📤 CI/CD Violation Alerts

🚨 ATTENTION DEVS. Your LLM spend is NOT sustainable. Your CFO is watching. Traditional observability is a post-mortem. CrashLens is your pre-mortem. Act now or pay later.

You need real-time, in-pipeline enforcement. Not a historical report. Your CI/CD is the new frontline for FinOps.

👮‍♂️ CRASHLENS: Your CI/CD FINOPS COP ON DUTY

Forget the bloated dashboards and retrospective reviews. We deliver direct, actionable alerts right where your team works: Slack or Markdown. This isn't about knowing you overspent last month; it's about preventing the burn before it ever hits production.

The timing is critical: LLM API costs are a top variable expense, with Gartner estimating $644Bn will be spent on Generative AI in 2025. Meanwhile, the EU AI Act's phased enforcement starts in 2025, demanding real-time policy enforcement on API usage. With IDC predicting that by 2027, 75% of organizations will combine GenAI with FinOps processes, you must shift left. Unchecked code vulnerabilities could waste billions.

DETECTION LOGIC: NO MORE HIDDEN COSTS

CrashLens is engineered to sniff out the most egregious LLM cost violations, directly from your logs. We target the insidious patterns that traditional monitoring misses:

RETRY LOOPS & FALLBACK OVERKILL

Misconfigured agentic workflows love to spin up endless, expensive API calls, burning thousands overnight, completely undetected by passive monitoring. CrashLens catches these runaway retries and cascading fallbacks. We alert you when your agent gets stuck in an expensive, performance-degrading loop. You're burning $1.2K/month on GPT-4 retries? Fix it: downgrade your model after 2 failures.

TOKEN WASTE & MODEL OVERKILL

Using GPT-4 for a task GPT-4o-mini could easily handle is financial negligence. Verbose prompts, excessive context, unmanaged output lengths – all inflate your token count. We identify when an expensive model is deployed for a simpler task or when prompts exceed defined token limits.

RATE LIMIT EXCEEDANCE

We flag when you hit API rate limits or usage quotas, which often results in inefficient retries and wasted spend.

POLICY-AS-CODE: YOUR RULES, YOUR SAVINGS

Define your cost guardrails directly in your repository using crashlens.yml. This isn't some obscure cloud UI; it's version-controlled, declarative policy that lives with your code. This file provides declarative, version-controlled governance over your AI assets.

Example crashlens.yml for Slack alerts and PR blocking:

#.github/crashlens.yml
version: 1
updates:
  - package-ecosystem: "llm-prompts"
    directory: "/prompts" # Scan this directory for prompt definitions
    schedule:
      interval: "daily"   # Run policy checks daily
    policies:
      - enforce: "prevent-model-overkill" # Stop using expensive models unnecessarily
        description: "Disallow GPT-4 for simple summarization tasks. Use cheaper alternatives."
        rules:
          - task_type: "summarization" # CrashLens infers task type
            input_tokens_max: 500       # If input is small
            disallowed_models: ["gpt-4", "claude-3-opus"] # Too pricey
            suggest_fallback: "gpt-4o-mini" # The efficient alternative
            alert_channel: "#finops-llm-alerts" # Slack channel for alerts
            actions: ["block_pr", "slack_notify"] # Hard block the PR, notify team

      - enforce: "cap-llm-retries" # Prevent runaway retries
        description: "Cap LLM retries to prevent cost spikes from runaway agentic loops."
        rules:
          - max_retries: 3 # Max allowed retries for any LLM call
            model_scope: ["all"] # Applies to all models
            alert_channel: "#eng-sre-alerts"
            actions: ["block_pr", "slack_notify"] # Fail PR, alert SRE

      - enforce: "limit-output-tokens" # Control verbosity and output costs
        description: "Enforce max output tokens to control cost and verbosity."
        rules:
          - task_type: "content_generation"
            max_output_tokens: 750 # Set a reasonable limit
            alert_channel: "#content-eng-alerts"
            actions: ["slack_notify"] # Notify if too verbose, don't block

This YAML is your active, programmable firewall. It means "zero-trust prompt usage" at merge time, preventing $1K+ leaks.

