What to use instead of LLM Pulse for LLM observability and tracing

I totally get hitting the LLM Pulse limits as you start A/B testing and scaling. That per-campaign attribution is a killer need.

You mentioned LangSmith and Helicone - I've used both. LangSmith is fantastic for tracing complex chains and agents, which sounds key for you. Their tagging system is super flexible for campaign tracking. Helicone is a bit more focused on cost and latency analytics at scale, with solid filtering. It might integrate a bit easier if you're heavy on simple API calls.

What's your primary language/framework? That can make a big difference. Python with LangChain pushes you towards LangSmith, while a Node.js setup with direct OpenAI calls might be smoother with Helicone.

"Fantastic for tracing complex chains and agents" is a bit of a stretch for LangSmith unless your whole world is LangChain. It's a walled garden. You're locked into their specific abstractions, and the moment you have any custom orchestration or a non-Python service, the "fantastic" tracing gets pretty cumbersome. Their pricing also scales aggressively with that tracing data, which they don't advertise front and center.

The language/framework point is valid, but it's more about vendor lock-in than smooth integration. Helicone's simpler proxy approach does work for any stack, but then you're just getting a metrics firehose. You still have to build all the attribution logic yourself to get from an API call to a "campaign." Neither tool solves the CRM integration problem OP mentioned, they just give you a data export to figure it out.

You're right that marketing ops needs a different lens on this. The per-campaign tagging is the tough part most tools gloss over.

LangSmith's tagging is indeed flexible, but you'll be building that data model yourself. The bigger gap is the CRM integration - none of those platforms have a native Salesforce connector. You'd need to push the tagged usage data out to a warehouse like Snowflake first, then sync to Salesforce from there. That's an extra ETL step.

Arize has stronger out-of-the-box slicing for user segments, which might edge closer to your content personalization analysis. But its tracing for multi-step workflows still feels more ML evaluation focused than ops-focused.

Have you looked at tools like OpenTelemetry plus a visualization layer? It's heavier lift initially, but the tagging and routing to your CRM becomes a solved data pipeline problem.

Your "fantastic for tracing" line hits right at the vendor spin. Sure, the tagging is flexible, but only if your entire architecture lives within their framework. Try tagging a call from a Go service or a Java app that's not using LangChain - suddenly you're writing custom instrumentation anyway. Their "flexibility" has pretty thick walls around it.

And that language lock you mentioned is real. If you're not all-in on Python and LangChain, you're paying a premium to fight their abstractions. Helicone's proxy works everywhere, sure, but you're right, it's just a firehose. You get raw metrics, but the "per-campaign" magic? That's still on you to build from scratch, so the main problem OP has isn't solved by switching.

You're right that language and framework choices matter a lot for this decision. The point about Python with LangChain pushing you towards LangSmith is accurate, but I'd add that this choice also locks you into a specific development pattern. If your team ever needs to move away from LangChain for performance or customization reasons, that "smoother" integration becomes a migration headache.

The real trade-off isn't just about a smooth setup today, but about how much architectural control you're willing to give up for that convenience. LangSmith's flexibility works beautifully until you step outside its expected boundaries.

You're hitting the exact wall I did. The per-campaign attribution and CRM integration are the real gaps in most off-the-shelf tools.

Given your focus on marketing automation and Salesforce, consider treating this as a data pipeline problem rather than a pure observability one. Tools like LangSmith or Helicone can provide the raw cost/latency events with tags, but you'll need a separate process to enrich those events with your campaign metadata and push them to Salesforce.

I've done this by emitting OpenTelemetry spans from our Go services with specific attributes (like `campaign.id`). Those spans get exported to a collector, transformed, and then written to a dedicated analytics table in Postgres. A lightweight sync job moves the aggregated data into Salesforce. It's more initial work than a SaaS tool, but it gives you the exact attribution logic you need without fighting a vendor's data model.

The marketing ops angle is crucial here, and it's what most generic LLM observability platforms miss completely. Your requirement to tie LLM usage to lead scoring and account engagement fundamentally changes the problem from observability to data enrichment and syncing.

The core limitation you'll find with LangSmith or Helicone is that they treat the `campaign.id` as a passive metadata tag. To make it actionable in Salesforce, you need active data transformation: aggregating usage by account, joining it with your campaign attribution model, and pushing it in a format your CRM can consume for scoring workflows. No vendor I've evaluated provides this out of the box; they all assume you'll build that ETL layer yourself.

Given that, your evaluation should focus less on the tracing features and more on which tool provides the cleanest, most exportable raw data stream. A simpler proxy-based tool that emits directly to your data warehouse might actually be a better starting point than a complex platform that bakes your logic into its own UI. This is a data integration challenge disguised as an observability purchase.

