LLMOps in 2024: What Was Actually Production-Ready

Observability, evaluation, and cost tracking for LLMs matured fast between 2022–24. The hard part in 2024 was not tooling - it was picking a minimal, coherent stack.

In early 2022, most teams building with GPT-3 were hand-rolling logs and Grafana dashboards. Prompts were stored in comments, model versions were tracked in someone's Notion page, and cost was discovered at the end of the month on a cloud invoice. By late 2024, a typical serious stack included a tracing tool, an evaluation platform, and a cost-tracking proxy. The question shifted from how do we even monitor this to how much monitoring is enough for this stage.

This maturation was good news, but it created a new problem: tool proliferation. By mid-2024 there were over a dozen LLMOps platforms, each with overlapping features and different strengths. Teams that tried to use all of them ended up with fragmented observability and a maintenance burden that competed with building. The teams that shipped the most reliably had made a different choice.

A Minimal, Coherent LLMOps Stack

The stack that showed up in the most successful production deployments we reviewed was deliberately minimal. Four capabilities, each handled by one well-integrated tool rather than a patchwork of scripts and dashboards.

• Tracing: one tool that captures every request, response, tool call, and retrieval step in a single structured trace
• Evaluation: a CI job that runs your labelled test set on every change to prompts, models, or retrieval logic
• Cost tracking: per-endpoint and per-tenant breakdowns refreshed at least daily for every LLM provider
• Alerting: automated thresholds on error rate, latency p95, quality drift, and unexpected cost spikes

Tracing: The Foundation Everything Else Depends On

Tracing is the foundation of LLMOps. Without structured, queryable traces, every other capability - evaluation, cost analysis, incident response - is hampered. A good trace for an LLM call captures: the prompt and its rendered version with all variable substitutions, the model and version, the full response, latency at every stage, token counts, and any tool calls or retrievals that happened during the interaction.

For multi-step chains and agent runs, the trace should be hierarchical - showing which sub-calls were part of which parent task, so you can isolate where a failure originated without reading thousands of lines of logs. LangSmith, Langfuse, and Arize Phoenix all provided this by late 2023. The key was instrumenting consistently from the start, not retrofitting tracing after problems appeared.

Evaluation Gates in CI: The Shift That Changed Everything

Before 2023, most teams ran evaluations manually - usually before a launch, usually under time pressure, usually on a sample that was too small to be meaningful. By 2024, the most mature teams had wired evaluation into CI. Every change to a prompt, model version, or retrieval configuration triggered an automated run against a curated test set, with hard gates on key metrics.

This single change - treating LLM evaluation like unit tests - produced a step change in reliability. Regressions that would have shipped to users were caught automatically. The team stopped debating whether a change was safe and started looking at objective metrics. Prompts that seemed like improvements but degraded edge-case performance were rejected before anyone saw them in production.

1. 1Keep your evaluation set in source control - it is as important as the code itself
2. 2Set metric gates that reflect real quality thresholds, not arbitrary numbers
3. 3Make evaluation fast enough to run on every PR - slow evaluations get skipped
4. 4Review the evaluation set every quarter - add cases from recent failures and remove stale ones
5. 5Track metric trends over time, not just pass/fail - gradual drift is a warning sign

Cost Tracking That Actually Changes Behaviour

LLM cost dashboards that showed only total monthly spend were nearly useless for engineering teams. The dashboards that changed behaviour were the ones that showed cost per feature, per tenant, per model, and per prompt - broken down enough that engineers could identify the specific calls driving the bill and make targeted optimisation decisions.

A common pattern in 2023: the engineering team would reduce cost by 40–60% within a week of getting a per-endpoint cost breakdown for the first time. Not because they were incompetent before, but because they had never had visibility into which calls were expensive and why. The data made optimisation conversations specific and productive rather than vague and political.

Alerting: What to Watch and When

Alerting for LLM systems is different from alerting for traditional APIs. Latency is inherently higher and more variable, so p50 latency alerts are noisy. Error rates need separate tracking for model errors, tool call failures, and infrastructure errors. And quality drift - where the system becomes gradually less accurate without throwing explicit errors - requires its own monitoring layer.

• Latency: alert on p95 crossing a threshold, not p50 - p50 is too variable for useful alerting
• Error rate: separate model-level errors from infrastructure errors; each needs a different response
• Quality drift: run a small sample of production traffic through your judge or eval set nightly and alert on significant drops
• Cost spikes: alert when a tenant or endpoint crosses a per-day cost threshold - catches runaway loops before they become expensive incidents
• Empty or truncated responses: often signals a context length or formatting issue that silently degrades quality

Avoiding Tool Sprawl

The biggest LLMOps mistake we saw in 2024 was not under-investment in tooling - it was the opposite. Teams that adopted every new platform created fragmented observability: traces in one tool, costs in another, evaluations in a spreadsheet, and alerts in a fourth system that nobody had updated in months. Correlating a production incident across four disconnected tools is miserable and slow.

Our recommendation: pick one platform that does tracing and evaluation well, add a cost proxy or proxy-level cost tracking for your LLM providers, and use your existing monitoring infrastructure (Datadog, PagerDuty, etc.) for alerting. Four tools, all integrated, all used consistently. That combination beat any number of specialist tools used inconsistently.