Jacey

Founder @
— one API, smarter multi-model routing at better prices. I share benchmarks, pricing radar & routing wins. Building in public.

[Jacey @Jaceyai]

Seedance 2.0 hype is real.

If you’re a dev/studio and want to build with the API today (pricing + quick start), here’s the fast lane: 👉 https://t.co/FcN8XAcLbO
Ship something this week. Don’t just watch the wave—ride it.

[Jacey @Jaceyai]

Every AI startup is building an "API gateway" now.

But the real moat is not routing — it is reliability monitoring, cost optimization, and fallback logic that just works.

The infra layer for AI apps is still massively underbuilt.

[Jacey @Jaceyai]

Hot take: Claude Code system prompt leaking is actually good for the ecosystem.

When devs can see how tool-use prompts are structured, everyone builds better agents.

Transparency > secrecy in developer tooling.

[Jacey @Jaceyai]

Google dropping Veo 3.1 Lite on the API right after Sora shutdown is not a coincidence.

The video gen API market is wide open and every major player knows it.

Whoever nails the developer experience (not just model quality) wins this round.

[Jacey @Jaceyai]

This is so true. The gap between demo and production is 90% infrastructure — rate limits, failovers, cost tracking, provider outages.

Building the AI feature is the easy part. Making it reliable at scale? That's where most teams burn months.

This is the exact problem space we focus on at EvoLink — handling the messy infra layer so devs can ship faster.

[Jacey @Jaceyai]

Honestly, anyone building in the video gen space saw this risk. OpenAI's execution on Sora was rushed — launched with quality issues, pricing confusion, and no clear API strategy.

Meanwhile, Kling, MiniMax, and others have been quietly shipping stable APIs that developers can actually build on.

The lesson: reliability and API-first design > hype.

[Jacey @Jaceyai]

This pricing structure is interesting — the gap between Standard and Fast tiers shows exactly what I keep saying: in AI APIs, you're not just paying for intelligence, you're paying for latency.

For most batch workloads, the Standard tier is a no-brainer. Save the Fast tier for real-time user-facing features.

Smart cost optimization starts with understanding which calls actually need speed.

[Jacey @Jaceyai]

Love this concept. API cost visibility is one of the most underrated problems in AI development.

Most teams have no idea what they're actually spending per task until the bill hits. Auto-accounting + budget guardrails should be table stakes for any production AI system.

Bookmarking this — aligns with how we think about cost-awareness at the API routing layer.

[Jacey @Jaceyai]

Smart move by Vercel. Video gen APIs are where LLM APIs were in early 2024 — fragmented, expensive, and hard to manage.

The abstraction layer play is going to be huge here. Different models excel at different styles, and costs vary 10x+ between providers.

We're seeing the same pattern at EvoLink — routing video gen requests to the best provider based on style, speed, and budget.

[Jacey @Jaceyai]

This is exactly why depending on a single AI provider is a liability, not a strategy.

Companies that built their entire video pipeline on Sora now have zero fallback. No API, no app, no migration path.

The lesson: abstract your AI dependencies. Use a routing layer. Have provider diversity built into your architecture from day one.

[Jacey @Jaceyai]

Vercel adding video generation to their AI Gateway is a signal.

Video gen APIs are following the exact same path as LLM APIs did 18 months ago:

1. Fragmented providers
2. Wildly different pricing
3. No standardized interface
4. Developers duct-taping integrations

The companies that solve the routing and abstraction layer for video/image gen will capture massive value.

We're building this at EvoLink → https://t.co/gMf1MMmLZK

[Jacey @Jaceyai]

Saturday thought:

The hardest part of building an AI API platform isn't the tech. It's deciding what NOT to build.

Every week there's a new model, a new modality, a new provider. The temptation is to support everything.

But your users don't need 200 models. They need the right 20, routed intelligently.

Constraint is a feature.

[Jacey @Jaceyai]

OpenAI shutting down Sora entirely — app, API, Disney deal — is a wake-up call.

Video generation is not a winner-take-all market. It's a fragmented, fast-moving space where yesterday's leader can exit overnight.

If your product depends on a single video gen provider, you're one announcement away from a scramble.

This is exactly why multi-provider routing matters. Not just for cost — for survival.

[Jacey @Jaceyai]

On-device + cloud hybrid is underrated. The latency win alone justifies it for many use cases. The tricky part is deciding the routing boundary — which requests stay local vs go to cloud. Context length and task complexity are the key signals we've found work best for automatic routing.

[Jacey @Jaceyai]

This is exactly what smart routing looks like in practice. Use the expensive model where it matters (planning, complex reasoning) and the cheaper one for execution. We've been preaching this multi-tier approach since day one. Most teams overspend 3-5x by using one model for everything.

[Jacey @Jaceyai]

Interesting theory but the tokenmaxxing behavior predates any single vendor push. It's a classic enterprise pattern — when a new category emerges, companies over-index on adoption metrics before they understand ROI. Same thing happened with cloud computing spend 10 years ago. The correction always comes.

[Jacey @Jaceyai]

This is a real pain point. Per-image pricing should be simple but providers love making it complex so you can't comparison shop easily. We built our routing layer partly because normalizing pricing across providers was impossible without a translation layer. Devs deserve transparent, predictable costs.

[Jacey @Jaceyai]

GPT-5.4 mini using only 30% of the quota is a smart move. For most production workloads, the mini is more than enough. The real unlock: dynamically routing between mini and full based on task complexity. We've seen teams cut costs 40-60% this way without any quality regression on benchmarks that matter.

[Jacey @Jaceyai]

@kevinroose The irony: companies tokenmaxxing are proving most tokens are wasted. We see this in routing data — 60-70% of enterprise tokens could be handled by a model 10x cheaper with identical quality. The leaderboard should track cost-per-useful-outcome, not raw consumption.

[Jacey @Jaceyai]

CNBC calling AI models "commodities" today.

As someone who routes millions of API calls across providers — yeah, this has been true for months.

For 80% of use cases, 3-4 models are interchangeable. The differentiation is in:
• Latency
• Reliability
• Cost optimization
• Fallback strategy

The model wars are over. The infrastructure wars are just starting.

[Jacey @Jaceyai]

LLM API pricing in March 2026 is wild:

→ Budget: $0.07/M input tokens (Gemini Flash Lite)
→ Mid-tier: $3-5/M (Claude Sonnet, GPT-5.4)
→ Premium: $15-75/M output (Opus, o3-pro)

Prices dropped 80-90% in 2 years. The real question isn't which model — it's which model FOR WHICH TASK.

Smart routing > picking one model.