Sourcegraph Cody + AI SaaS

Built a ai saas with Sourcegraph Cody?
We'll make it production-ready.

AI SaaS products add a unique layer of complexity on top of standard SaaS challenges — you're building a paid product on top of third-party AI APIs that are expensive, unpredictable, and rate-limited. Your margins depend on controlling API costs, your reliability depends on handling upstream failures, and your differentiation depends on prompt engineering and workflow design that AI coding tools can't optimize for you.

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AI SaaS challenges in Sourcegraph Cody apps

Building a ai saas with Sourcegraph Cody is a great start — but these challenges need attention before launch.

Cost management and unit economics

Every user action costs you real money in API calls. If a user generates 100 requests a day and each costs $0.05, that's $5/day per user — $150/month. Without token tracking, usage tiers, and cost optimization, your AI SaaS can lose money on every customer.

Upstream API reliability

OpenAI and Anthropic APIs have outages, rate limits, and variable latency. Your SaaS needs fallback providers, retry logic with exponential backoff, request queuing, and graceful degradation. AI tools build direct API calls with no resilience — one upstream outage takes your entire product down.

Prompt management and versioning

Your prompts are your product's core IP. AI tools hardcode prompts in the source code. You need a prompt management system with versioning, A/B testing capability, and the ability to update prompts without deploying code. A bad prompt update shouldn't require a rollback of your entire application.

Output quality and consistency

AI responses vary in quality, format, and accuracy. Your paying customers expect consistent output. You need output validation, structured output parsing, retry logic for poor responses, and quality monitoring. One hallucinated response in a customer-facing context can destroy trust.

Usage-based billing

AI SaaS products typically need usage-based or credit-based pricing rather than flat monthly fees. Tracking usage accurately, enforcing limits in real-time, and integrating metered billing with Stripe requires careful implementation that AI tools don't provide.

Data privacy with AI providers

Your customers' data is being sent to third-party AI APIs. You need clear data processing agreements, the option to use providers that don't train on your data, and compliance with privacy regulations. Enterprise customers will specifically ask how their data is handled.

What we check in your Sourcegraph Cody ai saas

API cost tracking — per-user and per-feature token usage monitoring
Upstream resilience — fallback providers, retry logic, circuit breakers
Prompt management — versioned prompts, separated from application code
Output validation — structured parsing, quality checks, error handling
Usage-based billing — metered usage, credit system, Stripe integration
Rate limiting — per-user limits that prevent runaway API costs
Streaming implementation — proper SSE for long-running AI responses
Data privacy — customer data handling, provider agreements, encryption
Caching — response caching for identical requests to reduce costs
Monitoring — cost dashboards, latency tracking, quality metrics

Common Sourcegraph Cody issues we fix

Beyond ai saas-specific issues, these are Sourcegraph Cody patterns we commonly fix.

highCode Quality

Suggestions based on deprecated or low-quality code patterns found in the existing codebase

Cody's suggestions are grounded in your actual codebase, which means if the codebase contains outdated patterns, deprecated library usage, or known-bad code, Cody will suggest those same patterns in new code — amplifying technical debt.

highSecurity

Cross-repo context can leak patterns from one team's code into another team's service

In large organizations where Cody indexes multiple repositories, suggestions can carry patterns from one team's codebase into another, introducing unfamiliar dependencies, different error handling conventions, or architectural approaches that do not belong in the target service.

mediumSecurity

Security vulnerabilities in existing code recommended as reference implementations

If the indexed codebase contains known security issues that have not yet been patched — unparameterized queries, missing auth checks, insecure deserialization — Cody may suggest these patterns as examples when generating similar code.

mediumPerformance

Performance anti-patterns replicated from existing high-traffic code paths

When Cody finds existing code as a reference for a new feature, it may replicate performance issues — N+1 queries, missing indexes, blocking synchronous calls — that are accepted in the existing code but will be problematic at the scale of the new feature.

Start with a self-serve audit

Get a professional review of your Sourcegraph Cody ai saas at a fixed price.

External Security Scan

Black-box review of your public-facing app. No code access needed.

$19
  • OWASP Top 10 vulnerability check
  • SSL/TLS configuration analysis
  • Security header assessment
  • Expert review within 24h
Get Started

Code Audit

In-depth review of your source code for security, quality, and best practices.

$19
  • Security vulnerability analysis
  • Code quality review
  • Dependency audit
  • Architecture review
  • Expert + AI code analysis
Get Started
Best Value

Complete Bundle

Both scans in one package with cross-referenced findings.

$29$38
  • Everything in both products
  • Cross-referenced findings
  • Unified action plan
Get Started

100% credited toward any paid service. Start with an audit, then let us fix what we find.

Frequently asked questions

Can I build a ai saas with Sourcegraph Cody?

Sourcegraph Cody is a great starting point for a ai saas. It handles the initial scaffolding well, but ai saas apps have specific requirements — cost management and unit economics and upstream api reliability — that need professional attention before launch.

What issues does Sourcegraph Cody leave in ai saas apps?

Common issues include: suggestions based on deprecated or low-quality code patterns found in the existing codebase, cross-repo context can leak patterns from one team's code into another team's service, security vulnerabilities in existing code recommended as reference implementations. For a ai saas specifically, these issues are compounded by the need for cost management and unit economics.

How do I make my Sourcegraph Cody ai saas production-ready?

Start with our code audit ($19) to get a clear picture of what needs fixing. For most Sourcegraph Cody-built ai saas apps, the critical path is: security review, then fixing core flow reliability, then deployment. We provide a fixed quote after the audit.

How much does it cost to fix a Sourcegraph Cody-built ai saas?

Our code audit is $19 and gives you a complete report of issues. Fixes start at $199 with our Fix & Ship plan. For larger ai saas projects, we provide a custom fixed quote after the audit — no hourly billing.

Get your Sourcegraph Cody ai saas production-ready

Tell us about your project. We'll respond within 24 hours with a clear plan and fixed quote.

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