Devgraph.ai
Devgraph.ai builds a live ontology of your code, infrastructure, and tools so AI and your team can finally understand what's actually running.
If your team spends its mornings chasing 'who owns this?' and 'what breaks if I ship this?' across six tabs, Devgraph is worth the $99/month entry. Its live ontology plus MCP integration means AI agents get real dependency data instead of hallucinating it — a genuine differentiator from generic search or chat-over-docs tools. The catch is the entity and seat caps: 500 entities on Liftoff is tight for any real microservice estate, so most teams should budget for the $499 Crew tier or Enterprise. Solo devs and small shops should look at lighter alternatives; teams with mature documentation may not need it at all.
Verified 23d ago · liveness 51/100 · cite: rightaichoice.com/tools/devgraph-ai
- Platform engineering teams managing complex microservice dependencies
- DevOps and SRE teams wanting pre-deploy impact analysis
- Distributed engineering teams that need unified context across many tools
- Teams onboarding engineers who need fast answers about systems
- Solo developers or small teams with only a few integrations
- Teams that can't connect third-party tools for compliance reasons
- Organizations that need a permanent free or very low-cost tier
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Skip Devgraph if you run a small stack with only a handful of integrations or already trust a well-maintained service catalog — the $99/month floor and 500-entity cap won't earn their keep.
Hitting the 500-entity ceiling on Liftoff forces an upgrade to Crew at $499/month — a 5x jump, not a gradual step.
At $99/month Liftoff suits a small platform team piloting one ontology with under 500 entities and two users. $499/month Crew is the realistic tier for a mid-size engineering org with up to 2,500 entities and 25 users. Enterprise is custom-priced for unlimited scale plus on-prem. Lighter alternatives suit solo devs and small shops far better than the $99 floor.
In short
Devgraph.ai — Devgraph.ai builds a live ontology of your code, infrastructure, and tools so AI and your team can finally understand what's actually running. Best for Platform engineering teams managing complex microservice dependencies, DevOps and SRE teams wanting pre-deploy impact analysis, Distributed engineering teams that need unified context across many tools. Plans from $99/mo.
What people actually say about Devgraph.ai — is it worth it?
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) · researched Jul 3, 2026.
Average across the 1 source that answered — each source counts once, not each post.
- +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.
- −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.
- • 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
How well maintained and how widely used is Devgraph.ai? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Real-time ontology building from connected dev tools
- Natural language query across GitHub, Jira, Slack, PagerDuty and more
- Impact analysis that maps what breaks before you deploy
- Model Context Protocol (MCP) integration for grounding AI agents
- Bring your own LLM: OpenAI, Anthropic, xKF, Ollama
- Self-hosted and air-gapped deployment options
- Slack thread summarization and tribal-knowledge surfacing
- New-hire onboarding assistant with instant ownership and deploy answers
- Living documentation auto-updated from connected systems
- Ownership lookups across code, infrastructure, and teams
- Unified search across multiple tools in a single query
- Flexible API for custom integrations
- Discovery providers that scan and map your stack
- 14-day free trial on Liftoff and Crew, 30-day on Enterprise
About Devgraph.ai
Devgraph.ai maps relationships between your code, systems, and teams into a live ontology — a connected graph of who owns what, what depends on what, and how changes ripple through your stack. Instead of maintaining manual wikis or relying on tribal knowledge, you connect your existing tools (GitHub, GitLab, Jira, Vercel, Kubernetes, Argo, FOSSA, Grafana, Slack, PagerDuty, Linear, Confluence and others), and Devgraph automatically builds the graph from those sources. You can then query it in natural language: ask who owns a service, what breaks if you change a microservice, or summarize what happened overnight across deployments, incidents, and Slack threads. It also generates living documentation that updates as your systems change, and grounds AI agents through a Model Context Protocol (MCP) integration so they act on real, current dependency data rather than stale assumptions. It's built for platform engineering, DevOps/SRE, and distributed teams that already run a dozen tools and need them to talk to each other. Devgraph is model-agnostic: bring your own LLM (OpenAI, Anthropic, xKF, or self-hosted Ollama), and it supports self-hosted and air-gapped deployments so your data and models stay on your infrastructure. Pricing starts at $99/month for Liftoff (500 entities, 5 MCP servers, 2 users) and scales to $499/month for Crew (2,500 entities, 10 MCP servers, 25 users), with Enterprise custom-priced for unlimited everything plus on-prem deployment and custom SLAs.
