I compared eight Convex alternatives. I'm not switching

I compared eight Convex alternatives in July 2026, and none gave my AI agents a simpler backend to change. Appwrite is the strongest established rival and InsForge is the most interesting new one, but Convex still wins because its schema, functions, jobs, storage logic, and auth configuration live together as TypeScript.

How I ranked them: fewer backend systems beat lower prices

I ranked the platforms by how many separate systems an agent must understand to finish one backend change. That criterion mattered more than price because five of the nine cheapest paid plans cost between $25 and $30 a month. Convex came first, InsForge second, and Appwrite third.

The deciding factor was not how many features each platform offered. It was whether an agent could see and change a complete backend feature without crossing several configuration systems.

Why does Convex work so well with AI coding?

Convex feels easier because it keeps the entire backend as code in one folder. The schema, queries, mutations, scheduled jobs, file storage, vector indexes, and auth configuration all sit next to each other as TypeScript files. An AI agent can therefore change a feature in the same way it changes any other module, without a separate dashboard step.

convex/
  schema.ts       # tables, indexes, vector indexes
  users.ts        # queries and mutations
  billing.ts      # actions calling Stripe
  crons.ts        # scheduled jobs
  http.ts         # webhook endpoints
  auth.config.ts  # auth providers

Those are the actual files an agent needs to understand before it can ship a feature, and they fit on one screen. In my projects, a rate-limited invite feature usually touches three files in that folder. If the model defines the data incorrectly, the generated types report an error. There is also no separate dashboard change to forget.

Convex also gives agents direct access to the backend through official tools. The Convex Developer Hub’s Claude Code guide installs the plugin with /plugin install convex@claude-plugins-official; the plugin can then read development data and logs, run functions, and keep generated types current [1]. Its Codex guide provides similar access through MCP and adds convex-expert and convex-reviewer subagents [2]. My agent can inspect the real backend instead of guessing how it works.

Vector search follows the same approach: I declare a vectorIndex in the schema, set its dimensions to 1536 for OpenAI embeddings, and query it from server code [3]. There is no second database or separate sync process to manage. People call this way of building vibe coding, but the label matters less than the practical benefit. Fewer moving parts give both the agent and me fewer things to forget.

Why I chose Convex over Supabase for AI coding

Supabase lost me because one backend change often crossed SQL migrations, row-level security policies, Edge Functions, storage rules, and generated types. I value its Postgres ecosystem, but in my production work those separate parts gave AI agents more chances to leave a change half-finished.

Supabase’s migration guide documents the required discipline clearly. Its main rule is to “never change the remote database directly,” so every schema change becomes a timestamped SQL migration that is tested locally, committed, and then pushed. A dashboard edit can put the local and remote versions out of sync [4].

That is a sound engineering process, but it gives an AI agent more places to make a partial change. In my projects, agents regularly updated four parts and forgot the fifth. Convex removes that particular failure because the fifth place does not exist.

Convex schema changes can still require a backfill, but the code for that work stays beside the rest of the backend instead of entering a parallel chain of files and tools.

Why the other Convex alternatives fell short

The remaining alternatives each miss a different part of the workflow I want. Appwrite is capable but spreads the backend across resources, InstantDB needs another platform for server logic, and Cloudflare requires me to assemble the backend myself. Firebase, Nhost, and Encore.ts introduce similar tradeoffs.

Appwrite is the strongest established alternative

Appwrite is the strongest established all-in-one alternative, especially if authentication scale matters more than keeping everything in one codebase. Its $25 monthly plan covers the whole organization and includes 200,000 monthly active users, 150 GB of storage, 2 TB of bandwidth, 3.5 million function executions, and daily backups [5].

Appwrite also provides agent skills for nine SDKs in Claude Code, Codex, and Cursor [6], while its MCP servers can manage databases, users, and files directly [7]. Its support for agent work is substantial. If Cursor is your editor, I have written about why its Agents Window can open first, and how to get the IDE back.

I still ranked Appwrite third because its backend is a collection of resources such as buckets, permissions, and schedules, rather than one codebase I can read. Vector search adds another boundary: Appwrite’s own guide connects a Function to OpenAI for embeddings and uses an external Pinecone index for search [8]. That extra service is exactly what I was trying to avoid.

InstantDB needs a second platform for server logic

InstantDB has the workflow that feels closest to Convex, but it covers only part of the backend I need. Its schema and permissions are code, and agents can use an MCP server to create applications and push changes to both. InstantDB also provides instruction files for Claude Code and Codex [9].

The free tier includes 1 GB, unlimited API requests, and no automatic pausing, while the $30 Pro plan raises storage to 10 GB and adds ten teammates and daily backups [10]. Those terms are attractive, but they do not solve the missing server-side features.

The problem is that InstantDB is a sync engine with auth and storage, not a place for server logic. Its documentation covers presence, cursors, permissions, and an admin HTTP API, but not hosted functions, cron jobs, or vector search [11]. I would still need a second platform for the server-side half of the product, so it does not meet my requirements.

Cloudflare is cheap, but it is not one backend

Cloudflare has the lowest starting price, but reaching feature parity requires several separate products. Its $5 monthly plan includes 10 million requests and 30 million CPU milliseconds [12], while Vectorize provides a vector database that connects directly to Workers [13].

