How much does Devgraph.ai cost?

Devgraph.ai is a paid tool with no free tier. Pricing is not publicly listed; you must contact their sales team for a quote. Enterprise plans typically scale based on team size and number of integrations.

Does Devgraph.ai offer a free trial?

Devgraph.ai does not appear to offer a free trial or free plan. The product is paid only, and demos are likely available upon request. Teams should evaluate its cost against their budget before committing.

What are the best alternatives to Devgraph.ai?

Alternatives include Atlassian Compass (for microservice cataloging), Cortex (for service maturity and scorecards), and Backstage (Spotify's open platform). Devgraph differs by unifying real-time ontology across tools like GitHub, Jira, and Slack.

Who should avoid Devgraph.ai?

Solo developers or small teams with few integrations, organizations unwilling to connect third-party tools due to compliance, teams needing a free or very low-cost solution, and those with mature manual documentation should avoid it.

What are the key limitations of Devgraph.ai?

Limitations include paid-only pricing (no free tier), reliance on connecting multiple tools for full value, a potentially steep learning curve for teams not used to ontology-based tools, and possible overhead for small teams.

What integrations does Devgraph.ai support?

Devgraph.ai integrates directly with GitHub, Jira, Slack, PagerDuty, and other development tools. It also supports LLMs via Model Context Protocol (MCP) and works with OpenAI, Anthropic, xAI, and self-hosted models like Ollama.

Is Devgraph.ai easy to set up?

Setup requires connecting your existing tools (GitHub, Jira, Slack, etc.) and choosing an LLM backend. For teams with many integrations, initial configuration can be complex, but once running, the ontology auto-updates with minimal maintenance.

Is Devgraph.ai good for AI-assisted development?

Yes, Devgraph.ai is designed for AI-assisted development. It grounds AI agents with real-time context from your stack, enabling natural language queries, impact analysis, and living documentation. It supports bring-your-own LLM for privacy.

Can Devgraph.ai be self-hosted?

Yes, Devgraph.ai supports self-hosted and air-gapped deployment options, giving teams control over data privacy and compliance. This is ideal for organizations with strict security requirements.

Does Devgraph.ai help with onboarding new engineers?

Yes, Devgraph.ai includes a new hire onboarding assistant that instantly surfaces system knowledge, ownership lookups, and dependency maps, reducing the time for new engineers to become productive.

Does Devgraph.ai work with Slack?

Yes, Devgraph.ai integrates deeply with Slack, offering thread summarization, knowledge surfacing, and natural-language querying directly within Slack, so teams can get answers without leaving chat.

Devgraph.ai

Q: Who is Devgraph.ai best for?

Devgraph.ai is best for platform engineering teams managing complex microservice dependencies, distributed engineering teams needing unified context, DevOps/SRE teams wanting pre-deploy impact analysis, and organizations onboarding new engineers faster.

Paid

Live ontology engine for developer tools and AI context.

By Tanmay Verma, Founder · Last verified 03 Jul 2026

0 views

Added 6d ago

77/100Safe Bet

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In short

Devgraph.ai — Live ontology engine for developer tools and AI context. Best for Platform engineering teams managing complex microservice dependencies, Distributed engineering teams needing unified context across tools, DevOps/SRE teams wanting pre-deploy impact analysis. Plans from $99/mo.

Compared withvs Presto Voice vs Spider Cloud vs Temporal Ai

Is Devgraph.ai actually worth it?

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See what real users actually say. We scan live discussions, reviews and complaints across the web and hand you an honest verdict — in under a minute.

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Editorial Verdict

Best for

Platform engineering teams managing complex microservice dependenciesDistributed engineering teams needing unified context across toolsDevOps/SRE teams wanting pre-deploy impact analysisOnboarding new engineers faster with instant system knowledgeAI-assisted development initiatives requiring grounded, real-time context

Not ideal for

Solo developers or small teams with few integrationsTeams unwilling to connect third-party tools due to complianceOrganizations needing a free or very low-cost tierTeams that already have mature documentation and dependency mappingShop that prefer manual documentation over automated ontology

Devgraph solves a real pain—unifying scattered dev tools into a queryable graph for AI. But the $99/mo entry point and entity limits make it a serious investment best for mid-to-large engineering orgs already adopting AI-assisted development.

Compare with: Devgraph.ai vs Persana AI, Devgraph.ai vs Resolve AI, Devgraph.ai vs Galileo AI Evals

Last verified: July 2026

What's new in Devgraph.ai

Checked 6 days ago

Across the latest 3 updates: 3 feature updates.

FeatureBlog·Sep 28Newest

What is Model Context Protocol? The Bridge Between AI and Your Tools

Explains Model Context Protocol (MCP) for bridging AI assistants with tools, enabling data pull, automation, and secure workflows.

FeatureBlog·Sep 25

The AI Paradox: Why Contrasting Reports Validate What We've Been Saying All Along

Describes how contradictory AI reports highlight the enterprise implementation gap Devgraph solves.

