Sherlock Calls vs Datadog LLM Observability
Datadog is the observability platform trusted by 27,000+ organizations — its LLM Observability module extends that visibility to AI applications with tracing, evals, and cost tracking. Sherlock Calls is built for a fundamentally different problem: investigating voice calls from your existing providers in plain English, from Slack.
TL;DR — The short answer
- 1
Datadog LLM Observability is a powerful extension of the Datadog platform — ideal for engineering teams who already use Datadog and need to trace LLM applications, track token costs, and detect quality issues.
- 2
Sherlock Calls is built for a different layer: voice call operations. It investigates failures, pulls transcripts, and correlates costs across 15+ voice providers from Slack — no engineering work required.
- 3
If your team runs voice AI on Twilio, ElevenLabs, or Genesys, Sherlock fills the gap Datadog LLM Observability is not designed to cover.
Understanding both tools
Sherlock Calls
AI-powered voice call investigation
Sherlock Calls is a Slack-native AI investigator purpose-built for voice operations teams. Connect your existing providers — Twilio, ElevenLabs, Vapi, Genesys, and 12 more — and ask questions about your calls in plain English. Sherlock autonomously gathers data across all connected services, correlates events, and delivers a sourced answer in under 5 seconds. No new dashboards. No SDK. No code changes.
- Works inside Slack — no new UI to learn
- Connects to 15+ voice providers in minutes
- Investigates calls autonomously with AI
- Free tier — 100 credits per workspace
Datadog LLM Observability
Develop, evaluate, and monitor LLM applications with confidence
Datadog LLM Observability is a module within the Datadog platform that traces LLM application requests end-to-end, tracks token usage and cost, detects hallucinations, and correlates AI performance with full-stack application metrics via APM and RUM.
- End-to-end tracing of LLM agent calls, prompt inputs, outputs, and tool invocations — correlated with APM and RUM for full-stack context within the Datadog platform
- Structured experiments: generate datasets from production traces, validate prompt and model changes in Playground before releasing to production
- Out-of-the-box hallucination detection, prompt injection scanning, and quality drift identification via cluster visualization
- Auto-calculated LLM cost tracking per request using provider pricing models — with integrations for OpenAI, Anthropic, AWS Bedrock, LangChain, LiteLLM, and Hugging Face
Feature comparison — General APM & DevOps
Sherlock Calls vs Datadog LLM Observability & peers
All tools in the General APM & DevOps category — so you can compare both head-to-head and within the landscape.
| Feature | SherlockCalls | Datadog LLM Observabilitythis page | Grafana | New Relic | Sentry |
|---|---|---|---|---|---|
| AI call investigation | |||||
| AI agent & LLM tracing | |||||
| AI governance & compliance | |||||
| Offline LLM evaluation | |||||
| Provider integrations | 15+ (all voice) | 600+ (~5 voice) | 300+ (~2 voice) | 700+ (~4 voice) | ~100 (~3 voice) |
| Cross-provider correlation | |||||
| Natural language queries | |||||
| Zero-code setup | |||||
| Per-call cost tracking | |||||
| Free tier available |
Scroll horizontally to compare all tools →
Key differences
Why teams switch from Datadog LLM Observability to Sherlock
Voice Call Investigation vs LLM Trace Monitoring
Sherlock Calls
Sherlock investigates specific voice call events — dropped calls, transcript anomalies, ElevenLabs latency spikes, Twilio billing discrepancies — in plain English from Slack, with no additional code or engineering work.
Datadog LLM Observability
Datadog LLM Observability monitors LLM application traces: prompts, completions, token usage, and agent tool calls. It is not designed for voice call investigation — Twilio call events, voice transcripts, and cross-provider voice correlation are outside its scope.
Operational Q&A vs Dashboard Analysis
Sherlock Calls
Ask Sherlock 'Why did calls from this number keep failing last Tuesday?' in Slack and get a sourced, multi-provider answer in under 5 seconds — no dashboard, no filters, no SQL.
