Solutions · Startups

Ship fast.
Don't break production.

Early-stage engineering teams have two failure modes: review every PR by hand and slow to a crawl, or skip review and watch the production incidents pile up. LGTM is the third path — six specialist AI agents review every PR automatically, plus 36 CI/CD security detectors across GitHub Actions, GitLab CI, CircleCI, and Azure Pipelines stop the bad merges before they land. One flat fee, your whole team.

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The startup engineering squeeze

Three engineers. Twenty PRs a week. One needs-must-ship deadline. Code review becomes the bottleneck — or, more commonly, the thing that gets cut.

Review is the bottleneck

Founder-engineer or senior dev becomes the review-all-PRs human. They burn out, or they rubber-stamp. Either way, quality drops while shipping slows.

Incidents start landing

The race condition that's obvious to a fresh reader hides from the dev who wrote it three hours ago. Production goes down at 7 p.m. Friday. Whole team scrambles.

Security is 'we'll get to it'

Self-hosted runner on public repo. Secrets echoed in CI. S3 buckets going public from Terraform. None of it gets caught by the rushed human review.

Tooling cost adds up

Snyk seats, CodeRabbit per-seat, Sentry, ProductHunt-recommended dev tools. Per-seat pricing kills startups doubly: as you grow the team, costs grow faster.

How LGTM changes the math

Pricing starts at ₹999/mo (Go) or ₹1,999/mo (Pro) for your whole team. No per-seat. Auto-review on every PR. Six specialists in parallel + thirty-six deterministic security checks across 4 CI systems. The engineer who would have been the review-all-PRs human gets their day back.

1

Auto-review on every PR (Pro)

opened / synchronize / reopened — the moment a PR exists, six agents start reviewing. The verdict + inline comments land in the PR thread before the human reviewer has even seen the diff. They focus on architecture and trade-offs; LGTM handles the everything-else.

2

Block bad merges automatically

LGTM Security writes a check_run with status: failure when any policy-flagged rule triggers. Branch protection refuses the merge. The supply-chain bugs that used to land in main get caught at PR time.

3

Reviewers stay on the human work

LGTM doesn't replace your reviewer — it does the boring layer. Stylistic nits, naming, hot-path queries, missing await, secrets in diffs — LGTM. Architecture, business logic, trade-offs — your human reviewer. Higher leverage on the limited senior-eng time you have.

Go ₹999/mo or Pro ₹1,999/mo. Your whole team, no per-seat.

Pro is per-account, not per-seat. One subscription covers all the engineers reviewing all the repos. Cancel anytime; no minimums.

3-person team

₹133/dev

per month

10-person team

₹40/dev

per month

25-person team

₹16/dev

per month

Why flat-rate, not per-seat? Per-seat pricing penalises you for hiring engineers — the exact thing a growing startup should be doing. We charge the same regardless of headcount, so growing the team doesn't grow our bill. On BYOK, your AI provider key (OpenAI or OpenRouter) bills you on usage — that scales with PR volume, not team size. On Managed AI, LGTM covers the LLM tokens and the flat plan fee is all you pay.

What Pro includes

Unlimited PR reviews

Every PR opened on a connected repo, automatically reviewed by six agents + synthesizer. No quota.

Auto-review fires on every event

pull_request opened / synchronize / reopened. The team never has to remember to trigger; review just appears.

Full LGTM Security

Enroll any number of repos. 36 detectors across 4 CI systems. Per-rule policy. Org policy with 4-role RBAC. Audit log. Runtime watchdog. Email alerts.

Per-repo model overrides

Pin GPT-4o on the high-stakes payments repo. Pin Claude Haiku on the marketing blog. Different repos, different tradeoffs.

Multi-repo from one account

One LGTM account covers all the repos your team reviews. Settings travel; AI keys travel; the team logs in via GitHub OAuth.

Direct email support

Go: 72h response. Pro: 24h response. Max: same-day. Enterprise: custom SLA. All from a real India-based team, not tier-one triage.

The honest cost math

LGTM is BYOK — you bring your own AI provider key. We don't mark up provider tokens. Your real monthly cost is ₹999 (Go) or ₹1,999 (Pro) plus your AI provider bill.

Typical startup monthly cost (10-person team, 80 PRs/month)

LineCost
LGTM Pro (flat, your whole team)₹999
OpenAI GPT-4o (80 PRs × ~₹8/PR)~₹640
Or Claude Haiku (cheaper)~₹160
Total range₹560 — ₹1,040 / month

Per-engineer that's ~₹50-100/month. One incident prevented per quarter pays it back many times over. The cheapest variable here is the provider — start with Claude Haiku or gpt-5.4-mini, switch up if you need deeper reasoning on hard PRs. LGTM auto-routes the docs and readability agents to the same-provider mini/flash tier so you already get ~3.3× savings on those two agents by default.

Startup-team FAQ

How do we onboard a new engineer?

They GitHub-OAuth into LGTM using their personal account. The repos your team has already connected appear in their dashboard — no admin-grants-access flow. Your team's custom guidelines, focus areas, and rule policies travel with the repo, not the user.

Do we get one shared AI provider key or per-engineer keys?

We're flexible. Most small teams set up one shared provider key on a separate API project (with usage alerts) so the team's LGTM reviews bill to one place. Larger teams sometimes prefer per-engineer keys for accounting attribution. Both work.

Can different repos run on different models?

Yes — per-repo overrides are a Pro feature. Pin Claude Opus on the high-stakes payments repo, Claude Haiku on the marketing blog. Different repos, different cost/quality tradeoffs.

How does LGTM not replace senior reviewers?

The agents focus on the deterministic-ish layer — bugs, security, performance, naming, missing docs. The work senior reviewers do best — architecture, trade-offs, business-context judgement — LGTM doesn't touch. The math: senior reviewer time is your most expensive engineering resource; LGTM stops them from spending it on the bottom 80% of review work.

What if our PRs are huge?

We chunk diffs > 500 lines per agent and have the synthesizer merge across chunks. 2000-line refactor PRs still come back, just at the 90-second end of review time. For really gnarly PRs we recommend the per-repo guidelines field to nudge agents on what to ignore vs what to flag.

Can we self-host?

Not currently. LGTM is a hosted SaaS; the worker processes that decrypt your BYOK key run on our Fly infrastructure. Self-host is on the long-term roadmap if there's demand from enterprise users. For startups, the BYOK design covers most of the "we don't want to share our code" concerns — your source isn't stored, your provider key is encrypted, your LLM calls hit your provider directly.

What's the worst-case lock-in if we leave LGTM?

Minimal. Your repos, code, and CI keep working — we don't modify your source. The only LGTM-specific artifacts are the GitHub Check Runs and PR comments, which become read-only history when you uninstall. Your AI provider bill stops (since we're not generating calls anymore). Audit log export available via API for the data you want to keep.

Ship fast. Stay safe. Pay ₹999.

Free 10 reviews/month to evaluate. Upgrade to Pro when auto-review on every PR matters. One subscription, whole team.

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