Guides → Playground & Guide → LLM Observability / Tracing Cost - Per-Span, Per-Trace, or Self-Host

LLM Observability / Tracing Cost - Per-Span, Per-Trace, or Self-Host

🚀 Open the calculator

Meet Liam Foster. Platform engineer instrumenting agent traces. "Agents generate a lot of spans. Priced per span, tracing can rival the model bill. Per-trace or self-host - what is cheapest at our volume?"

🔥 Our tracing vendor bills per span and agents are span-heavy. The observability invoice surprised everyone.

The story

Agents are span-heavy. A single agent task can emit dozens of spans, and per-span tracing pricing can quietly rival the model bill itself.

This calculator compares the three common models - per-span, per-trace, and self-host - at your span and trace volume, so you can see where each wins.

At low volume a managed per-trace plan is fine; past a threshold self-host pays off. The crossover is exactly what this surfaces.

🎮 Playground

LLM Observability / Tracing Cost Playground

Here are the inputs that move the result the most. Play with the sliders and check it out. The number updates live.

What will tracing cost?

Requests x spans per request x per-span rate. Per-span model shown; compare per-trace in the full calculator.

Estimated monthly cost
—

💡Per-span monthly = requests x spans x sampling x rate x retention. Agentic requests = 20-50 spans each.

Ready to run the numbers?

Open the full calculator. Pick a model, enter your tokens, see per-call, daily, monthly, and annual cost.

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Top 3 right now

Verified 13 hours ago

About this calculator: LLM Observability / Tracing Cost - Per-Span, Per-Trace, or Self-Host

Compare the tracing bill for agentic workloads across per-span, per-trace, and self-host pricing - so observability does not quietly become a top line item.

🎛 Inputs you control

Each input shapes the cost. Click an input on the calculator to set it. The explanations below match the live calculator field by field.

▸ Billing model: Which basis to headline: per-span, per-trace, or self-host. All three are computed.
How to choose: Per-span for agents, per-trace for simple chains, self-host past the managed break-even.
▸ Agent requests / month: User actions or agent runs per month; one trace each.
How to choose: Use your real monthly request volume.
▸ Spans / request: Spans generated per request across LLM, tool, retrieval, and rerank steps.
How to choose: Simple calls are 3-5; complex agents are 20-50.
▸ Requests traced pct: Share of requests you actually trace.
How to choose: 100 pct for full visibility; sample down to cut cost on high volume.
▸ Retention multiplier: Cost multiplier for longer retention.
How to choose: 1 for short (~14-day); ~2 for long (~400-day) retention.
▸ USD per million spans: Per-span rate. Inferred default.
How to choose: Logfire-class is ~$2/M; Datadog and Langfuse-class run tens of dollars per million.
▸ USD per 1k traces: Per-trace rate. Inferred default.
How to choose: LangSmith-class is ~$2.50/1k at 14-day retention.
▸ Self-host USD / month: Flat infra plus partial DevOps for a self-hosted stack. Inferred default.
How to choose: Low at small scale; 500+ vCPUs at high ingestion push it to five figures.
▸ Agent token bill USD / month: Your monthly token spend, for percent context.
How to choose: Pull from the cost or agent-loop calculator, or leave 0 to skip.
📋 Typical values & starting points Don’t know a value yet? Start with these broad, sourced ballparks — the calculator’s ▾ Typical menus pre-load the same options.

Context: Vendor prices verified Apr–Jun 2026 — re-check vendor pages before contracts. High-volume teams commonly sample tracing down to ~0.1–1%.

Input Default Typical ballparks
requestsPerMonth moves the needle 500,000 Pilot · 50K req/mo = 50,000 · Mid production · 500K/mo = 500,000 · High volume · 5M/mo = 5,000,000
spansPerRequest moves the needle 20 Simple chain · ~5 spans = 5 · Moderate agent · ~20 spans = 20 · Agent benchmark figure · ~25 spans = 25 · Complex multi-agent · ~50 spans = 50
samplingPct 100 Trace everything · 100% = 100 · Cost-managed · 10% = 10 · High-volume practice · 1% = 1
usdPerMillionSpans moves the needle 2 Arize Phoenix · $0.01/1k spans (≈$10/M) = 10 · Langfuse units · $8/100K (≈$80/M) = 80 · Self-host marginal · ~$2/M * = 2
usdPer1kTraces 2.5 LangSmith base · 14-day retention = 2.5 · LangSmith extended · 400-day = 5
retentionMultiplier 1 Standard 14-day · 1× = 1 · Extended 400-day · ~2× = 2
selfHostMonthlyUsd 1,500 Light infra · ~$500/mo = 500 · Typical production · ~$1,000/mo = 1,000 · HA / compliance setup · ~$2,000/mo * = 2,000
baseMonthlyTokenUsd 0 Pilot spend · ~$500/mo = 500 · Team · ~$5K/mo = 5,000 · Department · ~$50K/mo = 50,000 · Enterprise · ~$250K/mo = 250,000
pricingModel per-span —

Ballparks are broad industry starting points (sourced ranges; * = rough estimate) — your result gets more accurate as you replace them with measured numbers. Try them live in the calculator; API & agent users get the same data from the MCP resource aicost://input-reference/observability-cost.

Most popular

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Ready to run your own numbers?

You have seen the shape of it. Open the calculator with your model, your tokens, your volume.

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Methodology

Editorial gate
8-layer defense, see aicost.ai/ai-cost-economics
Last verified
7/20/2026, 8:00:00 PM

Author: Subu Vdaygiri, Founder & CEO of CloudIntelligence.ai. 17 years Fortune 100 (Ingram Micro, Siemens). Wharton CTO program · Kellogg CPO program · 10× AWS+Azure certified.

📖 Data sources & methodology 158 text models · 9 embeddings · 37 vision · 55 audio · 8 vector DBs across 10 vendor pages · last verified 2026-07-21

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-07-21.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-07-21
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 624 days captured
Anthropic Docs
2026-07-21
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 624 days captured
OpenAI
2026-07-21
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 625 days captured
Google AI
2026-07-21
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 600 days captured
Google Vertex
2026-07-21
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 600 days captured
DeepSeek
2026-07-21
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 539 days captured
xAI
2026-07-21
https://x.ai/api
Daily snapshot since Nov 2024 · 457 days captured
Mistral
2026-07-21
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 598 days captured
Cohere
2026-07-21
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 624 days captured
Voyage AI
2026-07-21
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash cachedInput Derived at 25% of input per Google 2.0 family caching rates.
Google — Gemini 2.0 Flash batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.0 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.0 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →