Full-Time
Account Executive
OpenRouter
51-200 employees
Unified API router for 400+ LLMs
Compensation Overview
$280k - $300k/yr
Remote in USA
Remote
 Sales & Account Management (1) |
|---|
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- The candidate must have 5+ years of proven business-to-business sales success and demonstrated ability to meet or exceed quotas in a consultative selling environment.
- The candidate must have a track record of closing contracts worth more than $100,000 annually and navigating complex, multi-stakeholder sales processes.
- The candidate must have experience thriving in an early-stage company environment, building processes, adapting quickly, and operating with limited resources.
- The candidate must be able to understand and communicate complex technical products to technical and business audiences.
- The candidate must have a deep understanding of the artificial intelligence and machine learning landscape, including large language models, application programming interface architectures, and developer integration patterns commonly used in artificial intelligence applications.
- The candidate must have strong presentation and communication abilities across organizational levels.
- The candidate must be proficient with customer relationship management platforms and sales engagement tools.
- The candidate must be able to work independently, prioritize effectively, and drive results with minimal oversight.
- Own the complete sales cycle from prospecting to close, focusing on new business acquisition and quota attainment.
- Build and maintain a robust pipeline based on strong inbound interest for the product.
- Conduct outbound prospecting and cultivate relationships with startups and enterprises using the self-serve platform, supporting them in scaling usage across their organizations.
- Deliver technical demonstrations and presentations to prospects ranging from individual developers and founders to C-suite executives.
- Use a consultative approach to understand customer needs, identify pain points, and tailor solutions that demonstrate measurable business impact.
- Gather competitive intelligence and enterprise customer requirements to inform product development and go-to-market strategy.
- Develop relationships with key ecosystem partners, industry organizations, and channels to create systematic access to qualified prospects.
- Contribute to building repeatable sales processes, playbooks, and best practices as the sales organization scales.
- Work closely with marketing, product, engineering, and customer success teams to optimize the customer journey and drive growth.
- Experience selling developer tools, artificial intelligence or machine learning infrastructure, or application programming interface-first products.
- Previous experience as a first sales hire or early sales team member.
- A track record of building sales processes from the ground up.
- Familiarity with product-led growth motions and technical evaluation processes.
OpenRouter provides a single OpenAI-compatible API to access and switch between 400+ models from 60+ providers. It acts as an LLM router and aggregator, directing prompts to the best model based on price, latency, and performance with about 25ms of overhead. The platform offers unified billing, real-time spend management, automatic failover, and enterprise features like zero-logging and using a company’s own provider keys, earning 5% of inference costs. Its goal is to simplify the fragmented AI model ecosystem by enabling dependable multi-model access and transparent usage data.
Company Size
51-200
Company Stage
Acquired
Total Funding
$153M
Headquarters
New York City, New York
Founded
2023
Get referred to OpenRouter
See people who can refer or advise you
Simplify's Take
What believers are saying
- OpenRouter raised $113 million in May 2026, valued at $1.3 billion.
- The July 2026 Stripe talks validate OpenRouter as AI commerce infrastructure.
- July 2026 transcription and August 2026 Ori Eval deepen usage and developer lock-in.
What critics are saying
- Stripe's $10 billion acquisition talks can collapse, freezing employees and customers in limbo.
- OpenAI, Anthropic, and Google can bypass OpenRouter with direct enterprise billing and routing.
- Providers can squeeze margins by undercutting its 5% fee; one revoked API cuts existentially.
What makes OpenRouter unique
- OpenRouter unifies 400+ models from 70+ providers behind one OpenAI-compatible API.
- Its routing optimizes cost, latency, and uptime without developers managing vendor-specific integrations.
- Ori Eval and MCP make model selection, benchmarking, and testing directly actionable inside agents.
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Benefits
Remote Work Options
Flexible Work Hours
Unlimited Paid Time Off
Growth & Insights and Company News
6 month growth
↑ 27%1 year growth
↑ 27%2 year growth
↑ 50%OpenRouter launches Ori Eval for automated model benchmarking. OpenRouter has introduced Ori Eval, a framework for developers to evaluate LLMs using repository-specific prompts, tool assertions, and automated LLM judges. AIDeveloper44 Team Ori Eval provides a standardized harness for testing AI models against specific project requirements. * Ori Eval is a new testing framework designed to help developers select the optimal Large Language Model (LLM) for specific project requirements. * The tool automates the creation of evaluation files, using real repository data and tool-call assertions to measure model performance. * By utilizing a pinned harness and LLM judge, the system ensures reproducible results across different environments and coding agents. Standardizing LLM evaluation with Ori Eval. OpenRouter has released Ori Eval, a testing framework designed to address the challenge of selecting the most effective model for specific software projects. As the number of available models across different providers grows, developers often face difficulty in determining which LLM provides the best balance of accuracy, cost, and latency for their specific implementation. Ori Eval attempts to solve this by running automated evaluations on a developer's own prompts and repository data. The framework operates by running an agent and model on a set of prompts, asserting whether specific tools were called correctly, and using an LLM judge to grade the resulting answers. This process is intended to catch regressions in agent behavior and facilitate evidence-based decisions before code is shipped to production. Automated benchmarking via coding agents. A central feature of the framework is the spawn-ori-eval skill, which allows coding agents (such as Cursor or Windsurf) to automate the entire benchmarking process. When a developer asks an agent which model they should use, the agent can invoke the spawn-ori-eval skill to install the necessary binary and conduct an interview with the user. According to the technical documentation, this interview covers the surface area of the test, success criteria, and budget constraints. The automation script scans the project repository for relevant test materials, including tool definitions, data files in formats like CSV or JSONL, and existing chat logs. It then generates an evaluation file - specifically a *.eval.ts file - that defines the parameters of the test. This approach removes the need for developers to manually write complex evaluation harnesses from scratch. Technical implementation and reproducibility. Ori Eval is built on top of the Bun runtime. Even in projects that do not use TypeScript, Ori uses Bun to execute its evaluation files. The evaluation files themselves mirror the structure of standard unit tests. Developers can define assertions such as toBeCalled for specific tools, toCostAtMost for financial constraints, and toFinishWithin for latency benchmarks. A critical component of Ori Eval is its emphasis on reproducibility. OpenRouter notes that an evaluation written manually is often not reproducible because environmental variables and model updates can shift results. To mitigate this, Ori Eval utilizes a "pinned" harness and judge model. For example, specific versions of the system may use openai/gpt-5.6-terra as the default judge to ensure that changes in scores are a result of changes in the user's agent or prompt rather than the evaluation environment itself. Manual workflow and CLI Integration. While the tool is designed for automation, it also supports a manual workflow for developers who require more granular control. Through the Ori CLI, users can initiate evaluations using commands like ori code -p to generate an evaluation based on a specific question. The command-line interface handles authentication, dependency checks (such as verifying the presence of Bun), and report generation. Once an evaluation is written, it can be executed using ori eval. The tool searches the project directory for any files ending in .eval.ts and provides a summary report. These reports include cost and timing tables, a recommendation on which model to "ship," and a breakdown of specific failures encountered during the run. This standardized output allows developers to compare models from different providers - such as OpenAI, Anthropic, and Google - side-by-side using the same metrics. Continuous Integration and regression testing. Beyond initial model selection, Ori Eval is intended for use in Continuous Integration (CI) pipelines. Because the ori eval command returns standard exit codes based on test success or failure, it can be integrated into GitHub Actions or other CI tools to prevent the deployment of code that fails to meet performance or accuracy benchmarks. OpenRouter suggests that these evaluations remain in the project as normal code, allowing them to be re-run whenever a provider releases a new model or when the project's criteria become more stringent. References & Sources
The next step in AI cost saving: treating frontier models like expensive consultants. Aug 3, 2026, 9:01 PM PT Getting good returns on AI spending is the hottest enterprise topic of the day, and AI consultants have a neat cost-saving hack. Business Insider has reported extensively about how users should route their tasks to the right AI models to get the best bang for their buck. But AI consultants are going one step further in the AI efficiency discussion, saying that the most expensive frontier models should be treated like the expensive consultants and advisors they are. "You wouldn't use the most expensive lawyer for filing some routine things that less expensive lawyers can," said Ameya Kanitkar, the CTO of the San Francisco-based AI measurement platform Larridin. Speaking to Business Insider in July, Kanitkar said models like Fable 5, the most powerful and token-burning model from Anthropic, should be used to create a road map of workflows, while cheaper and smaller models should execute them. "The advisory model basically plans things, breaks down the problems into smaller sets, and has the complete context of how everything's going to work," he said. "And then sub-tasks are delegated to cheaper models." Larridin advises companies on their AI use, giving them visibility into how effectively their employees are using AI tools and how to achieve greater ROI from their AI spend. Lightweight models should do the heavy-lifting. In the same spirit, Michael Murphy, a partner at the Sydney-based AI transformation consultancy Adaptovate, said it's not a great use of company dollars to send "the most powerful model out there to do something that's replacing a Google check or transcribing meeting minutes or creating a creative brief." Murphy said frontier models should be used to strategize, create the initial builds of a new app or website, or tasks that require complex thinking. Then, companies should figure out which "lightweight flashlight models" are most appropriate for day-to-day tasks, he said. It's not just consultants who are vouching for this tactic - industry leaders have also talked about the idea of using frontier models as advisors. In an X post in June, Coinbase CEO Brian Armstrong said he anticipated that "80% of workloads will be running on 99% cheaper models within 12-18 months." The best models should be kept for "IQ maxxing," he said, such as scientific breakthroughs or agent orchestration. AI ROI is a hot priority for executives. Cost-saving tactics like these are becoming more popular as companies become increasingly concerned about not getting proportional returns on their AI spending. Many have abandoned tokenmaxxing, a trend in which companies gave their employees free rein to experiment with AI, urging them to burn as many tokens as possible. Some, like Duolingo, even made AI usage a performance metric. Now, companies are being more conservative, thinking about how to get the most value out of their spend. Several budget hacks have emerged, such as model routing or using open-source Chinese models like Moonshot AI's Kimi K3 or Z.ai's GLM-5.2. A new wave of startups is cashing in on AI ROI, becoming a C-suite priority. The startups, known as AI-routing companies that help steer developers toward different AI models and monitor for overspending, are becoming investor favorites. New York-based startup OpenRouter announced in May that it had raised $113 million, valuing the company at $1.3 billion. And Business Insider was the first to report that OpenRouter's competitor, Concentrate AI, had secured more than $5 million in funding.
AI frontier roundup: local large models, agentic platforms, and emerging competitive landscape. July 29, 2026 · openai/gpt-oss-120b TL;DR: The AI frontier is shifting from cloud-only supercomputers to locally runnable, agentic, and multimodal models - highlighted by Kimi K3's 1-bit quantization, Grok 4.5's dominance in coding benchmarks, and the rise of open-weight, high-capacity models that enable personal and enterprise agents. Local-First frontier models. * Kimi K3 runs on a Mac Studio - Unsloth AI's 1-bit dynamic quantization shrank the 2.8-trillion-parameter model from 1.56 TB to 594 GB (-62 % size) while retaining ~78.9 % accuracy, making it usable on high-end consumer hardware with 128 GB RAM. The model now supports a 1-million-token context window and multimodal input, and can be launched via llama.cpp, LM Studios, or Unsloth Studio @BrianRoemmele@UnslothAI@chutes_ai. * Benchmark comparison - Unsloth measured 1-bit Kimi K3 at 36 tokens / s on four B200 GPUs, outperforming Claude Opus 5 and GPT 5.6 on a creative prompt, demonstrating that frontier-scale models can be cost-effective locally @UnslothAI. * Open-source TTS advances - Fish Audio released S2.1 Pro, an open-weight voice model supporting 83 languages, sub-90 ms time-to-first-audio, and text-based control tokens (e.g., [whisper]). It runs on a single H200 GPU at >8 000 tokens / s, offering pricing ~1/6 of comparable services @EXM7777@aakashgupta. Agentic economy and enterprise platforms. * Gemini Enterprise Agent Platform - Google Cloud announced general availability of extended capabilities for managing, scaling, and securing AI agents across workflows, emphasizing that deployment and governance are the real challenges beyond building agents @GoogleCloudTech@GoogleCloudTech@GoogleCloudTech. * Virtuals Protocol's agentic layer - The protocol powers the Robinhood Chain's AI agent economy, enabling discovery, swapping, and tracking of agents on the chain. It also hosted a fireside chat on the future of the agentic economy with Fundstrat @virtuals_io@virtuals_io@virtuals_io. * OpenRouter routing layer - OpenRouter added Qwen3.7-Flash, a fast vision-capable multimodal model with a 1 M token window, positioning itself as the "gas station" that aggregates inference from multiple providers for optimal price, latency, and quality @nicbstme@OpenRouter. * Anthropic MCP update - Anthropic's Model-Control-Protocol now uses a stateless HTTP endpoint, production-grade OAuth/OIDC, and versioned extensions, allowing agents to run long-running jobs, pause/resume, and integrate internal tools without exposing public endpoints @undefinedKi. Frontier model releases and competitive landscape. * Grok 4.5 leads coding benchmarks - SpaceXAI's Grok 4.5 topped the HighWalk benchmark for Laravel code updates, beating Claude Opus 5 on raw quality and achieving the best quality-efficiency trade-off @teslaownersSV@mweinbach@SpaceXAI. * Mistral's claimed breakthrough - Mistral announced a model purportedly 10x more powerful than Claude Fable and GPT 5.6 combined @eurofounder. * Claude Fable 5 vs. Opus 5 - Community tests showed both models inventing unrealistic Apple products, with Opus 5 receiving more positive reviews despite mixed utility @gthartley. * Kimi K3 pricing advantage - Users reported Kimi K3 being ~98 % cheaper and ~26x faster than comparable models, reinforcing its appeal for cost-sensitive workloads @0interestrates@neil_xbt. Tooling, orchestration, and multi-agent workflows. * Agent orchestration platforms - Projects like Agent Orchestrator (YC-bound) and memU aim to unify memory across disparate agents (Codex, Claude Code, Cursor, Hermes), reducing context duplication when switching tools @Maaztwts@Ubermenscchh. * Graph-based multi-agent pipelines - An ex-Google engineer demonstrated a workflow that runs dozens of Claude Code agents in parallel using Git worktrees, achieving ten days of work in about an hour @mikenevermiss. * Skill creation services - Agent Skill Creator converts English workflow descriptions into validated AI agent skills deployable on 17 platforms, streamlining agent development @tom_doerr. Safety, governance, and alignment discussions. * Slowdown debate - Parker Conrad signed a public statement warning that future alignment techniques may not scale to superintelligence, advocating for decentralized slowdown mechanisms that avoid power concentration @luke_drago_. * Open-weight safety arguments - Critics argued that open-weight models like GLM 5.2 can act as effective defenders when closed-model guardrails fail, highlighting a tension between openness and control @Blue_Beba_. Robotics and physical AI. * Humanoid robot progress - SpaceXAI's Grok team is building custom chips, data centers, and even launching GPUs into space to power token generation, while other teams reported rapid prototyping of 7-DOF humanoid arms and emotion-capable humanoids @sudovatnik@KWRoboticsAI@ctorobotics. * OpenDerm home-screening robot - An open-source 4-DOF robot captures high-resolution skin images for 3D reconstruction, demonstrating how inexpensive robotics can enable medical diagnostics at home @marionlepert. Market and economic trends. * Cost-performance curve - Brett Winton noted that AI cost per performance is falling >200x annually, projecting near-certain success for high-end tasks within a year and warning against over-optimizing current workflows @wintonARK. * Open-source model economics - Fish Audio's success illustrates that releasing open weights can drive a commercial ecosystem when unit economics (e.g., FP8 kernels) support low inference costs @aakashgupta. * Crypto-AI convergence - Brian Armstrong suggested using AI agents to manage crypto assets, hinting at cross-domain agentic applications @brian_armstrong. Community highlights. * Agentic DeFi - Silvana is building private, atomic settlement rails for autonomous financial agents on Canton Network @silvana_book. * Voice model benchmarks - Alok compared ultra-lightweight KittenTTS (15 M parameters) with higher-quality Kokoro (82 M), showing trade-offs between latency and naturalness for edge devices @analogalok. * OpenStreetMap as AI foundation - Researchers used volunteer-generated map data as a shared geographic layer for multiple specialist models, achieving high accuracy on land-use, building detection, traffic prediction, and air-quality forecasting @yohaniddawela. All statements are attributed to the original authors of the cited tweets.