Behind the Verdict
Devgraph's core idea is sound: stop maintaining a wiki nobody updates, and instead derive an always-current graph from the tools your team already uses. The discovery engine connects to GitHub, GitLab, Jira, Vercel, Kubernetes, Argo, FOSSA, Grafana, Slack, PagerDuty, Linear, and Confluence, then infers relationships between services, repos, tickets, and people. That graph powers four practical jobs — natural-language query across the whole stack, impact analysis before you deploy, ownership lookups, and living documentation. The MCP integration is the most interesting part: it lets AI coding agents query your ontology, so an agent drafting a ticket or a PR can see real dependency data rather than inventing it. Because Devgraph is model-agnostic (OpenAI, Anthropic, xKF, or self-hosted Ollama) and supports self-hosted and air-gapped deployment, it also clears the compliance bar that kills a lot of AI dev tools for regulated teams. The honest weaknesses are cost and scale. At $99/month you get only 500 entities and 2 users — fine for a pilot, painful for a real estate of hundreds of microservices. Crew at $499/month lifts you to 2,500 entities and 25 users, and Enterprise is the only tier with unlimited everything plus automatic backups and on-prem options. There's no permanent free tier, only a 14-day trial (30 for Enterprise). If your team already has accurate docs, runbooks, and a service catalog you trust, the payoff shrinks. But if your knowledge lives in Slack threads and the heads of two senior engineers, the graph pays for itself the first time it prevents a 3am page.
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Real-world workflow fit
Concrete scenarios for the personas Devgraph.ai actually fits — and what changes day-one when you adopt it.
You connect GitHub, Kubernetes, Argo, and PagerDuty, then ask 'What depends on the billing service?' before a refactor.
Outcome: Devgraph returns the downstream services, owning teams, and recent changes, so you scope the change and alert the right people instead of discovering breakage in production.
A service pages at 3am and you need to know who owns it and what changed overnight.
Outcome: A single ownership lookup plus an overnight change summary across deployments, incidents, and Slack threads gives you the owner and the likely cause in minutes.
You need to understand how to deploy a service and who to ask about it.
Outcome: The onboarding assistant answers 'How do I deploy it?' and 'Who owns this?' directly from the ontology, cutting ramp-up from weeks of archaeology to hours.
Use Cases
- Ask 'Who owns this service?' and get the owning team, recent changes, and related docs in one answer.
- Identify which services break when you change a specific microservice before you deploy.
- Run one query across GitHub, Jira, Slack, and PagerDuty instead of switching tabs five times.
- Summarize overnight changes across deployments, incidents, and Slack threads in a morning digest.
- Ground an AI agent via MCP so it creates tickets using real-time dependency data.
- Onboard a new engineer with instant answers to 'What does it depend on?' and 'How do I deploy it?'
- Generate living documentation that reflects your current systems without manual wiki upkeep.
- Trace a decision buried in a Slack thread back to the code and ticket it affects.
Models Under the Hood
as of 2026-09-26
Limitations
- Entity limits cap how large your ontology can grow: 500 entities on Liftoff and 2,500 on Crew, with unlimited entities on Enterprise.
- MCP server limits (5 on Liftoff, 10 on Crew) and discovery provider limits (5 on Liftoff, 10 on Crew) constrain how many agent integrations and sources you can connect.
- User seats are capped at 2 on Liftoff and 25 on Crew, so larger teams must move to Enterprise.
