惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Jina AI
Jina AI
V
Visual Studio Blog
博客园 - 司徒正美
TaoSecurity Blog
TaoSecurity Blog
博客园 - 聂微东
IT之家
IT之家
博客园_首页
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
C
Cyber Attacks, Cyber Crime and Cyber Security
博客园 - Franky
雷峰网
雷峰网
罗磊的独立博客
S
Schneier on Security
C
Cybersecurity and Infrastructure Security Agency CISA
The Cloudflare Blog
T
Tailwind CSS Blog
B
Blog RSS Feed
H
Help Net Security
T
The Blog of Author Tim Ferriss
C
CXSECURITY Database RSS Feed - CXSecurity.com
T
Threatpost
C
CERT Recently Published Vulnerability Notes
博客园 - 三生石上(FineUI控件)
P
Palo Alto Networks Blog
I
Intezer
G
GRAHAM CLULEY
Engineering at Meta
Engineering at Meta
S
Securelist
J
Java Code Geeks
V
V2EX
Y
Y Combinator Blog
Simon Willison's Weblog
Simon Willison's Weblog
L
LINUX DO - 热门话题
云风的 BLOG
云风的 BLOG
Spread Privacy
Spread Privacy
MongoDB | Blog
MongoDB | Blog
P
Privacy International News Feed
cs.CL updates on arXiv.org
cs.CL updates on arXiv.org
B
Blog
Forbes - Security
Forbes - Security
Google Online Security Blog
Google Online Security Blog
Help Net Security
Help Net Security
S
SegmentFault 最新的问题
N
Netflix TechBlog - Medium
Webroot Blog
Webroot Blog
Microsoft Security Blog
Microsoft Security Blog
SecWiki News
SecWiki News
Scott Helme
Scott Helme
aimingoo的专栏
aimingoo的专栏
N
News and Events Feed by Topic

Developer tools

We're rolling out AlphaEvolve widely to solve Google Cloud customers' hardest problems. Expanding Managed Agents in Gemini API: background tasks, remote MCP and more The latest AI news we announced in June 2026 Ask an AI expert: What exactly is the full stack? Interactions API: our primary interface for Gemini models and agents DiffusionGemma: 4x faster text generation See what 3 builders are making with Gemma 4 Bringing the latest Gemini models to Apple developers Gemma 4 QAT models: Optimizing model compression for mobile and laptop efficiency Kaggle is making AI benchmark creation effortless Introducing Gemma 4 12B: a unified, encoder-free multimodal model How we used Gemini to build Google I/O 2026 Take our I/O 2026 quiz, vibe coded in Google AI Studio. Here's what developers can do with the latest Google Play updates. Building the agentic future: Developer highlights from I/O 2026 I/O 2026 Introducing Managed Agents in the Gemini API Bring any idea to life: Google AI Studio at I/O 2026 Gemini API File Search is now multimodal: build efficient, verifiable RAG Accelerating Gemma 4: faster inference with multi-token prediction drafters The latest AI news we announced in April 2026 Reduce friction and latency for long-running jobs with Webhooks in Gemini API Join the new AI Agents Vibe Coding Course from Google and Kaggle Deep Research Max: a step change for autonomous research agents Start vibe coding in AI Studio with your Google AI subscription. Prepay for the Gemini API to get more control over your spend Introducing Learn Mode: your personal coding tutor in Google Colab Gemma 4: Byte for byte, the most capable open models The latest AI news we announced in March 2026 Improve coding agents’ performance with Gemini API Docs MCP and Agent Skills. Build with Veo 3.1 Lite, our most cost-effective video generation model
New ways to balance cost and reliability in the Gemini API
Lucia Loher Product Manager, Gemini API · 2026-04-02 · via Developer tools

Introducing Flex and Priority inference: advanced controls for developers to optimize costs and reliability through a single, unified interface.

Hussein Hassan Harrirou

Engineering, Gemini API

Your browser does not support the audio element.

Listen to article

This content is generated by Google AI. Generative AI is experimental

[[duration]] minutes

Today, we are adding two new service tiers to the Gemini API: Flex and Priority. These new options give you granular control over cost and reliability through a single, unified interface.

As AI evolves from simple chat into complex, autonomous agents, developers typically have to manage two distinct types of logic:

  • Background tasks: High-volume workflows like data enrichment or "thinking" processes that don't need instant responses.
  • Interactive tasks: User-facing features like chatbots and copilots where high reliability is needed.

Until now, supporting both meant splitting your architecture between standard synchronous serving and the asynchronous Batch API. Flex and Priority help to bridge this gap. You can now route background jobs to Flex and interactive jobs to Priority, both using standard synchronous endpoints. This eliminates the complexity of async job management while giving you the economic and performance benefits of specialized tiers.

Flex Inference: scale innovation for 50% less

Flex Inference is our new cost-optimized tier, designed for latency-tolerant workloads without the overhead of batch processing.

  • 50% price savings: Pay half the price of the Standard API by downgrading criticality of your request (making them less reliable, and adding latency).
  • Synchronous simplicity: Unlike the Batch API, Flex is a synchronous interface. You use the same familiar endpoints without managing input/output files or polling for job completion.
  • Ideal use cases: Background CRM updates, large-scale research simulations, and agentic workflows where the model "browses" or "thinks" in the background.

Get started fast by simply configuring the service_tier parameter in your request:

Flex tier will be available for all paid tiers and is available for GenerateContent and Interactions API requests.

Priority Inference: Highest reliability for critical apps

The new Priority Inference tier offers our highest level of assurance at a premium price point. This helps to ensure your most important traffic is not preempted, even during peak platform usage.

  • Highest criticality: Priority requests get highest criticality leading to higher reliability, even during peak load.
  • Graceful downgrade: If your traffic exceeds your Priority limits, overflow requests are automatically served at the Standard tier instead of failing. This keeps your application online and helps to ensure business continuity.
  • Transparent response: The API response indicates which tier served your request, giving you full visibility into your performance and billing.
  • Ideal use cases: Real-time customer support bots, live content moderation pipelines, and time-sensitive requests.

To use Priority Inference, simply set the service_tier parameter accordingly:

Priority inference will be available to users with Tier 2 / 3 paid projects across the `GenerateContent` API and Interactions API endpoints.

Visit the Gemini API documentation to see the full pricing breakdown and start optimizing your production tiers today. To see it in action, check out the cookbook for runnable code examples.

Related stories