Hey everyone! 👋 I've been deep-diving into LLM observability tools lately, and I keep circling back to a specific challenge that I think a lot of us in SaaS face. While tools like LangSmith, Helicone, and OpenTelemetry for LLMs are fantastic for tracing calls, monitoring latency, and catching anomalies, I'm hitting a wall when it comes to detailed cost attribution.
Here’s my scenario: We run a B2B platform where each of our customers (tenants) can trigger LLM calls through our features—think AI-generated email copy, support ticket summarization, or content moderation. We're using a mix of providers (OpenAI, Anthropic) and the costs are starting to add up quickly.
The native dashboards from the LLM providers show us total usage and cost, and our observability tools show us beautiful traces with token counts and latencies. But bridging these two worlds to answer the business question—"How much did Tenant A cost us last month?"—feels surprisingly manual. I want to move beyond just tracking total spend and into true per-tenant P&L for AI features.
My specific questions for the community:
* **Tagging Strategies:** Are you injecting tenant IDs into trace metadata or using custom attributes in your spans? I'm worried about bloating the trace data but see it as the most straightforward path.
* **Aggregation & Dashboards:** Once you have the tenant ID attached, what's your backend process for rolling up token usage/costs per tenant? Are you writing custom queries against your tracing data, or have you found tools that offer this as a built-in report?
* **Provider vs. Observability Data:** Are you reconciling the cost data from your LLM provider's invoice with the usage data from your traces? I'm thinking about slight discrepancies and wanting a single source of truth.
* **Shared Context Cost-Splitting:** How do you handle cases where a single LLM call (like a large batch job) serves multiple tenants? Is there a sane way to prorate costs, or do you just attribute the full call to the initiating tenant?
I've been cobbling together a script that parses spans from our tracing backend and joins it with our tenant activity log, but it feels fragile. I'd love to hear how others are architecting this. What's working? What turned out to be a rabbit hole?
Happy testing!
Happy testing!
Oh man, tagging strategies are exactly where I'd start. We had a similar scramble when our AI costs ballooned last year. For us, the key was making the tenant ID a non-negotiable part of the span context from the very first middleware layer. That way, everything downstream - LLM calls, vector DB lookups, you name it - inherits it automatically. We used a simple HTTP header like `X-Tenant-ID` that our API gateway validates and injects.
One gotcha we hit: some of the fancier LLM observability tools don't always pass your custom span attributes through to their own cost calculation exports. We ended up writing a small sidecar processor that subscribed to our OpenTelemetry collector, enriched the metrics with our internal tenant and project codes from the span, and then shoved it all into a dedicated timeseries DB for billing reports. It's a few extra hops, but now accounting loves me.
What's your pipeline for getting trace data out right now? Are you using OTLP to ship it somewhere, or more of a vendor-specific agent?
it worked on my machine
You're hitting on the core challenge of moving from engineering metrics to business accountability. Tagging tenant IDs at the span level is necessary but not sufficient. The critical step you're missing is a deterministic cost attribution model that maps raw LLM usage metrics to actual invoices.
For example, OpenAI's pricing per 1K tokens varies by model (GPT-4 Turbo vs. 3.5 Turbo) and sometimes by region. A trace might log token counts, but your post-processing must apply the correct, time-bound rate card. You need a separate service that ingests enriched traces, applies the per-model cost schedule from your procurement agreement, and allocates the calculated cost to the tenant dimension. This logic rarely exists within general-purpose observability tools.
Without that model layer, you're just counting tokens, not dollars. How are you currently mapping the `prompt_tokens` metric from a trace to the actual cents-per-thousand charge from your latest Azure OpenAI contract amendment?
show me the SLA
Spot on about the tagging being non-negotiable from the first middleware. We tried to retrofit it later and it was a mess of missing context in nested async calls.
The sidecar processor is the way to go. We hit that same vendor-specific export black box, especially with some managed services that promise cost breakdowns but don't expose the raw tagged metrics. Our processor sits in front of the billing timeseries DB too, but we also use it to spit out CSV files for our finance team's archaic systems.
Are you enriching with just the tenant ID, or are you also tagging things like feature flags or model tier? We found that adding a `plan_tier` attribute helped explain why some tenants' costs were suddenly 10x last month.
YMMV