Google Gemini API Development Services
InterCode builds production applications with Google Gemini — Google's most capable multimodal AI family. From long-document analysis using the 1M token context window to video understanding and enterprise deployment via Vertex AI, we help you leverage Gemini's unique capabilities to solve problems that other models cannot.
Multimodal AI With the Longest Context Window Available
Google Gemini 1.5 Pro introduced capabilities that set it apart from competing models: a context window of up to one million tokens, native understanding of text, images, video, audio, and code in a single prompt, and grounding with Google Search for responses backed by real-time web knowledge. Gemini Flash offers the same multimodal capabilities at significantly lower latency and cost, making it suitable for high-throughput applications. At InterCode, we build Gemini integrations that take advantage of these distinctive features. We design document analysis pipelines that pass entire legal contracts, financial reports, or codebases to Gemini 1.5 Pro in a single context — no chunking, no retrieval step, no information loss. We build multimodal product catalogues where a single API call processes both image and text attributes. For enterprise deployments, we use Vertex AI to host Gemini with Google's enterprise SLAs, VPC Service Controls, and audit logging. We implement Gemini's function calling, Code Interpreter, and system instructions to build structured AI workflows. We configure safety settings and tune grounding thresholds for applications that need factual accuracy alongside generative fluency. Integration with Google Workspace allows Gemini to read and write Docs, Sheets, and Drive files directly.
What We Build With Google Gemini
We build long-document analysis tools that pass full legal contracts or financial reports — hundreds of pages — to Gemini 1.5 Pro in one prompt and extract structured answers without a retrieval pipeline. We create multimodal product catalogue systems where Gemini processes product images alongside textual descriptions to generate richer, more accurate metadata. We build video understanding pipelines that send recordings directly to Gemini for transcription, chapter segmentation, and summarisation. For developer teams, we integrate Gemini into CI/CD workflows for automated code review and PR summarisation. Enterprise chatbots deployed on Vertex AI use Google Search grounding to answer questions about current events and company-specific data simultaneously.
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Learn moreFrequently Asked Questions
Gemini 1.5 Pro's standout advantage is its one-million-token context window — far larger than GPT-4 Turbo (128K) or Claude 3 Opus (200K) — making it ideal for tasks that require processing entire books, codebases, or video files. Gemini is also uniquely capable with video and audio natively. For pure text reasoning and instruction following, Claude often benchmarks higher. GPT-4 remains the most widely integrated model with the broadest tool ecosystem. The right choice depends on your modalities and context requirements.
Gemini 1.5 Pro offers maximum capability — the full 1M token context, highest reasoning quality, and best performance on complex tasks — at higher cost and latency. Gemini Flash is a distilled model optimised for speed and cost, with the same multimodal input support but lower latency and significantly cheaper pricing. We recommend Pro for complex analysis tasks and Flash for high-throughput applications like real-time chat or batch classification.
Three things stand out: native video and audio understanding (you can send a video file directly and ask questions about it), the longest available context window at one million tokens (useful for processing entire documents without chunking), and grounding with Google Search (Gemini can cite live web sources in its responses, reducing hallucinations on factual queries).
Gemini is priced per million tokens of input and output. Gemini 1.5 Flash is substantially cheaper than Pro, and prompts under 128K tokens qualify for lower per-token rates on Flash. Context caching is available to reduce costs when the same large context is reused across many requests. Pricing changes frequently — check the Google AI Studio pricing page for current rates before sizing your budget.
Google AI Studio is a developer console for prototyping — fast to start, with a free tier and API keys scoped to your Google account. Vertex AI is the enterprise deployment path: it offers VPC Service Controls to keep data within your GCP project, IAM-based access control, audit logging, SLAs, and integration with other Google Cloud services. For any production workload handling sensitive data, we recommend Vertex AI over direct AI Studio API usage.
When you use the Gemini API through Google AI Studio, Google may use prompts and responses to improve its models by default, though you can opt out. On Vertex AI, your data is not used for model training, and all processing stays within your selected GCP region. For applications handling PII, confidential documents, or regulated data, Vertex AI with VPC Service Controls is the correct deployment path.
Build With Google Gemini
Talk to our AI engineers about your Gemini integration. We will design the right architecture — long-context analysis, multimodal pipelines, or Vertex AI deployment — for your use case.
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