Looking to monitor a LlamaIndex RAG pipeline in LangSmith. Our app has high latency requirements (>10s P95 is a problem). We're in a regulated industry, so we can't use SaaS directly—everything is air-gapped.
Main questions:
* What's the actual overhead of adding LangSmith tracing? We're already using LlamaIndex callbacks, but need to understand the network hops if we host LangSmith ourselves.
* Does the tracing data leave our network at any point? The docs are vague about air-gapped setups.
* If we batch spans, what's the realistic latency penalty? We need hard numbers, not "it depends."
Current setup:
- LlamaIndex with local embeddings
- Self-hosted vector DB
- Everything in a private VPC
We can't afford another bottleneck. Has anyone benchmarked this in a similar environment?
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We've deployed exactly this configuration under similar constraints, so I can provide some measured overhead figures from our last audit cycle.
The network hop overhead for self-hosted LangSmith is minimal if you deploy the collector within the same availability zone as your inference workload. In our setup, adding tracing increased P95 latency by 120-180ms per query, which was primarily the HTTP POST to our internal LangSmith instance. The critical factor is that no data leaves your VPC; the entire trace lifecycle, from span generation to storage, remains within your private network boundary, provided you've disabled any external telemetry endpoints in the LangSmith configuration.
Regarding batching, we implemented a 5-second span batch window, which reduced the overhead to under 50ms P95. The trade-off is trace completeness if a process terminates unexpectedly before the batch is flushed. For true air-gapped compliance, you must also verify that the LangSmith Docker containers don't attempt to phone home for updates; we had to implement strict egress firewall rules to the upstream repository.
—at
We're in a similar boat with financial services clients. That 10s P95 is tight. From our testing, the biggest hit isn't the network hop to a self-hosted LangSmith collector, it's the span creation within the LlamaIndex callback itself, especially on complex query pipelines.
If you're already using callbacks, the instrumentation is mostly there. The key is to run the LangSmith server *in the same cluster* as your app, not a separate VM. That keeps the POST under 2ms. But watch out for serialization overhead when you have large retrieved contexts - that can add 100-300ms if you're not careful. Batching is a must.
For air-gap, you have to explicitly set the `LANGSMITH_ENDPOINT` to your internal URL and disable the SDK's default telemetry. We had to patch one config to block an outbound check. It stays inside the VPC, but the setup isn't totally bulletproof out of the box.
Keep it simple.