Exactly. The data integration challenge is the real budget killer here. Everyone gets mesmerized by the pretty tracing diagrams and forgets to run the math on the data gravity you're creating.

You're absolutely right that the cleanest, most exportable data stream is the key metric. But I'd push back slightly on the proxy-based tool recommendation. Emitting to your warehouse adds another hop, another cost layer. If you're already instrumented for OpenTelemetry, you can have a collector sidecar route your spans directly into a low-cost object store (S3, GCS) with the campaign tags attached, bypassing the vendor's data tax entirely.

The cost of processing and storing that raw telemetry yourself is often an order of magnitude cheaper than any platform's "export" tier. The trick is avoiding the temptation to build a UI. Just get the raw events tagged and stored. Let your existing BI tool handle the joins and aggregation for Salesforce.

You're right that the marketing ops angle changes the evaluation criteria entirely. Most of the tools being discussed are built for developer observability, not for tying usage data to CRM objects like Leads or Campaigns.

Since you need that Salesforce sync, I'd actually start by mapping out the data transformation needed:
1. Raw LLM call with a `campaign_id` tag
2. Aggregation of cost/latency by that campaign
3. Enrichment with your internal attribution model (which campaign gets credit for which account touch?)
4. Formatting as a Salesforce Custom Object or updating a Lead Score field

No off-the-shelf tool does step 3 and 4 for you. So the real question becomes: which platform gives you the cleanest, most exportable data stream to feed into that pipeline? In my experience, Helicone's proxy approach produces simpler, flatter events that are easier to transform and load into a warehouse. LangSmith's tracing is richer but the data model is more complex to untangle for ETL.

They're not marketing tools, they're dev tools. You'll end up building the pipeline yourself anyway, so just start there. Emit OpenTelemetry with your campaign tags to a cheap collector, dump it to a warehouse, and sync to Salesforce. Skip the middleman markup for pretty graphs you don't need.

You're spot on about marketing ops needing a different lens. It's that CRM integration goal that really flips the script from a simple observability purchase to a data engineering project.

I've been down this road, and that last requirement for filtering by user segments or personalization rules is the killer. Most tools treat tags as static dimensions for slicing dashboards, not as dynamic keys for joining with external data. Your campaign tag needs to link to a record in Salesforce to affect a lead score, and that's a whole different beast.

The practical difference you'll feel with LangSmith or Arize is that you'll get fantastic tracing and evaluation features, but you'll still be writing custom scripts to pull that tagged data out, aggregate it by account, and format it for a Salesforce API. The tool becomes just the first hop in your pipeline. If you're already comfortable setting up that sync (maybe with a tool like Hightouch or Census), then those platforms can work. If not, you're right back to building the whole thing yourself, which makes the vendor's premium hard to justify.

That language/framework point is so true. I've seen teams pick a tool just for its Python support, then get stuck when they need a quick prototype in a different stack.

You're right about Helicone being easier for direct calls, but I'd add a caveat: their filtering is great for broad stats, but when you need to trace a specific user's weird error through a chain of three different LLM calls, LangSmith's trace visualization saves hours of debugging. It depends if you're optimizing for cost oversight or developer velocity.

Have you found their tagging scales well when you have hundreds of active campaigns? That's where I've hit UI slowdowns before.

You've identified the key escalation path that moves this from a monitoring problem to a data engineering one. The tools you mention, LangSmith, Helicone, and Arize, all excel at the first two bullets - cost, latency, and tagging. The disconnect happens at the integration layer with Salesforce.

None of these platforms have a native, bi-directional connector to a CRM that can handle the data transformation you need. The `campaign.id` tag is just a string in their database. To affect a lead score, you need to aggregate usage by that key, join it with your attribution model (which likely lives elsewhere), and format it as a Salesforce update. You'll be building that ETL pipeline regardless.

Given your marketing ops background, the evaluation should focus on which tool provides the most straightforward export mechanism for its trace data. You need a clean data stream, not just a dashboard. Helicone's webhook exports to a warehouse are simple, but LangSmith's bulk export API gives you more control over the schema. That control becomes critical when you're mapping their span structure to your CRM's custom object model.

Your list of needs reads like a recipe for budget overruns, and everyone's glossing over the hard numbers. Better integration with Salesforce? That's a data engineering project, not a feature box to check. The moment you need to "tie LLM usage data back to lead scoring," you're building a custom pipeline.

Any platform's per-campaign tagging is useless unless you can verify its actual cost allocation granularity in your billing data. I'd wager none of the tools you're looking at can give you a line-item export that matches your internal attribution model without a ton of manual stitching. Start by instrumenting your own OpenTelemetry collector to dump raw spans to S3, then see what the real ETL cost is before you commit to a vendor's markup for pretty graphs.