Mesh API

one api key. every ai model. pay in rupees. deploy from india. by the @aifiesta team.

[Mesh API @meshapi_ai]

day 49 of building @meshapi_ai

we're focused on helping AI-native teams build systems that are fast, scalable, and reliable in production.

big piece of that is role-based access control and org management - almost there

#RBAC is scheduled to be released in the coming week

[Mesh API @meshapi_ai]

@alan__rice Not on mesh api

[Mesh API @meshapi_ai]

years of slow progress, then gpt-3 hits and the chart just goes vertical.

and these are only a few frontier models. genuinely no idea what 5 years from now looks like.

day 48 of building @meshapi_ai

[Mesh API @meshapi_ai]

Implement RAG with 300+ models - https://t.co/aHTHCFkv64

[Mesh API @meshapi_ai]

day 47 of building @meshapi_ai
How Retrieval-Augmented Generation (#RAG) works in #Mesh API

[Mesh API @meshapi_ai]

day 46 of building @meshapi_ai

Suddenly Qdrant failed and the entire RAG went haywire. #RAG is fixed now.

As asked by our customers - we added a user-set timeout option, cleaned up the supported-models page for OpenAI and Vertex.

Now working on auto-recharge and updating API docs.

[Mesh API @meshapi_ai]

@murthyug comparing across different benchmarks and running our own custom tasks to figure out published vs actual performance

[Mesh API @meshapi_ai]

day 45 of building @meshapi_ai

keeping up with which model to actually use has quietly become a full-time job.

opus 4.8, gpt-5.5, deepseek v4, kimi, glm… the "best" one changes almost every week.

so we're building live rankings into Mesh - what's worth using right now, by price + performance + task.

sneak peek 👇 (DM if you want to contribute)

[Mesh API @meshapi_ai]

day 44 of building @meshapi_ai

we just shipped a new #RAG parser, plus the option to bring your own

we realized that most RAG failures don't start at the model. they start at parsing.

a PDF is a positioned text on a page - if you flatten it directly, tables turn to word salad, columns get read across instead of down. then you chunk that mess, embed it, and wonder why retrieval pulls nonsense.

a layout-aware parser reads structure first. tables stay tables, reading order survives, the chunks actually mean something.

[Mesh API @meshapi_ai]

try here: https://t.co/U7Qk6FwBeF

[Mesh API @meshapi_ai]

day 43 of building @meshapi_ai

Masssiiivvveee upgrade to our CLI - it plans now

give #Mesh a task and:
- it figures out the steps itself,
- tracks them as a live checklist,
- reads and writes files,
- runs shell commands, and
- spins up dev servers in the background

working across many steps without losing the thread.

the part no other agent CLI does: it runs on any model on Mesh - Claude, GPT, Gemini, Llama, 300+ in all. pick one, switch mid-run, or let Mesh route every call (cheapest / fastest / balanced).

stop picking a model and praying. just let Mesh route intelligently.

[Mesh API @meshapi_ai]

day 42 of building @meshapi_ai

today we started working with a new #D2C brand. not a typical LLM gateway customer profile, which is what makes it interesting.

enterprise B2B is where most gateways focus. consumer brands are running real LLM workloads now and nobody's serving them well.

rest of the day was bug fixes, cleanup, and research into lowering latency. most days look like this when you're shipping something real.

[Mesh API @meshapi_ai]

day 41 of building @meshapi_ai

Realtime and RAG are both live on prod. all reported bugs cleared.

structured outputs enabled. docs updated for both.

also started experimenting with Google Ads today. first time paying for traffic. curious how it plays.

started customizing for our biggest enterprise client yet. more soon

[Mesh API @meshapi_ai]

day 40 of building @meshapi_ai

- Realtime API fixed and shipping to prod.
- RAG has one bug left, DOC extraction is breaking.
- also found API key cap validation edge cases.
- 3 UX gaps caught during testing, all going on the fix list before launch.

[Mesh API @meshapi_ai]

day 39 of building @meshari_ai

a friend downloaded a popular agent template last week, 50k+ stars on @github - whole setup crashed within an hour.

after going through 100s of repos and building dozens of projects in the last 30 days, I realized - most viral templates and blueprints shared on @X just don't work.

thinking of putting together a place where we test these and rate them transparently - with proper documentation.

would this be useful and would you contribute?

[Mesh API @meshapi_ai]

• The seven missing categories (incl. multilingual non-Latin scripts + hyper-complex boss-fight prompts)
• Stretch lineup: Gemini 2.5 Flash Image ("Nano Banana"), Imagen 3, GPT-Image-1
• Third Gemini-vision judge for own-provider-bias triangulation
• Automatic metrics: CLIPScore + VQAScore + LAION aesthetic
• 50-prompt human calibration set Repo + raw CSVs + paper: https://t.co/Aj1H44urv1

If you run it on your own prompts and get different numbers, reply or drop an email to [email protected]

[Mesh API @meshapi_ai]

day 38 of building @meshapi_ai

#Mesh Dollar per Image Bench

A holistic cost, quality, latency pilot benchmark of five 2026 frontier text-to-image models, all routed through one API gateway (https://t.co/l5eGKQbvm9).

n=5/model fully reproducible.

paper: https://t.co/Aj1H44urv1

[Mesh API @meshapi_ai]

Pilot caveats, hold us to all of them:

• n=5/model
• 5 of 12 prompt categories sampled (long-prompt, knowledge, counting, neg-space, edge-case, multilingual, hyper-complex deferred to full run)
• Single region, single time, single seed
• 2-judge ensemble, no human eval yet
• Mesh only routes OpenAI + Google for image; FLUX/Midjourney/Ideogram absent