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12 specialist agents · self-healing deploys · reliability published

Specialist AI agents
for every layer of your stack

A narrow expert agent for every technology deploys to your own cloud, verifies health from the outside — live endpoint, logs, metrics — and self-heals with a bounded repair ladder. Every deploy ends verified-healthy or honestly failed, and we publish our pass rates.

Join the private beta See how it works
This site and our backend are deployed by HiveDeploy itself
hivedeploy · session: deploy-prod-postgres-cluster
postgres-agent ▸
Gate 4: Sizing — based on your declared 8K req/s read traffic, I recommend db.r6g.xlarge × 1 primary + 2 sync standbys.
Citation: memory/postgres/sizing.md#read-heavy — ~1500 RPS per vCPU at typical OLTP query mix.
approval ▸ request_approval(step=4, title="Infra & sizing", summary="db.r6g.xlarge × 3, multi-AZ, ~$612/mo")
postgres-agent ▸
Estimated cost: $612/mo on AWS RDS · approval pending →
Multi-cloud · cross-account IAM · generates IaC, never applies silently · verified-healthy or honestly failed
AWS Google Cloud Microsoft Azure Cloudflare Deno Deploy Kubernetes
12
Live specialist agents
95.7%
Converged pass rate, Java × GCP — published with evidence →
6
Approval gates per deploy
5×
Cloud providers

Dogfood

We ship HiveDeploy with HiveDeploy.

Both this marketing site and our backend AI orchestrator were deployed by our own specialist agents — across the same multi-cloud setup we sell to you. If we wouldn't trust HiveDeploy for our own production, we wouldn't ship it to you.

🌐 Live www.hivedeploy.in

This marketing site

Static landing page deployed by the Frontend specialist agent. Cross-account IAM, auto-renewing TLS, global CDN.

▸ agent frontend
▸ target CDN + Object storage
▸ gates 6/6 ✓
🐍 Live api.hivedeploy.in

Backend AI orchestrator

The Python FastAPI service powering every conversation you have with our agents — itself shipped by the Python specialist agent.

▸ agent python (FastAPI)
▸ target Fargate + ALB
▸ gates 6/6 ✓
🐘 Live db.internal

Production Postgres

Our session store + audit log. Tuned, monitored, and indexed by the Postgres specialist agent — with citations from its own memory pack.

▸ agent postgres
▸ target CloudNativePG / EKS
▸ gates 6/6 ✓
Every deploy you see from us was reviewed at 6 gates, signed by an operator, and applied via Terraform our own agents emitted. Same flow your team gets on day one. No "do as we say, not as we do."

Proof

We publish our pass rates.
Raw number first, every time.

Every claim below comes from volume campaigns of real cloud deployments, with every failure root-caused and published as an evidence dossier. When a run fails, we fix the failure class platform-wide and re-run the exact failed slots — and we publish both numbers.

64.1%
Raw discovery pass

41/64 first-attempt runs across 8 deterministic + generative topologies. The honest starting point — published, not buried.

95.7%
Converged after class fixes

22/23 failed slots recovered on the strict gate after each failure class was fixed platform-wide and re-run.

100%
Deploy-health, final ledger

23/23 recovered once the last class — a Cloud Run probe-contract defect our re-runs exposed — was fixed and verified green.

Second agent, second cloud — Python agent × AWS · closed 19 Sep 2026
complete · 76/76 first attempt
76/76
raw pass on first attempt — 64 reliability + 12 coverage runs. No re-runs needed, so raw and converged are the same number. 0 deploy failures, 76 clean teardowns.
52/52
on the strict health + logs + metrics gate. The other 24 rows are learning-curve evaluations on a reachability-only gate, by design.
16
runs that drifted from a generative ask onto a template — healthy deploys that don't test what they were written to test. Published per row, and fixed in the router after the campaign closed.
6
defects only running it found — 3 in pre-campaign canaries, 3 mid-campaign (incl. a provider-credit halt and a 2.5 h worker wedge, both off-ledger and disclosed).