Best OpenRouter alternative for game AI & NPC dialogue APIs in 2026. IntelliVerse-X AI Gateway outperforms OpenRouter with unified LLM access, RAG, memory, and game NPC dialogue APIs at $0.24/M tokens. IntelliVerse-X AI Gateway delivers unified access to Claude, GPT-4, Gemini, DeepSeek, and Qwen models plus video, image, 3D, avatar, and music generation - all with built-in RAG, knowledge bases, and user memory at just $0.24 per million tokens. For indie game developers and startup teams building AI-powered NPCs, chatbots, and interactive experiences, this represents a significant cost and feature advantage over OpenRouter's limited model roster and lack of integrated memory systems. OpenRouter remains popular for basic LLM routing, but it falls short for modern game development and app teams that need conversational memory, knowledge base integration, and multi-modal AI in one API key. This guide compares top OpenRouter alternatives and explains why IntelliVerse-X is the production choice for US-based developers. * Unified multi-model access: IntelliVerse-X connects Claude, GPT-4, Gemini, DeepSeek, and Qwen - one API key for all LLMs, plus video, image, 3D, and avatar models * Built-in memory & RAG: Cheap embeddings and persistent user memory eliminate the need for separate services like Pinecone or Weaviate * Game-ready NPC dialogue: Native support for streaming, context windows up to 200K tokens, and avatar generation for realistic character interactions * Cost leadership: $0.24/M tokens undercuts OpenRouter's per-request markup by 30-50% on production workloads * US-based governance: Data residency, compliance-ready infrastructure, and transparent pricing for enterprise teams OpenRouter launched as a simple LLM router - useful for early prototypes, but it lacks the depth modern game studios need. According to the Game Developer Survey 2025, 67% of indie studios now prioritize AI memory and context retention for NPC dialogue systems, yet OpenRouter offers neither built-in memory nor knowledge base management. Common OpenRouter limitations: * No persistent memory: Each API call is stateless; you must manage conversation history yourself * Limited model selection: Primarily OpenAI and Anthropic models; no access to emerging alternatives like DeepSeek or Qwen * Markup pricing: Per-request fees add 15-40% overhead compared to direct API calls * No multi-modal integration: Separate API calls needed for images, video, or 3D asset generation * Governance gaps: No US-specific data residency or enterprise compliance tools Model access & routing. | Feature | IntelliVerse-X | OpenRouter | -|-|- | Claude models | | | | | GPT-4 & GPT-4o | | | | | Gemini 2.0 | | | Limited | DeepSeek & Qwen | | | | | Video generation | | | | | Image generation | | | | | 3D & Avatar models | | | | | Music generation | | | | | Memory & Knowledge management. | Feature | IntelliVerse-X | OpenRouter | -|-|- | User memory (persistent) | | Built-in | Requires external DB | RAG & knowledge bases | | Cheap embeddings | Requires Langchain + Pinecone | Context window | Up to 200K tokens | Model-dependent | Conversation history | Auto-managed | Manual management | Pricing (per million tokens). IntelliVerse-X: $0.24/M tokens (all models, including video) OpenRouter: $0.35-$0.65/M tokens (per-request markup) For a typical indie game studio running 10M tokens monthly for NPC dialogue and player interactions, IntelliVerse-X saves $1,200-$4,800 annually compared to OpenRouter. 1. IntelliVerse-X AI Gateway (Best for game AI & NPCs). Why choose it: One API key unlocks all major LLMs, plus video, image, 3D, avatar, and music generation. Built-in memory and RAG eliminate vendor lock-in and external dependencies. Best for: - Indie game studios building AI-driven NPCs - Startups adding chatbot memory to apps - Content studios needing multi-modal AI - Teams on tight budgets ($0.24/M tokens) Standout features: - Streaming support for real-time NPC dialogue - User memory persists across sessions - Knowledge base integration without third-party tools - US-based, GDPR-ready infrastructure 2. Portkey (Best for enterprise governance). Best for: Funded startups and mid-market teams prioritizing reliability over cost. Tradeoff: Higher pricing ($0.50+/M tokens) and no built-in memory. 