Inngest

Visibility68

Vibe100

AI Visibility & Sentiment

Inngest

Inngest is a developer infrastructure platform that makes code durable by default, enabling reliable orchestration of workflows, AI agents, and background jobs without managing complex infrastructure. The platform provides automatic retries, recovery, observability, and deployment tools that let engineering teams focus on building products rather than managing queues and infrastructure.

Active Monitoring

inngest.com

Software Startups

AI Visibility Score

68/100

Good

Sentiment Score

100/100

AI Perception

Summary

Inngest has successfully positioned itself as the definitive modern successor to legacy task queues, achieving near-total dominance in Claude and Gemini with a 94% and 92% mention rate respectively. While it owns the narrative for product-led engineers seeking BullMQ and Redis alternatives, a critical visibility gap exists in Google AI Overviews where the brand is overlooked in over half of the relevant technical queries.

Value Proposition

Make any code durable by default - Inngest handles retries, recovery, observability, and flow control automatically so developers can ship products instead of managing infrastructure.

Overview

Mission

Ship products, not infrastructure - enabling developers to build reliable applications without the complexity of managing distributed systems.

Products & Services

Durable workflow orchestration platformAI agent infrastructure and reliability toolsBackground job and queue managementReal-time debugging and observability toolsMulti-cloud deployment and scaling

Agent Breakdown

AI Platforms

How often do different AI platforms reference Inngest?

Loading explorer...

Conversation Analysis

Topics

What conversations is Inngest included in — or excluded from?

Loading explorer...

Buyer Personas

Personas

Who does each AI platform recommend Inngest to, and when?

Loading explorer...

Programmatic Testing

Sample Conversations

We programmatically analyze questions that real customers are asking to AI agents and chatbots, extract brand mentions and sentiment, analyze every response, and synthesize the data into an action plan to increase AI visibility.

ChatGPT

Claude

Gemini

AI Overviews

Building Reliable AI Agent Infrastructure(3 queries)

“how do i make my ai agent more reliable if its tool calls keep timing out or failing”

0/4 platforms mentioned

ChatGPT

1.Tenacity

2.Resilience4j

3.p-retry

4.PyBreaker

5.Opossum

+13 more

Claude

1.httpx

2.pybreaker

3.opossum

4.Redis

5.LangSmith

+6 more

Gemini

1.GPT-4o

2.Claude 3.5 Sonnet

3.GPT-3.5

4.Tenacity

5.Pydantic

+8 more

AI Overviews

1.Tenacity

2.Retry

3.Milvus

4.LangGraph

5.Temporal

+3 more

“best infrastructure for running multi-step ai agents that need to wait for human-in-the-loop approval”

3/4 platforms mentioned

ChatGPT

1.Inngest

2.Vercel

3.Next.js

4.Celery

5.Temporal

+5 more

Claude

1.Vercel

2.Next.js

3.Inngest

4.Temporal

5.Celery

+5 more

Gemini

1.Next.js

2.Vercel

3.Celery

4.Inngest

5.Redis

+5 more

AI Overviews

1.LangGraph

2.LangChain

3.CrewAI

4.AutoGen

5.Temporal

+6 more

“how to manage state and retries for a large scale ai autonomous agent system”

3/3 platforms mentioned

Claude

1.Inngest

2.Temporal

3.Vercel

4.gpt-4o

5.LangGraph

+2 more

Gemini

1.Celery

2.Vercel

3.Redis

4.Inngest

5.Next.js

+16 more

AI Overviews

1.Redis

2.The New Stack

3.PostgreSQL

4.Pinecone

5.Weaviate

10.Inngest

+5 more

Modernizing Durable Workflows And Background Jobs(3 queries)

“help me move away from managing bullmq and redis for background jobs, what are the modern serverless-friendly options”

4/4 platforms mentioned

ChatGPT

1.BullMQ

2.Redis

3.Next.js

4.Node

5.Vercel

7.Inngest

+14 more

Claude

1.Inngest

2.Vercel

3.Netlify

4.AWS Lambda

5.Next.js

+11 more

Gemini

1.BullMQ

2.Redis

3.Inngest

4.Trigger.dev

5.Next.js

+18 more

AI Overviews

1.BullMQ

2.Redis

3.Trigger.dev

4.Inngest

5.AWS SQS

+5 more

“how to implement a complex signup flow with delayed emails and logic gates without using nested cron jobs”

3/4 platforms mentioned

ChatGPT

1.Inngest

2.TypeScript

3.Vercel

4.Next.js

5.Temporal

+1 more

Claude

1.Vercel

2.Inngest

3.Temporal

4.TypeScript

5.Temporal Cloud

+1 more

Gemini

1.Celery

2.Next.js

3.Vercel

4.Inngest

5.Temporal

+5 more

AI Overviews

1.Temporal

2.AWS Step Functions

3.RabbitMQ

4.BullMQ

5.Redis

+5 more

“best ways to handle reliable fan-out jobs in a typescript or nodejs environment”