FeatureBlog·Sep 23

AI Can’t Replace You Yet: Enterprise Decision Gaps

Discusses why AI can't replace jobs yet and how Devgraph's ontology engine with MCP powers real-time, relationship-driven decisions.

What independent users actually report about Devgraph.ai

We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.

1 mentions across 1 source (Hacker News).

50% positive50% critical

Recurring strengths

+Live ontology eliminates manual documentation updates.
+Reduces context-switching by unifying Slack, GitHub, Jira, PagerDuty.
+Impact analysis aids safer code deployments.
+Natural language queries make system understanding accessible.
+Self-hosted and air-gapped options support privacy-conscious teams.

Recurring frustrations

−No substantial user reviews or community validation.
−Learning curve likely steep for non-ontology-savvy teams.
−Ontology accuracy across diverse tools remains unverified.
−Pricing may escalate with team size or data volume.
−Requires connecting sensitive tools, raising security concerns.

Patterns worth knowing

Lack of community feedback makes assessment impossible

Seen on Hacker News

Learning curve

intermediateProductive in ~Days of setup

Hidden costs people mention

• Overage charges for data volume beyond plan limits? Not specified.
• Additional fees for multi-user seats? Not disclosed.
• Self-hosted deployment may require additional infrastructure costs.

Viability Score

77/100

Safe Bet

How likely is Devgraph.ai to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.

momentum

funding runway

website health

wrapper dependency

100

Last calculated: July 2026

How we score →

Key Features

Real-time ontology building from connected tools
Natural language query across GitHub, Jira, Slack, etc.
Impact analysis for code changes with dependency mapping
AI agent integration via Model Context Protocol (MCP)
Bring your own LLM (OpenAI, Anthropic, xAI, Ollama)
Self-hosted and air-gapped deployment
Slack thread summarization and knowledge surfacing
New hire onboarding assistant
Living documentation auto-updated from systems
Ownership lookups across code, infra, and teams

About Devgraph.ai

PaidIntermediateAPI availableWeb · API · Plugin

Devgraph.ai builds a live ontology from your development stack—GitHub, Jira, Slack, PagerDuty, and more—so AI agents and engineers can understand code, infrastructure, and team relationships in real time. Instead of manual wikis or context-switching, the platform auto-discovers dependencies, surfaces tribal knowledge, and powers natural-language queries across all connected tools. It integrates with major LLMs (OpenAI, Anthropic, xAI) or self-hosted models via Model Context Protocol (MCP), giving teams control over privacy and cost. Key capabilities include impact analysis before deployments, instant ownership lookups, new-hire onboarding assistance, and living documentation that updates with every change. While smaller teams may find the paid plans pricey, Devgraph is a strong fit for platform engineering, DevOps, and distributed teams tired of managing context across dozens of tools.

Behind the Verdict

Devgraph's live ontology approach is a clear step up from static documentation or simple search. For platform engineers who constantly track dependencies and answer 'who owns this?', it delivers immediate value—especially when combined with AI agents that can query the graph in natural language. The MCP support and bring-your-own-model are smart moves for privacy-conscious teams. However, the pricing starts at $99/month for only 500 entities and 2 users, which locks out smaller shops. The Crew tier at $499/month scales to 2,500 entities and 25 users, but that's still a stretch for many mid-sized teams. Compared to tools like Sourcegraph or linear dependency checkers, Devgraph wins on relationship mapping across tools, but loses on accessibility—there's no free tier. In practice, you'll need to commit to connecting multiple sources and maintaining MCP servers. It's best for organizations where context-switching is a daily bottleneck and where AI initiatives already have budget. If you're a solo dev or a small team with 2-3 tools, skip it. For a 50-person engineering org drowning in Slack threads and Jira tickets, it's worth the trial.

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Use Cases

Ask 'Who owns this service?' and get instant team, changes, and docs.
Identify which services break when you change a specific microservice.
View a unified search across GitHub, Jira, Slack, and PagerDuty from one query.
Summarize overnight changes across deployments, incidents, and Slack threads.
Ground an AI agent with MCP so it creates tickets based on real-time dependency data.

Models Under the Hood

OpenAIAnthropicxAIOllama

Limitations

Free trial is 14 days for Liftoff (30 for Enterprise).
Entity limits cap ontology size per plan (500/2,500/unlimited).
MCP and discovery provider limits (5/10/unlimited) constrain agent integrations.
No free tier beyond trial.

12-month cost

Project the real annual outlay, including the implied monthly cost when only an annual tier is published.

Plan

Annual total

$1,188

Over 12 months

Effective monthly

$99

Billed monthly

Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.

Integrations

GitHubGitLabJira VercelKubernetesArgoFOSSAGrafanaSlackPagerDutyLinearConfluenceOpenAIAnthropicxAI

Resources & Guides

Frequently Asked Questions

Tools that pair well with Devgraph.ai

Common stack mates teams adopt alongside Devgraph.ai, with the specific reason each pairing earns its keep.

Persana AI

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Resolve AI

AI agents that handle on-call and production operations so engineers can build.

Galileo AI Evals

Eval engineering platform that turns evals into production guardrails at 96% lower cost.

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