prompts, comparing models against one another, and storing API keys in a built-in vault rather than in environment variables scattered across services. The lifecycle framing on the site runs instrument, develop, manage, observe, improve, covering both development and production stages. Deployment is self-hosted via Helm chart or Docker with OAuth sign-in, documented for both. A fully hosted OpenLIT Cloud is announced for teams that would rather not operate it, but is not yet available.
Ideal use cases:
- •Teams or individuals who need opentelemetry-native tracing — spans go to your existing collector and backend
- •Teams or individuals who need instruments gpus, llms, mcp servers, vector dbs and coding agents
- •Teams or individuals who need built-in llm evaluations and model comparison
- •Teams or individuals who need prompt management and an api key vault
- •Anyone focused on opentelemetry workflows
- •Anyone focused on observability workflows
Best for: Spanlens
Spanlens is an MIT-licensed LLM observability platform whose main design goal is that instrumenting an application should cost one line rather than an afternoon. A CLI initialiser rewrites the call sites for you, after which every OpenAI, Anthropic and Gemini request is logged with its cost, latency and token counts, and multi-step agent workflows are traced into a Gantt view that shows where the time and money actually went. Beyond straight logging it runs three checks that most cost dashboards do not: anomaly detection on a three-sigma basis, PII and prompt-injection detection on request content, and a model recommendation pass that proposes cheaper models with a dollar-figure saving attached rather than a vague suggestion. Recent SDK releases added Ollama support for local models and LangGraph tracing, so self-hosted and graph-structured agent stacks are covered alongside the hosted APIs. The licensing position is the sharpest part of the pitch: the vendor states the project is fully MIT with no `ee/` folder, meaning there is no enterprise-only directory carved out of the open-source repository — self-host with Docker Compose and every feature listed on the paid tiers is available at zero cost. The hosted tiers exist to sell managed retention, seats and alerting rather than to gate functionality, and the site publishes direct comparisons against Langfuse and Helicone.
Ideal use cases:
- •Teams or individuals who need one-line instrumentation via a cli that rewrites your call sites
- •Teams or individuals who need request logging with cost, latency and token counts for openai, anthropic and gemini
- •Teams or individuals who need agent workflow tracing in a gantt view
- •Teams or individuals who need pii and prompt-injection detection on request content
- •Anyone focused on llm-observability workflows
- •Anyone focused on tracing workflows
🤖 Other AI Agent Infrastructure Tools to Consider
OpenLIT and Spanlens aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is OpenLIT better than Spanlens?
It depends on your needs. OpenLIT offers 5 key features including OpenTelemetry-native tracing — spans go to your existing collector and backend and Instruments GPUs, LLMs, MCP servers, vector DBs and coding agents, while Spanlens provides 6 features including One-line instrumentation via a CLI that rewrites your call sites and Request logging with cost, latency and token counts for OpenAI, Anthropic and Gemini. OpenLIT uses a free model with a free tier, while Spanlens is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is OpenLIT cheaper than Spanlens?
OpenLIT doesn't have standard paid plans, while Spanlens starts at $29/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use OpenLIT and Spanlens together?
Yes, many users combine OpenLIT and Spanlens in their workflow. OpenLIT excels at opentelemetry-native tracing — spans go to your existing collector and backend, while Spanlens shines with one-line instrumentation via a cli that rewrites your call sites. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between OpenLIT and Spanlens?
While both are ai agent infrastructure tools, OpenLIT emphasizes opentelemetry-native tracing — spans go to your existing collector and backend, whereas Spanlens is known for one-line instrumentation via a cli that rewrites your call sites. The best choice depends on your specific workflow and feature priorities.
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