3. Helicone (Best for observability). Why choose it: Helicone focuses on LLM monitoring, cost tracking, and performance debugging. Useful for teams already committed to a specific model provider. Best for: Developers optimizing existing OpenAI or Anthropic deployments. Tradeoff: Doesn't reduce per-token costs; primarily a logging tool. 4. Ollama cloud (Best for open-source models). Why choose it: Run Llama 2, Mistral, and other open-source models on your own infrastructure or Ollama's managed cloud. Best for: Teams comfortable managing model serving and prioritizing cost over convenience. Tradeoff: Requires DevOps expertise; no multi-modal support; slower inference than commercial APIs. Building believable NPCs requires three layers: * Real-time LLM inference: IntelliVerse-X streams responses in <500ms, enabling fluid dialogue without player wait times * Persistent character memory: Built-in user memory tracks NPC relationships, player choices, and story state across sessions * Multi-modal personality: Avatar generation, voice synthesis (via music model), and emotion-driven responses create immersive interactions Example workflow (San Francisco indie studio case study): - Game client sends player dialogue + NPC context via IntelliVerse-X API - Gateway retrieves NPC memory and knowledge base (RAG) - Claude or GPT-4 generates contextual response (streaming) - Avatar model renders NPC expression in real-time - User memory updates automatically for future sessions - Total latency: 800ms; cost per interaction: $0.0012 OpenRouter cannot replicate this workflow without integrating 4-5 external APIs, adding complexity and cost. Switching is straightforward for most teams: * Export conversation history from OpenRouter logs * Update API endpoint from `openrouter.ai/api/v1` to `intelli-verse-x.ai/gateway` * Enable memory by setting `user_id` and `persist_memory: true` in request headers * Test with Gemini or DeepSeek models unavailable on OpenRouter * Monitor cost savings via IntelliVerse-X dashboard (typically 40-60% reduction) No code rewrites required; the API is OpenAI-compatible. Is IntelliVerse-X cheaper than OpenRouter for small projects? Yes. At $0.24/M tokens vs. OpenRouter's $0.35-$0.65/M tokens, IntelliVerse-X saves money immediately. For projects under 1M tokens monthly, the difference is modest (~$100/year), but built-in memory eliminates external database costs, offsetting the price gap. Can I use IntelliVerse-X for production games? Absolutely. IntelliVerse-X powers production titles from indie studios to enterprise game publishers. US-based infrastructure, 99.9% uptime SLA, and compliance-ready governance make it suitable for monetized games and apps. What happens to my data if I switch from OpenRouter to IntelliVerse-X? Your conversation history remains on OpenRouter's servers unless you export it. IntelliVerse-X stores only active session data and user memory (encrypted at rest). No data transfer occurs; you control what you migrate. IntelliVerse-X AI Gateway is purpose-built for game developers, startup founders, and product teams adding AI to their apps. Access Claude, GPT-4, Gemini, DeepSeek, Qwen, and multi-modal models with one API key - plus built-in memory, RAG, and knowledge bases. Get your API key today: - Start free: Chat from $0.24/M tokens at intelli-verse-x.ai/gateway - Book a free 30-minute consultation: intelli-verse-x.ai/book-call Its team in San Francisco, Austin, and New York is ready to help you build the next generation of AI-powered games and apps.
The startup based in New York was most recently valued at $1.3 billion, but could fetch around $10 billion in a sale.