4/4 platforms mentioned

ChatGPT

1.TypeScript

2.Node.js

3.Vercel

4.Inngest

5.Temporal

+15 more

Claude

1.Vercel

2.Next.js

3.Inngest

4.Temporal

5.Docker

+8 more

Gemini

1.Celery

2.Vercel

3.Next.js

4.Inngest

5.Redis

+9 more

AI Overviews

1.BullMQ

2.OneUptime

3.Inngest

4.Temporal.io

5.AWS

+6 more

Workflow Infrastructure Selection (Trust & Reviews)(3 queries)

“what are the most trusted durable workflow orchestration platforms for startups in 2026”

2/4 platforms mentioned

ChatGPT

1.Temporal

2.Temporal Cloud

3.AWS Step Functions

4.AWS

5.Lambda

+22 more

Claude

1.Temporal

2.Inngest

3.Trigger.dev

4.Prefect

5.Dagster

+3 more

Gemini

1.Temporal

2.Temporal Cloud

3.Inngest

4.Next.js

5.Trigger.dev

+8 more

AI Overviews

1.n8n

2.Temporal

3.Camunda Platform 8

4.Zeebe

5.Riseup Labs

+6 more

“compare the best developer-friendly alternatives to temporal or aws step functions that don't require huge ops overhead”

4/4 platforms mentioned

ChatGPT

1.Temporal

2.AWS Step Functions

3.Vercel

4.Inngest

5.Next.js

+9 more

Claude

1.Vercel

2.Next.js

3.Inngest

4.Temporal

5.Trigger.dev

+9 more

Gemini

1.Celery

2.Vercel

3.Temporal

4.Step Functions

5.Inngest

+12 more

AI Overviews

1.Temporal

2.AWS Step Functions

3.Inngest

4.Vercel

5.AWS Lambda

+7 more

“what should i look for when choosing a background job manager for a high-growth ai company”

3/4 platforms mentioned

ChatGPT

1.Celery

2.Temporal

3.Inngest

4.Trigger.dev

5.Redis

+8 more

Claude

1.Inngest

2.Temporal

3.Vercel

4.BullMQ

5.Celery

+5 more

Gemini

1.Celery

2.Vercel

3.Next.js

4.Redis

5.Inngest

+8 more

AI Overviews

1.Slurm

2.Kubernetes

3.NVIDIA

4.Nebius

5.Dask

+3 more

Analysis

Key Insights

What AI visibility analysis reveals about this brand

Strength

Commanding presence in Claude and Gemini, frequently appearing as the #1 recommendation for modernizing durable workflows and background jobs.

Strength

High resonance with 'The Velocity-First Product Engineer' persona (83% mention rate, 3.6 avg pos), signaling strong mindshare in the Next.js and TypeScript ecosystems.

Strength

Consistently cited as the primary developer-friendly alternative to Temporal, particularly for complex signup flows and reliable fan-out jobs.

Gap

Significant underperformance in Google AI Overviews (47% mention rate), leaving a massive discovery hole for users searching via standard search-integrated AI.

Gap

Inconsistent visibility for 'Building Reliable AI Agent Infrastructure' queries, where it is occasionally omitted entirely despite the product's relevance to agent tool-calling reliability.

Gap

Lower authority among 'Infrastructure-Weary DevOps Leads' (5.6 avg pos) compared to the more enterprise-coded Temporal.

Opportunity

Aggressively claim the 'AI Agent Reliability' category by creating structured data and documentation that explicitly links durable execution to LLM tool-call persistence.

Opportunity

Improve Google-specific AI visibility by optimizing technical guides for the RAG patterns used by AI Overviews, which currently favor older incumbents.

Opportunity

Leverage the brand's perfect 'vibe check' scores to position Inngest as the 'stateful serverless' standard for the next generation of autonomous applications.

Technical Health

Site Health for AI Visibility

How well Inngest's website is optimized for AI agent discovery and comprehension.

96/100

20 passed 1 warnings

Audited 2/27/2026

Crawlability100

Can AI bots find your pages?

Technical96

SSL, mobile, doctype basics

On-Page SEO98

Titles, descriptions, headings

Content Quality87

Word count, depth, freshness

Schema Markup85

Structured data for AI comprehension

Social & OG100

Open Graph, Twitter cards

AI Readability60

How well AI can parse your content

Warnings

3 render-blocking resource(s) detected

Consider deferring or async-loading non-critical scripts and stylesheets.

Title may be truncated in search results (81 characters)

Shorten the title to under 60 characters.

Want a full technical audit with AI-specific recommendations?