- There is no permanent free tier — only a 14-day trial on Liftoff and Crew, and a 30-day trial on Enterprise; automatic backups and on-premises deployment are Enterprise-only.
as of 2026-09-14
Verification history
We have re-verified Devgraph.ai 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Devgraph.ai tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Liftoff
$99/mo
Ideal for
A small platform team piloting Devgraph on one environment with under 500 entities and up to 2 users.
What this tier adds
Starting tier: 1 ontology, 1 environment, 500 entities, 5 MCP servers, 5 discovery providers, 2 users, 14-day trial.
Crew
$499/mo
Ideal for
A mid-size engineering org running a real microservice estate that needs up to 25 seats and 2,500 entities.
What this tier adds
Adds scale over Liftoff: 2,500 entities, 10 MCP servers, 10 discovery providers, 25 users, and 1 business day support.
Enterprise
Custom
Ideal for
Large or regulated organizations that need unlimited scale, on-prem deployment, and custom SLAs.
What this tier adds
Removes all caps (ontologies, environments, entities, MCP servers, providers, users) and adds automatic backups, on-prem options, and a 30-day trial.
Where the pricing makes sense
The company stage and team size where Devgraph.ai's pricing actually pencils out — and where peers do it cheaper.
At $99/month Liftoff suits a small platform team piloting one ontology with under 500 entities and two users. $499/month Crew is the realistic tier for a mid-size engineering org with up to 2,500 entities and 25 users. Enterprise is custom-priced for unlimited scale plus on-prem. Lighter alternatives suit solo devs and small shops far better than the $99 floor.
Setup time & first value
How long it actually takes to get something useful out of Devgraph.ai — broken out by persona, not the marketing-page minute.
Connecting your first tools and generating an initial ontology takes a working session — expect an afternoon for a small stack. Value compounds as you add discovery providers and integrations; a full multi-source graph across GitHub, Kubernetes, Jira, and Slack realistically takes several days. Enterprise on-prem deployment adds infrastructure lead time on top.
Switching to or from Devgraph.ai
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual wikis and Confluence pages: keep the pages read-only and let Devgraph's living documentation replace them as sources get connected.
- →From a service catalog tool: connect the same source repos and infrastructure so Devgraph rebuilds ownership and dependency edges automatically.
- →From tribal Slack knowledge: connect Slack as a discovery provider so decisions surface alongside the code and tickets they touch.
- ↗To a lighter search or documentation tool: export your ontology via the flexible API before disconnecting sources.
- ↗To a general-purpose AI coding agent: point it at your Devgraph ontology over MCP during overlap, then cut over once its context is grounded elsewhere.
- ↗To an in-house graph: use the flexible API to pull entities and relationships, since Devgraph doesn't lock your source data.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 4 videos for “Devgraph.ai”, and we withheld 4: 4 could not be judged, because “Devgraph.ai” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Devgraph.ai.
Official links
Tools that pair well with Devgraph.ai
Common stack mates teams adopt alongside Devgraph.ai, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Devgraph Ai vs Spider Cloud
If you need to feed AI agents with real-time web data at low cost, Spider Cloud is the clear choice — blazing fast crawling and structured output with a free tier. For platform teams drowning in tool sprawl, Devgraph.ai’s live ontology and MCP integration turn disconnected tools into a queryable knowledge graph, though at a higher price point without a free option.
Devgraph Ai vs Presto Voice
Choose Presto Voice if you run a QSR chain needing drive-thru voice automation with proven upselling ROI. Choose Devgraph.ai if you lead platform engineering for mid-to-large orgs and need automated dependency mapping and AI context across your dev tools. They serve entirely different domains, so your decision hinges on whether your challenge is restaurant operations or engineering complexity.
Devgraph Ai vs Temporal Ai
Choose Temporal AI if you need rock-solid durable execution for AI agents that survive crashes and retries—especially if you're building multi-step workflows or human-in-the-loop systems. Choose Devgraph.ai if you need a live ontology unifying your dev tools (GitHub, Jira, Slack) to give AI agents real-time context for impact analysis and onboarding. They solve different problems: Temporal ensures reliability of the execution itself; Devgraph ensures AI understands your codebase and team. For teams doing both, they could complement each other.
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