Resolve AI vs Sherlocks AI
A fair, fact-checked comparison for engineering teams choosing an AI SRE platform to investigate production incidents.
Resolve AI vs Sherlocks AI at a Glance
| Dimension | Resolve AI | Sherlocks AI |
|---|---|---|
| Category | AI production engineer | AI SRE platform |
| Founded | 2024 | 2025 |
| Funding | $190M+ raised, $1.5B valuation | $900K pre-seed (₹7.5 Cr) |
| Best for | Fortune 500 and large enterprises | Mid-market to enterprise |
| Data architecture | Queries existing observability platforms | Direct infrastructure integration via in-VPC agent |
| Autonomy level | Investigation plus suggested fixes with human approval | Investigation plus remediation recommendations |
| Agent model | Multi-agent orchestrator with sub-agents | 16+ domain-specialized agents |
| Memory and context | Infrastructure knowledge graph | Awareness graph plus institutional memory from Slack and postmortems |
| Deployment model | Enterprise install across code, infra, telemetry | Lightweight agent, in-VPC option |
| Security posture | Enterprise security (specifics not public) | SOC 2 Type 2 (per Sherlocks AI), in-VPC deployment |
| Pricing model | Enterprise only, custom-quoted, not public | Accessible mid-market pricing, free tier |
| Setup time | Weeks to months | Days |
Funding and founding figures for Resolve AI from public reporting (TechCrunch, Forbes). Sherlocks AI security and pricing claims are the company's own stated positions.
What Is Resolve AI?
Resolve AI is an AI production engineer, an autonomous platform that investigates, diagnoses, and helps resolve production incidents. It was founded in 2024 by former Splunk executives Spiros Xanthos and Mayank Agarwal, whose previous company Omnition was acquired by Splunk, and the team has deep roots in observability and OpenTelemetry.
The company has raised more than $190 million and reached a reported $1.5 billion valuation, led by investors including Lightspeed and Greylock (TechCrunch). Resolve connects to your code, infrastructure, and telemetry, and works primarily by querying the observability platforms you already run. It is positioned for large enterprises, with pricing that is enterprise only and not published publicly.
What Is Sherlocks AI?
Sherlocks AI is an AI SRE platform that investigates production incidents using 16+ domain-specialized agents, one each for databases, Kubernetes, networking, security, CI/CD, and more. It was founded in 2025 by Gaurav Toshniwal and Akshat Sandhaliya, and raised a $900K pre-seed (₹7.5 Cr) round led by SenseAI Ventures with Upekkha (Entrackr).
Instead of relying only on what your dashboards expose, Sherlocks AI runs a Watson agent inside your VPC that reads infrastructure directly, from databases and clusters to cloud and queues, so it can investigate even when observability tools have blind spots. It carries institutional memory across past incidents, runbooks, and Slack conversations, states that it is SOC 2 Type 2 certified, and targets mid-market to enterprise teams that want value in days rather than a months-long install.
Key Differences That Actually Matter
Where the data comes from
Resolve AI
Resolve AI queries your existing observability platforms, so its investigation quality is bounded by what those tools already collect and expose.
Sherlocks AI
Sherlocks AI reads infrastructure directly through an in-VPC agent, so it can still investigate when a dashboard is incomplete or the observability layer itself is degraded.
Why it matters: If your observability coverage is deep and mature, querying it is efficient. If you have blind spots, direct infrastructure access finds causes the dashboards never captured.
Company stage and funding
Resolve AI
Resolve AI is a well-funded unicorn with $190M+ raised, deep enterprise backing, and the resources of a category leader.
Sherlocks AI
Sherlocks AI is an early-stage company on a $900K pre-seed, which means a leaner product, faster iteration, and pricing aimed at accessibility rather than large enterprise contracts.
Why it matters: Funding is a real signal of stability and roadmap resources. It is not a signal of fit. A smaller team may not need, or want to pay for, an enterprise-scale platform.
Target market and pricing
Resolve AI
Resolve AI is optimized for Fortune 500 and large enterprises, with enterprise-only, custom-quoted pricing that is not public.
Sherlocks AI
Sherlocks AI targets mid-market to enterprise, publishes accessible pricing, and offers a free tier, so smaller teams can adopt without a procurement cycle.
Why it matters: Budget and buying process decide this one. Large orgs with procurement teams fit Resolve. Growing teams that want to start quickly fit Sherlocks AI.
Deployment and time to value
Resolve AI
Resolve AI integrates across code, infrastructure, and telemetry, a deeper install that generally takes weeks to months for a large environment.