To build a full backend, I must combine Workers, D1, R2, Queues, and Vectorize, each with its own configuration, then provide consumer authentication separately. The EU data setup needs care as well: D1 can keep a database in an EU jurisdiction, but Workers may still access it from anywhere because the location restriction applies to the database, not the compute [14].

Firebase remains a set of separate products

Firebase remains competitive, with free authentication for up to 50,000 monthly active users before Google Cloud Identity Platform pricing applies [15]. It is still a group of Google Cloud products with separate consoles and pricing, so it does not meet my requirement for one backend codebase.

Nhost brings back a multi-part migration process

Nhost combines Postgres, GraphQL, auth, and storage from $25, including $15 in compute credits and unlimited users [16]. A schema change produces both a SQL migration and Hasura metadata in YAML, which Nhost’s local development guide says to commit and deploy together [17]. That process resembles the Supabase workflow that caused problems in my projects.

Encore.ts starts at $148 before the cloud bill

Encore.ts solves a different problem: I declare infrastructure in TypeScript, and Encore provisions it in my own AWS or GCP account. Production starts at $49 per member plus $99 per cloud environment, followed by $2.50 per resource and the separate AWS bill [18]. The minimum platform fee is therefore $148 a month.

That can make sense for a team that already manages a cloud account, and Encore’s support for AI development is useful. It does not fit my way of shipping small products quickly.

InsForge is the Convex alternative I’m watching

InsForge comes closest to the workflow I want because it is the only alternative on the list built specifically for AI agents. Its product direction fits my work, but its age and funding keep me from moving customer data there today.

When version 2.0 launched on March 9, 2026, InsForge described itself as a backend for agentic development and said agents already performed 99% of operations on the platform [19]. OpenAI approaches agent infrastructure from the other side, and I go through what the Agents API hosts for you and which costs remain yours.

The product backs that claim with Postgres and pgvector, auth, storage, edge functions, realtime, and an OpenAI-compatible gateway for models from several providers [19]. It supports cron-triggered functions through pg_cron [20], and its Apache 2.0 code can be self-hosted with Docker [21]. The $25 Pro plan includes 100,000 monthly active users, an 8 GB database, and $10 in compute credit, with small always-on instances starting at $5 [22].

I ranked InsForge second rather than first partly because Postgres brings migrations and row-level security back into the system, even when agents manage them. My larger concern is the company’s age. Crunchbase lists a $1.5 million pre-seed round from MindWorks Capital and Baidu Ventures [23], while MindWorks dates the company to 2025 [24].

Convex is much further along: it raised $24 million in November 2025 in a round led by a16z and co-led by Spark Capital [25], then reported nearly 10,000 paying teams in April 2026 [26].

Convex, latest round

$24M

led by a16z, November 2025

paying teams on Convex

10,000

company figure, "nearly 10,000", April 2026

InsForge, pre-seed

$1.5M

MindWorks Capital and Baidu Ventures, 2025

The maturity gap is why I would test InsForge but would not move customer data to it yet. Its Apache 2.0 license and self-hosting option make a later trial less risky.

What does Convex cost in the EU? 30% more, with less included

A paid Convex EU deployment costs 30% more than its US equivalent and does not receive the included usage from US paid plans. Convex’s main pricing page includes 25 million function calls with Pro on US deployments [27], while its EU region post says paid EU resources cost 30% more and are billed on demand [28].

The EU region runs in AWS eu-west-1 in Ireland and is available on every plan, including free. For a European product with significant traffic, the price difference belongs in the cost model before choosing the region.

The EU premium is not my only concern. Free and Starter deployments use the S16 class, with 16 concurrent queries and 16 concurrent mutations, while Professional moves to S256 [29]. Traffic alone can therefore force the $25-per-developer upgrade [27].

Convex Auth is another limitation. It remains in beta, and the documentation says that “it isn’t complete and may change in backward-incompatible ways.” For production auth, Convex recommends Clerk, Auth0, or WorkOS, with WorkOS free for up to one million users [30].

The simple one-folder backend also creates lock-in. My backend uses Convex queries and mutations against a document model, not SQL that I can connect directly to a common reporting tool.

Despite those concerns, the entry price is good. The free Starter tier supports production applications for up to six developers and includes one million function calls on US deployments [27]. I keep accepting Convex’s costs because every alternative I found asks for more development time, and that time costs me more than the platform bill.

Which Convex alternative should you choose?

For an AI-assisted product that needs to ship quickly, I would choose Convex. I would test Appwrite if authentication for hundreds of thousands of users became the main constraint, because its flat $25 organization price becomes more attractive than Convex’s per-developer bill as a team grows [5].

I would review InsForge again in mid-2027. Another funding round and named production customers could change my answer, while its self-hosting license already makes a future test less risky.

The practical test is simple: open the repository after a week away and see whether one folder tells you where the backend logic lives. If it does, an AI agent is more likely to make a complete change without missing a separate configuration step. For everything outside the backend folder, I rely on a four-layer map that points my coding agents at the current facts.

The last piece I wrote examined how much authority to give an AI model. Choosing a backend raises a related question about how many separate parts that model must understand. Convex remains my choice because it keeps that number low.