CLI IN ACTION: SIMULATE SAVINGS, ENFORCE COMPLIANCE

CrashLens integrates directly into your CI/CD pipeline (e.g., GitHub Actions).

crashlens validate --config .github/crashlens.yml: Ensure your policies are correct before deployment. Prevents misconfigurations from ever entering the pipeline.

crashlens simulate --policy prevent-model-overkill --task summarization --input-tokens 400 --model gpt-4: Dry-run scenarios to see policy violations and quantify estimated savings before you merge. This delivers concrete "this PR would have wasted $X" stories directly in your CI/CD. It's about knowing the cost impact before the code is even merged.

When a policy violation is detected, CrashLens automatically opens a pull request, providing a clear title, description, and a code diff with a suggested fix. This could be a "debloated" prompt or refactored agent logic. Crucially, it includes contextual metadata, like "Estimated 25% token reduction" or "Reduces risk of prompt leaking by 90%".

IMPACT: ROI IN YOUR FACE

When a policy is violated, CrashLens sends a real-time alert to your designated Slack channel. No more waiting for monthly bills or drilling into complex dashboards.

Slack Alert Example:

🚨 LLM Policy Violation: Cost Overkill Detected 🚨
**Policy:** `prevent-model-overkill`
**Description:** Disallowed `gpt-4` for simple summarization.
**Detected In:** `feature/new-chatbot-summary-pr` (Commit: `a1b2c3d4e5`)
**Location:** `prompts/summary_agent.py`
**Estimated Waste:** ~$12.50 per 1000 requests.
**Suggested Fix:** Downgrade model to `gpt-4o-mini`.
**Action:** PR Blocked. Contact #finops-llm-alerts for override or fix.

This alert provides immediate, transparent ROI: clear signals to adjust behavior, optimize usage, or even block the PR entirely. It reduces waste by 50-78% through automated checks.

THE CRASHLENS EDGE: BEYOND OBSERVABILITY

Competitors like Langfuse, Helicone, and Datadog AI offer "observability" – usage logging, traces, and dashboards. They tell you what happened after the fact. You might see metrics, but none enforce policies or alert proactively in CI/CD like CrashLens. They are akin to receiving a damage report after a car crash.

Langfuse/Helicone: Often require heavy infrastructure (Docker, ClickHouse), gate core features behind expensive tiers, and users complain about complexity and misleading pricing. They provide traces and dashboards but lack robust, active enforcement and the granular, CLI-driven, YAML-based control that CrashLens offers.

Datadog AI: An incumbent observability giant, but its policy engine is generic and infrastructure-centric, not purpose-built for AI governance or specific LLM failure patterns like prompt injection or token inefficiencies. It provides deep visibility but lacks the direct, proactive enforcement at the dev-code level.

API Gateways (Portkey, Kong, Solo.io Gloo AI Gateway): Their primary focus is proxying requests, unified API access, and some monitoring/failover. While some like Portkey offer budget limits and alerts, they often act as a centralized infrastructure component, not a lightweight, local-first tool embedded directly in the developer's workflow. CrashLens doesn't operate as an inline proxy by default, avoiding potential latency and single points of failure.

CrashLens is different:

CLI-first: Lightweight, no Docker, no heavy databases. Works by reading your logs directly.

Prevention, not post-mortem: Active blocking and routing based on policies before code merges, not just showing the damage after it's done.

Zero infra, zero lock-in: Fully open-source, self-hostable, no cloud-only features, no artificial limits on prompts or usage. Your data stays local.

FinOps cop in Git: Embeds financial accountability directly into your CI/CD pipelines. It's the "Dependabot for AI".

Focus on real pain: Addresses the specific frustrations of developers being "burned by fake OSS" and overpriced tools.

The market for LLM cost control is surging. Don't get caught with a surprise bill. Control your LLM costs at the commit level. Your FinOps team and your budget will thank you.