Sherlocks AI
Sherlocks AI uses a lightweight agent and reports value in days, with an in-VPC option that keeps raw telemetry inside your environment.
Why it matters: If you have a large, complex estate and time to invest, a deep install pays off. If you want results this sprint, a lighter agent gets there faster.
Agent architecture
Resolve AI
Resolve AI runs a multi-agent orchestrator with sub-agents coordinating over the data it queries.
Sherlocks AI
Sherlocks AI runs 16+ agents each specialized to a domain, so a database incident is handled by an agent tuned for database failure modes.
Why it matters: Orchestration and specialization are both valid designs. Specialization can catch domain-specific root causes; orchestration can be simpler to reason about across a broad estate.
When to Choose Resolve AI
- You are a Fortune 500 or large enterprise with a dedicated procurement process and budget for a category-leading platform.
- You have a deep, mature observability investment and want an agent that sits on top of it rather than adding a new data path.
- You value the stability and roadmap resources that come with a heavily funded, $1.5B-valuation vendor.
- You have time to invest in a deeper install across code, infrastructure, and telemetry.
- Your buying decision weights market leadership and enterprise references heavily.
When to Choose Sherlocks AI
- You are a mid-market to enterprise team that wants accessible pricing and a free tier to start.
- You want setup measured in days, not a months-long enterprise integration.
- You have observability blind spots and want an agent that reads infrastructure directly, not only your dashboards.
- You need an in-VPC deployment so raw telemetry never leaves your environment.
- You want domain-specialized agents for databases, Kubernetes, networking, and security.
- You are a growing team that does not have, or does not want, an enterprise procurement cycle.
The Short Answer
Choose Resolve AI if you are a large enterprise with an established observability investment, dedicated procurement, and budget for a category-leading, heavily funded platform.
Choose Sherlocks AI if you are a mid-market to enterprise team that wants faster deployment, accessible pricing, and an agent that reads infrastructure directly rather than only what your dashboards expose.
Neither is strictly better for every team. The honest deciding factors are team size, budget, and how much you have already invested in observability.
Frequently Asked Questions
What is the difference between Resolve AI and Sherlocks AI?
Resolve AI queries your existing observability tools and is built for large enterprises. Sherlocks AI runs an agent inside your VPC that reads infrastructure directly, and targets mid-market to enterprise teams that want faster deployment.
Which is cheaper, Resolve AI or Sherlocks AI?
Resolve AI is enterprise-only and custom-quoted, with no public pricing. Sherlocks AI publishes accessible pricing and a free tier, so it is usually the lower-cost option for most teams.
Is Resolve AI good for mid-market companies?
Resolve AI is optimized for Fortune 500 and large enterprises, which shows in its pricing and heavier integration. A mid-market team can use it, but it is often more platform than smaller teams need.
Does Sherlocks AI work if I already have Datadog?
Yes. Sherlocks AI integrates with Datadog and other tools, and its in-VPC agent also reads infrastructure directly, so you do not have to replace your existing observability stack.
What is Resolve AI pricing in 2026?
Resolve AI does not publish pricing; its page routes to a sales contact form. Expect a custom enterprise quote sized to your organization rather than a self-serve plan.
Can I use both Resolve AI and Sherlocks AI?
Technically yes, but most teams pick one investigation layer to avoid duplicated cost and conflicting recommendations. Running both is uncommon outside a short head-to-head evaluation.
Which has better security compliance?
Sherlocks AI states it is SOC 2 Type 2 certified and offers in-VPC deployment, so raw telemetry stays in your environment. Resolve AI markets enterprise security but does not publish detailed certifications, so verify specifics with each vendor.
How long does Resolve AI take to deploy?
Resolve AI integrates across code, infrastructure, and telemetry, which typically takes weeks to months for a large environment. Sherlocks AI uses a lightweight agent and reports value in days.
How is Sherlocks AI different from Resolve AI's multi-agent system?
Resolve AI runs a multi-agent orchestrator over the data your observability tools expose. Sherlocks AI runs 16+ domain-specialized agents against data it reads directly from infrastructure.
Which is better for teams under 200 engineers?
For teams under roughly 200 engineers, Sherlocks AI is usually the better fit thanks to accessible pricing and setup in days. Resolve AI can work, but its enterprise model is calibrated for larger organizations.
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About this comparison: This is a comparison written by Sherlocks AI. We are one of the vendors being compared, and this is our honest take on where each product fits. Competitor figures come from public reporting; Sherlocks AI claims are our own stated positions.
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