Resolve AI vs. Sherlocks AI
Both offer AI-powered SRE, but their approaches and target audiences differ significantly. Here's the breakdown.
"Resolve AI reads your dashboards. Sherlocks AI reads your infrastructure."
The Short Version
Information Transparency
While Sherlocks AI provides detailed public documentation, Resolve AI operates with more limited public information. This comparison is based on available industry reports, public customer testimonials, and reported enterprise engagements.
| Feature | Resolve AI | Sherlocks AI |
|---|---|---|
| Built for | Fortune 500 enterprises (optimized for large, complex orgs; best fit for deep existing investments in observability) | Mid-market to enterprise (flexible for growing teams; accessible for smaller orgs who want enterprise-grade SRE capabilities) |
| Data approach | Queries your existing observability tools (no need to move data; fast for orgs that already rely on established dashboards) | Watson agent in your VPC plus direct integrations (goes straight to the source for greater insights and fewer data blind spots) |
| Pricing | $500K-$1M+/year enterprise only (high investment, may be out of reach for smaller teams) | Accessible pricing for growing teams (democratizes AI SRE power, keeps costs predictable as teams scale) |
| Autonomy | Investigation + suggested fixes (decreases manual toil but may require human review, can accelerate time to resolution) | Investigation + remediation recommendations (gives concrete steps to speed up incident responses with less manual digging) |
| Go-live | Heavy integration across code, infrastructure, telemetry (integration can take months, best for orgs with established processes) | Lightweight setup, value in days (onboarding is quick so teams get results faster and see impact sooner) |
Where They Align (Similarities)
While their technical implementations differ, both platforms share a core vision for the future of SRE:
AI-First Investigation
Both move away from static dashboards towards active, AI-driven root cause analysis (RCA).
Multi-Agent Architecture
Both utilize a sophisticated orchestration of multiple AI agents to handle different stages of an incident.
Enterprise-Grade Security
Both prioritize security, offering deep integrations with enterprise identity providers and secret management.
Focus on MTTR
Both platforms are laser-focused on reducing Mean Time to Resolution by automating the most time-consuming parts of the investigation.
Autonomous Remediation
Both aim to move from mere investigation to autonomous or semi-autonomous remediation of production issues.
What Resolve AI Does
Resolve AI wants to be your "AI Production Engineer"—an autonomous platform that investigates, diagnoses, and fixes production incidents. The team comes from Splunk's observability business and helped build OpenTelemetry.
While they've raised $150M+ and are already valued at $1B, real-world proof of full autonomy at scale remains somewhat limited, so the investment and valuation reflect healthy market confidence as well as signal potential product risks.
Resolve plugs into your code, infra, and telemetry—think repos, deploys, Kubernetes, Datadog, the usual suspects. The pitch: auto-resolve 80% of alerts so humans don't have to wake up.
Their customers:
Detailed Comparison
Dashboard-led vs. Infra-led
Resolve AI:
- Queries existing observability platforms (Datadog, Splunk, etc.)
- Quality is bounded by what your tools expose
- Inherits gaps in your setup
Sherlocks AI:
- Watson agent in your VPC fetches data directly from infra
- Direct integrations with DBs, K8s, Cloud, Queues
- Not dependent on single observability vendor
- Investigates even when observability tools are failing
Sherlocks' Specialist Moat
During a database outage, Database Sherlock reduced MTTR by 18 minutes by identifying lock contention missed by generic tools. Specialized agents such as Kubernetes Sherlock and Security Sherlock consistently identify root causes that generalist AI may overlook.
Specialized vs. Generalist
- Resolve: Multi-agent system with a planner orchestrating sub-agents organized around investigation stages (triage, hypothesis).
- Sherlocks: 16+ domain-specialized agents. Each individually trained and fine-tuned for its specific domain (K8s, DB, etc.).
Graphs vs. Deep Memory
Both platforms build an understanding of your environment, but the scope of what they "learn" differs.
Knowledge Graph
Resolve maps pods, nodes, services, and dependencies to learn patterns and outcomes.
Awareness Graph + Tribal Knowledge
Sherlocks adds institutional knowledge from Slack, past postmortems, and team documentation.
"This pattern matches the Redis connection storm from Jan 15 recorded in Slack. Identified fix: Increase client-side timeout to 500ms."
Learning preserved even when team members leave.
Pricing & Accessibility
Resolve AI
Enterprise-only, 6-7 figure ACVs ($500K+). No public pricing or self-serve tier.
Sherlocks AI
Accessible mid-market entry points. Low 5-figure starting range. Designed for growing teams.
Integration & Setup
Resolve AI
Heavy project. Requires broad access across code, CI/CD, infra, and telemetry.
Sherlocks AI
Lightweight. Watson agent deploys in minutes. Value in days, not months. Works with heterogeneous stacks.
Security & Privacy
Resolve AI
Limited public documentation on data handling. Reported on-prem options for enterprise.
Sherlocks AI
Watson is deployed in your VPC—raw telemetry never leaves your network. SOC 2 Type 2 certified.
Proven Results
Resolve AI Results
- Coinbase73% faster RCA
- DoorDash87% faster investigation
- Zscaler30% fewer engineers
Sherlocks AI Results
- Downtime Reduction70% reduction
- Alert Noise90% reduction
- MTTR Improvement3.5h → 22m
What Resolve AI Lacks
When to Choose
Choose Resolve AI if...
- You are a Fortune 500 company with a $500K+ budget for AI SRE.
- You require code-level investigation closely tied to pull requests and deployment history.
- You are heavily invested in Splunk, Datadog, or Grafana and need direct integration.
- Your technology stack is homogeneous and already well-instrumented.
Choose Sherlocks AI if...
- You want to slash pager fatigue and reclaim your nights, without Fortune 500 spend.
- You require sensitive data to remain in your VPC (Watson-in-VPC).
- Your infrastructure is complex/multi-cloud with observability blind spots.
- You want to tap into institutional memory (Slack, past postmortems) for investigations.
- You need to go from signup to value in days, not months.
Summary Table
| Dimension | Resolve AI | Sherlocks AI |
|---|---|---|
| Founded | 2024 | 2025 |
| Funding | $150M+ | $0.9M (seed) |
| Pricing | $500K-$1M+/year | Accessible mid-market |
| Data source | Observability platforms | Direct infra (Watson) |
| Agents | General-purpose | 16+ domain-specialized |
| Institutional memory | Infra graph | Full: infra + Slack + Docs |
| On-prem option | Yes (Reported) | Yes (In-VPC) |
| Security cert | Not public | SOC 2 Type 2 |
| Setup time | Months | Days |
| G2 rating | N/A | 4.9/5 (28 reviews) |
| Best for | Fortune 500 enterprises | Mid-market to enterprise teams |
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