Datadog LLM Observability
Datadog is a powerful dashboard-driven platform. Investigating a specific voice call — finding its transcript, correlating cost, identifying the provider failure — requires navigating the Datadog UI, which is built for engineers, not operations managers.
Native Voice Integrations vs SDK Instrumentation
Sherlock Calls
Sherlock connects to your Twilio, ElevenLabs, Vapi, Retell, Genesys, Amazon Connect, HubSpot, and Datadog accounts via API key — no SDK, no schema changes, no deployment. Operational in under 2 minutes.
Datadog LLM Observability
Datadog LLM Observability requires instrumenting your application with the Datadog SDK and configuring spans to capture LLM calls. While Datadog has a Twilio error monitoring integration, it does not natively correlate voice transcripts, per-call billing, or multi-provider voice data.
Which tool is right for you?
When to choose Sherlock vs Datadog LLM Observability
Choose Sherlock Calls if…
- Your team operates voice AI in production and needs to investigate specific call failures, not debug LLM application code
- You want cross-provider correlation across Twilio, ElevenLabs, HubSpot, and Datadog without writing new instrumentation
- Your operations or support team needs call intelligence in Slack without engineering involvement
- You need per-call cost breakdowns and transcript analysis on demand across your voice provider stack
Consider Datadog LLM Observability if…
- Your engineering team already runs Datadog and needs LLM application tracing, hallucination detection, and prompt experimentation within the same platform
- You need full-stack correlation between LLM agent behavior and application performance metrics (APM, RUM, logs) in one unified observability platform
Pricing
Cost comparison
Sherlock Calls
Free to start
100 credits per Slack workspace. Team plans from $50/month. No credit card required to start.
- Free tier — 100 credits/workspace
- Team: $50–$5,000/month (usage-based)
- Enterprise: custom pricing
- No sales call required to start
- Cancel anytime
Datadog LLM Observability
Paid — per LLM span
Datadog LLM Observability is billed per LLM span (each individual LLM provider call). A single user request may trigger multiple LLM spans. Specific per-span pricing requires a Datadog account and sales engagement.
* Pricing sourced from public information. Contact Datadog LLM Observability for current rates.
FAQ
Frequently asked questions
What is Datadog LLM Observability?
Datadog LLM Observability is a module within the Datadog platform that traces LLM application requests end-to-end, monitoring token usage, latency, cost, and quality — including hallucination detection and prompt injection scanning. It is designed for engineering teams building and running LLM applications, not for voice call investigation.
Can Datadog LLM Observability investigate voice calls from Twilio or ElevenLabs?
Datadog LLM Observability is designed for tracing LLM application calls — not voice telephony events. While Datadog has a separate Twilio error monitoring integration, it does not natively correlate voice transcripts, per-call costs, or multi-provider voice data. Sherlock Calls provides native integrations with Twilio, ElevenLabs, Vapi, and 12+ other voice providers.
Is Sherlock Calls a Datadog LLM Observability alternative?
They solve fundamentally different problems at different layers. Datadog LLM Observability is right for engineering teams who need to monitor LLM application performance within their existing Datadog stack. Sherlock Calls is right for voice operations teams who need to investigate production voice calls in plain English from Slack.
How do I migrate from Datadog LLM Observability to Sherlock Calls?
No migration needed — Datadog and Sherlock serve different teams with different workflows. Sherlock actually integrates with Datadog: if you use Datadog to monitor your voice infrastructure, Sherlock can query that data alongside Twilio, ElevenLabs, and your CRM in a single investigation.
Does Sherlock Calls replace Datadog LLM Observability?
No. Datadog LLM Observability is the right choice for engineering teams who need LLM application tracing and full-stack correlation within the Datadog ecosystem. Sherlock Calls is the right choice for voice operations teams who need to investigate voice calls and get instant answers from their telephony stack.
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