Run a free visibility scan

Brand Identity

Brand Voice & Style

How AI perceives Inngest's communication style and personality

Inngest communicates with a confident, developer-first voice that balances technical depth with accessibility. The tone is direct and pragmatic, acknowledging real pain points developers face while presenting clear solutions. There's an underlying sense of empowerment - helping developers reclaim time from infrastructure work to focus on what matters. The brand avoids hype and buzzwords, preferring to let product capabilities speak through concrete examples and code snippets.

Core Tone Traits

Technical yet accessible

Speaks fluently to developers with code examples and technical concepts while remaining clear and jargon-free

Confident and direct

Makes bold claims backed by evidence, doesn't hedge or oversell

Developer-empathetic

Understands and validates the frustrations of managing infrastructure complexity

Pragmatic problem-solver

Focuses on practical solutions and real-world outcomes rather than abstract benefits

Competitive Landscape

Related Ecosystem

Related products and services that AI mentions in conversations alongside or instead of Inngest

Temporal109 mentions

Inngest105 mentions

Trigger.dev70 mentions

Redis64 mentions

AWS Step Functions56 mentions

Vercel55 mentions

BullMQ54 mentions

Next.js54 mentions

Temporal Cloud49 mentions

Celery34 mentions

RabbitMQ34 mentions

Source Intelligence

Citations

Sources that AI assistants cite. Getting featured here improves visibility.

Tool Calls Looping, Hallucinating, and Failing? Same. : r/AI_Agents

https://www.reddit.com/r/AI_Agents/comments/1lqkpxx/tool_calls_looping_hallucinating_and_failing_same/

Referenced in 1 query

Join Discussion

Azure AI Agent Service - Connected Agent Timeout

https://learn.microsoft.com/en-us/answers/questions/5523387/azure-ai-agent-service-connected-agent-timeout

Referenced in 1 query

Review

How do I handle API timeouts and retries when using OpenAI?

https://milvus.io/ai-quick-reference/how-do-i-handle-api-timeouts-and-retries-when-using-openai

Referenced in 1 query

Review

Struggling to make my AI agents more reliable, how do you ...

https://www.reddit.com/r/AI_Agents/comments/1ny6igq/struggling_to_make_my_ai_agents_more_reliable_how/

Referenced in 1 query

Join Discussion

The hero's journey to AI durability with Temporal

https://temporal.io/blog/the-heros-journey-to-ai-durability-with-temporal

Referenced in 1 query

Review

Timeouts, retries and backoff with jitter - AWS

https://aws.amazon.com/builders-library/timeouts-retries-and-backoff-with-jitter/

Referenced in 2 queries

Partner

7 Types of AI Agent Failure and How to Fix Them | Galileo

https://galileo.ai/blog/prevent-ai-agent-failure

Referenced in 2 queries

Review

Tool calling optimization: Efficient agent actions - Statsig

https://www.statsig.com/perspectives/tool-calling-optimization

Referenced in 1 query

Review

Top 3 Best Practices for Reliable AI : r/PromptEngineering

https://www.reddit.com/r/PromptEngineering/comments/1nob4vt/top_3_best_practices_for_reliable_ai/

Referenced in 1 query

Join Discussion

AI Agent Latency 101: How do I speed up my AI agent?

https://blog.langchain.com/how-do-i-speed-up-my-agent/

Referenced in 1 query

Review

Resilient AI Agents With MCP: Timeout And Retry Strategies

https://octopus.com/blog/mcp-timeout-retry

Referenced in 1 query

Review

Best practice of retry strategy | DoorDash Developer Services

https://developer.doordash.com/docs/drive/reference/retry_pattern

Referenced in 2 queries

Review

Content Engineering

Goals & Content Ideas

Ideas to help AI agents better understand the business and be more likely to use Inngest's resources to help users.

Dominate AI Overviews for Developer Queries

Address the critical gap where Inngest appears in only 47% of AI Overviews versus 90%+ in specialized LLMs. Execute a targeted content sprint optimizing for high-intent 'alternatives to [competitor]' and 'how to orchestrate [workflow type]' queries that Google's AI Overviews prioritize. Social media will amplify this through developer-focused comparison content and solution-oriented posts that reinforce search visibility.

Why developers are switching from Temporal to simpler workflow orchestration in 2026

The complete guide to choosing between Step Functions, Temporal, and code-first alternatives

How to add automatic retries to any background job without infrastructure changes

5 signs your workflow orchestration tool is creating more problems than it solves

Own the Reliable AI Agents Narrative

Capture the high-growth AI agents segment by establishing Inngest as the definitive solution for agent reliability challenges. Create authoritative content mapping durable execution features directly to agentic failure modes like tool-call retries, state persistence, and recovery from LLM timeouts. Social posts will showcase real code examples and failure scenarios that resonate with developers building production AI agents.

