Google Gemini API Review

A board does not lose sleep over Google as a vendor. The Gemini API runs on the same infrastructure as Search and Cloud, and Google has shipped AI products for over a decade. Viability is the one question you can skip here.

The real call is whether this advances us or just swaps one model provider for another. Gemini's native multimodal range is wider than most rivals: the Live API handles real-time voice agents, and Nano Banana does conversational image editing inside the same API. Document Understanding parses 1,000-page PDFs without a separate pipeline. OpenAI matches the text quality, but Google bundles more input types under one key.

Pricing is usage-based and aggressive: Gemini 3.5 Flash-Lite runs $0.30 per million input tokens and $2.50 output. The catch is platform pull. Once prompts, caching, and grounding sit inside Google Cloud, switching costs climb quietly. Pilot two workloads for 60 days, confirm the per-call math, then commit.

A platform CTO scoping an LLM provider through 2029 should read the model ladder first. Gemini exposes one REST surface spanning Flash-Lite, Flash, and Pro tiers, so you tune cost against capability per route without rewriting the integration. Gemini 3.5 Flash-Lite sits at $0.30 input per 1M tokens, and Long Context Processing scales past a million tokens on the same call.

The craft ceiling is real. Function Calling and Structured Outputs make agentic routing a primitive rather than a glued-on parser, and Gemini Enterprise Agent Platform (formerly Vertex AI) is a genuine graduation path into Google Cloud IAM and compliance SLAs. Against OpenAI, the edge is multimodal breadth, with Veo for video and the Nano Banana image model under one key.

But the catch is roster churn. Gemini 3.1 Pro still ships as a Preview model, and Google shut down Gemini 3 Pro Preview less than four months after launch, so production systems track deprecations Google sets, not you — a standing migration tax on the dependency.

No seats and no sales call for the developer tier; the paid tier opens with a $5 minimum prepay. The docs indicate Gemini 3.5 Flash-Lite runs $0.30 per 1M input tokens and $2.50 per 1M output. Free Tier needs no credit card. Procurement won't fight this one.

The budget risk is the asymmetry. Output costs roughly 8x input, so a chatty agent reprices fast. The catch is the two billing paths: the Developer API and Gemini Enterprise Agent Platform (formerly Vertex AI) quote separately, and the docs say enterprise rates differ. Batch Processing API cuts both rates 50% on a 24-hour window, and Context Caching saves up to 90% on repeated input. Model those before forecasting.

ROI is legible. Token counts are metered per call, so cost-per-feature is measurable, not hand-wavy. Compare OpenAI's GPT-6 Luna, priced lower on output at $0.50 per 1M tokens. Gemini's long-context window is the offsetting bet.

An engineer judges an AI API by the 2 a.m. incident where a malformed prompt returns garbage, not the demo. The Gemini API surfaces that case decently. Structured Outputs constrains responses to a JSON schema, so parsing logic stops guessing, and the docs ship runnable curl plus SDK snippets that match the actual response shape.

Workflow fit is real for multimodal work. Long Context Processing accepts millions of tokens, and Document Understanding chews through 1,000-page PDFs without a chunking pipeline you would otherwise hand-roll. Context caching cuts repeated input tokens by up to 90%, which matters once a RAG prompt stops being a toy.

The catch is the model churn. Gemini 3.1 Pro is paid-only with no free tier, and preview models like Gemini 3 Flash Preview shift under you. OpenAI's API versioning feels steadier for production pinning.

Most AI APIs hand you a text box and a key and call it a day. Gemini gives you the whole pantry. One key reaches text, the Nano Banana image model, Veo video, and a Live API for real-time voice, plus document parsing that swallows a 1,000-page PDF. For a developer shipping more than a chatbot, that breadth saves real glue code.

The daily feel leans generous. Google AI Studio lets you experiment free, no credit card, and Gemini 3.1 Flash-Lite starts at $0.25 per million input tokens. OpenAI's GPT-6 Luna lists $0.10, so Gemini no longer wins the low end on price alone. Context caching quietly trims up to 90% off repeated input.

The catch is Gemini 3.1 Pro: it has no free tier, so anything past Flash means billing setup and a $5 minimum prepay. And the lineup changes fast enough that pinning a version is its own small chore.

No funding question here. Google funds this. The graveyard worry isn't the company — it's the model lineup.

Gemini 1.0 and 1.5 are already gone, returning 404s. Gemini 2.0 Flash shut down in June 2026, and the 2.5 models are now closed to new projects. That's the pattern to watch: pin a model in production and you're re-testing prompts every cycle. The product itself is strong. Long Context Processing handles millions of tokens, Function Calling covers agentic work, and Gemini 3.1 Pro Preview runs $2.00 input per 1M tokens — competitive against OpenAI's GPT line. But "Preview" labels on the newest models mean today's pricing isn't a promise.

Exit is reasonable. REST calls and Structured Outputs port to other providers with prompt rework, not a rewrite. The yellow flag is lock-in through Gemini Enterprise Agent Platform (formerly Vertex AI) if you lean on built-in Google Search grounding.