Your SaaS Has AI. Now Who Watches the AI? The Solo Builder's LLM Ops Guide

Cost tracking, prompt management, fallback routing, caching. The 6 tools that keep your AI costs from eating your MRR, compared for solo builders.

Month one: "AI features are working great." Month two: "$340 OpenAI bill. What happened?" Month three: "OpenAI went down for two hours. My users saw errors." You need a layer between your app and the model APIs. Not a team of ML engineers. Just one tool that logs, tracks, and fails over.

What AI ops actually means for a solo builder

"MLOps" is a word invented by people with ten engineers. You have one engineer: you. What you actually need is four things, in order of when they hurt you.

1. Cost tracking. How much am I spending per feature, per user, per day? Without this, surprises show up on your card statement.
2. Logging and observability. What prompts went out, what came back, how long did it take? You cannot debug what you cannot see.
3. Caching. Same input means same output means you should not pay twice. System prompts, few-shot examples, and repeated queries are all cacheable.
4. Fallback routing. If Claude is down, route to GPT. If OpenAI is slow, route to Anthropic. Single provider equals single point of failure.

Two more things are useful but optional until you feel the need: prompt management (version prompts, change them without redeploying) and evals (automated quality checks on model output).

Tool 1

Helicone: fastest path to cost visibility

What it is: LLM observability and cost tracking. You add one header to your API call and Helicone logs every request, cost, latency, and error.

Free tier: 100K requests per month. Generous enough that most solo SaaS apps never leave it.

Setup: Add a base URL override and one auth header to your existing Anthropic or OpenAI client. Done. Your code calls Helicone, Helicone calls the model.

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://anthropic.helicone.ai",
  defaultHeaders: {
    "Helicone-Auth": `Bearer ${process.env.HELICONE_API_KEY}`,
  },
});

// Use client exactly as before.
// All calls are now logged and cost-tracked.

Best for: Every solo builder at MVP stage. Add it on day one.

Limitation: Observability only, not a proxy. It does not do fallback routing or caching. You need Portkey or LiteLLM for those.

Tool 2

Portkey: AI gateway with fallback and caching

What it is: An AI gateway that proxies your requests. It adds fallback routing, load balancing, semantic caching, and request logging. You replace your API base URL with Portkey's and configure routing rules in a dashboard.

Free tier: 10K requests per month. Lower than Helicone, but you get more functionality.

Best for: Builders using two or more models who need reliability. If your SaaS is calling Claude for quality tasks and a cheaper model for fast tasks, Portkey handles the routing and gives you one dashboard for all of it.

Semantic cache: Portkey can cache responses to semantically similar prompts, not just identical ones. At scale this cuts costs meaningfully.

Limitation: Adds a small amount of latency (single-digit milliseconds in practice). More configuration than Helicone.

Tool 3

OpenRouter: one API key for 100+ models

What it is: A single OpenAI-compatible API endpoint that routes to over 100 models from every major provider. You use one API key and switch models by changing one parameter.

Pricing: Pay-per-use, no subscription. You pay model pricing with a small markup. No free requests, but no monthly fee.

Best for: Builders who want the simplest way to run multiple models in production without managing separate API keys and SDK versions. Good for experimentation and cost comparison across providers.

Limitation: No logging dashboard, no prompt management, no fallback routing logic beyond provider availability. It is a model router, not a full ops platform. Also adds a dependency on a third-party aggregator.

Tool 4

LiteLLM: open-source proxy for full control

What it is: An open-source proxy server that gives you a unified API for 100+ LLM providers. You deploy it yourself, configure routing, and your app talks to your proxy instead of the model APIs directly.

Pricing: Free. Open source. You pay for hosting (a small VM, roughly $5 to $10 per month).

Best for: Builders who want full control, no vendor lock-in, and are comfortable running one more server. Pairs well with Langfuse for observability.

Limitation: You maintain it. There is no hosted dashboard out of the box. Adds operational complexity that most solo builders do not need at early stages.

Tool 5

Langfuse: open-source observability and prompt management

What it is: Open-source LLM observability with prompt versioning, tracing, and an eval framework. You can manage prompts in a UI and pull them in your app without redeploying to change a prompt.

Free tier: 50K observations per month on the hosted version, or unlimited if you self-host.

Best for: Builders who iterate on prompts heavily and want to change prompt copy without touching code. Good complement to LiteLLM for a full self-hosted stack.

Limitation: Not a proxy, so it does not give you routing or caching on its own. Pair with LiteLLM or Portkey for those.

Tool 6

Braintrust: prompt playground and evals

What it is: A developer-focused prompt playground, eval framework, and logging tool. Best-in-class side-by-side model comparison and an eval framework that lets you catch regressions when you change a prompt.

Free tier: Generous free tier for early-stage use.

Best for: Builders who care about output quality and need to run structured tests when changing prompts. If a prompt change in your SaaS could break user experience, Braintrust gives you a safety net.

Limitation: Newer and smaller community than Langfuse. Not a gateway or proxy.

Side-by-side comparison

The staged recommendation

Match the tool to the stage. Adding everything at once is over-engineering.

Stage 1: MVP (0 to 1K users)

Use the Anthropic or OpenAI SDK directly. Add Helicone for cost tracking. One header, no architecture change, and you gain a full dashboard showing cost per feature, per day, per model. Total added cost: $0.

Stage 2: Growing (1K to 10K users, multiple models)

Add Portkey as your gateway or switch routing to OpenRouter. Either gives you fallback routing when a provider goes down and caching for repeated prompts. If you add Portkey, you can drop Helicone (Portkey has built-in logging). Total added cost: $0 to $20 per month.

Stage 3: Scaling (10K+ users)

Self-hosted LiteLLM proxy with Langfuse for observability. Full control, no per-request fees on the ops layer, and you own all your data. Add Braintrust if you are running structured prompt evals. Total added cost: hosting only, roughly $10 to $30 per month.

What I use

For jynlab at its current stage: Helicone on every Anthropic call. It took three minutes to add and immediately showed which features were driving the most cost. That was useful. I have not needed anything else yet. When I add a second model provider for fallback, I will add Portkey. That is the plan.

FAQ