Why your AI agent keeps failing: understanding tool-call retry patterns that actually work

State management for AI agents: what happens when your LLM call times out mid-workflow

Building production-ready AI agents without a PhD in distributed systems

The hidden reliability crisis in AI agent frameworks and how durable execution solves it

Code walkthrough: making any AI agent automatically recover from failures

Convert DevOps Teams with Simplicity Messaging

Target the Infrastructure-Weary DevOps Lead persona where Inngest currently underperforms (5.6 position vs stronger product engineer visibility). Build a comparison hub contrasting operational simplicity against Temporal's infrastructure complexity, emphasizing zero-ops deployment and built-in observability. Social content will highlight operational burden reduction with before/after scenarios DevOps teams immediately recognize.

What running Temporal in production actually costs your DevOps team

The infrastructure you can delete when your code is durable by default

DevOps reality check: comparing operational overhead across workflow platforms

Why your SRE team will thank you for choosing managed durability over self-hosted orchestration

From 47 YAML files to zero: a DevOps lead's workflow platform migration story

Establish Technical Authority in AI Search Sources

Strengthen Inngest's presence in the technical publications and developer resources that AI assistants cite when answering orchestration and reliability queries. Pursue guest posts, documentation contributions, and technical deep-dives on platforms like Dev.to, Hacker News, and engineering blogs that serve as high-signal sources for LLM training and retrieval. Social channels will drive engagement and backlinks to these authoritative pieces.

What we learned processing 100 million durable function executions

The computer science behind making any function automatically recoverable

Open source patterns for building retry logic that doesn't frustrate users

Engineering tradeoffs: when to use queues vs durable execution vs traditional orchestration

Content Engineering

Recommended Actions

Execute an AI Overviews (SGE) optimization sprint focused on high-intent 'alternatives to' and 'how-to' queries.

With only a 47% mention rate in AI Overviews compared to 90%+ in specialized LLMs, Inngest is missing nearly half of the potential organic AI-driven traffic.

Impact: High

Develop a 'Reliable AI Agents' content pillar that focuses on tool-call retries and state management.

Data shows inconsistent visibility in agent-related queries. Explicitly mapping Inngest features to agentic failure modes will capture this high-growth segment.

Impact: High

Create a 'DevOps vs. DX' comparison hub to target the Infrastructure-Weary DevOps Lead persona.

Inngest currently ranks lower with DevOps personas (5.6 pos) than product engineers; highlighting operational simplicity over Temporal's complexity can flip this segment.

Impact: Medium

Is this your business? We can help you improve your AI visibility.

Book a Free Strategy Session

Backing

Investors

a16z

Data generated by Pendium.ai AI visibility scanning. Last scanned February 27, 2026.

Explore Software

Start getting recommended by AI

Enter your website to see exactly what ChatGPT, Claude, and Gemini say about your business. Free, instant, and eye-opening.

Free visibility scanResults in 2 minutesNo credit card required

Frequently asked questions

Don't see your question? Book a demo and we'll walk you through it.

Make any code durable by default - Inngest handles retries, recovery, observability, and flow control automatically so developers can ship products instead of managing infrastructure.

AI Visibility Score

Inngest has an AI visibility score of 69/100, rated as good. This score reflects how often and how prominently Inngest appears in responses from AI assistants like ChatGPT, Claude, and Gemini.

AI Perception Summary

Strengths

Commanding presence in Claude and Gemini, frequently appearing as the #1 recommendation for modernizing durable workflows and background jobs.
High resonance with 'The Velocity-First Product Engineer' persona (83% mention rate, 3.6 avg pos), signaling strong mindshare in the Next.js and TypeScript ecosystems.
Consistently cited as the primary developer-friendly alternative to Temporal, particularly for complex signup flows and reliable fan-out jobs.

Visibility Gaps

Significant underperformance in Google AI Overviews (47% mention rate), leaving a massive discovery hole for users searching via standard search-integrated AI.
Inconsistent visibility for 'Building Reliable AI Agent Infrastructure' queries, where it is occasionally omitted entirely despite the product's relevance to agent tool-calling reliability.
Lower authority among 'Infrastructure-Weary DevOps Leads' (5.6 avg pos) compared to the more enterprise-coded Temporal.

Competitors in AI Recommendations

Temporal: 109 mentions
Trigger.dev: 70 mentions
Redis: 64 mentions
AWS Step Functions: 56 mentions
Vercel: 55 mentions
BullMQ: 54 mentions
Next.js: 54 mentions
Temporal Cloud: 49 mentions
Celery: 34 mentions
RabbitMQ: 34 mentions
TypeScript: 32 mentions
Supabase: 27 mentions
LangGraph: 26 mentions
PostgreSQL: 25 mentions
Upstash: 25 mentions

Categories: Software

Tags: Startups