Why it went 76/76 where Java × GCP opened at 64.1% and needed a fix-then-rerun pass: every infrastructure- and deployment-level failure class the Java agent hit on AWS was ported into the Python agent before this campaign launched. That is the flywheel working — and it is exactly why this number ships with its caveats attached, not instead of them. Coverage included four production topologies this agent had never deployed on AWS: hardened EKS, multi-service Fargate, an auto-scaling group behind an HTTPS load balancer, and multi-AZ HA EKS.

23/23 failures dossier'd gate versions labeled on every row costs included Python × AWS: 76/76 first attempt, published Java × AWS in flight
Read the reliability ledger

Numbers cover the published envelope (Java agent × GCP and Python agent × AWS — both final, with gate mode and route labeled on every row). We don't do unqualified reliability claims — each number links to its evidence.

Why HiveDeploy

Other platforms ship a generic "deploy" button.
We ship an expert per technology.

Qovery, Porter, Flightcontrol all wrap a generic compute primitive. We treat each technology as a domain that deserves its own lifecycle owner — deploy, monitor, scale, tune, troubleshoot.

Specialist per technology

Postgres agent has tools like propose_create_index and propose_optimize_table. Kafka agent rebalances partitions. Go agent picks Gin vs Echo vs Fiber based on your workload class.

postgres kafka go java +8 more

Memory-grounded, with citations

Every recommendation cites the memory pack it came from — "use NLB not ALB for gRPC because HTTP/2 trailers" links to memory/go/grpc_deployment.md. No black-box advice.

cite ▸ memory/postgres/sizing.md#read-heavy

Generates IaC, never applies silently

Agents emit Dockerfile, Terraform, K8s manifests, GH Actions workflows into a spec. A separate orchestrator applies them. Every change is reviewable, signable, replayable. Compliance-first by default.

Dockerfile main.tf deploy.yml k8s.yaml

Self-heals — within honest bounds

Failures hit a repair ladder: deterministic fixers first (capacity rotation, name collisions, JVM memory hardening — applied in milliseconds, no AI call), then AI repair with anti-repeat steering, then redeploy. Bounded at 5 attempts: no infinite loops burning your money. The platform even distills new fixers from its own repair history.

Gets more deterministic with every deploy

Ask for a topology outside the catalog and the agent generates it. Every verified generative success is ratified into the deterministic template library — the next identical ask routes through the frozen template: faster, cheaper, variance-free. Mistakes become lessons that steer future generation. Generative once, deterministic forever.

How it works

Six explicit gates.
Every deploy is replayable + signed.

No "agent did something" black box. Each deploy walks 6 named approval gates. Every gate is a structured artifact in your audit log — replayable from end to end.

1

Workload class

HTTP API · Worker · Batch · gRPC · Realtime · Serverless. Picks the right specialist agent.

2

Runtime & framework

Go 1.25 · Gin vs Echo vs Fiber · CGO mode · build flags. Cited from memory.

3

Datasources

Postgres · Redis · Kafka. Hands off to the relevant specialist agent for sizing.

4

Infra target & sizing

Fargate · Lambda · Cloud Run · GKE · AKS. With pre-deploy cost estimate.

5

CI/CD & security

GitHub Actions · govulncheck · Jit.io · race-detector tests · OPA policy.

6

IAM & final deploy

Least-priv IAM policy · Terraform diff preview · cross-account assume-role.

After gate 6 terraform apply → health verification (endpoint · logs · metrics — probed from outside) → self-heal if needed (≤5 bounded attempts) → verified healthy or honestly failed, with the evidence

Load-balancer error pages never count as "up". A deploy that can't prove it's serving is a failed deploy — there is no "applied cleanly, hope it works" state.

The catalog

12 specialist agents live.
150+ in the roadmap.

Every agent has its own memory pack with deep technical knowledge — sizing rules, cross-cloud equivalents, framework gotchas, IaC templates.

🐘
Live
Postgres
9 templates · CNPG · RDS · Aurora
🦅
Live
Kafka
7 templates · Strimzi · MSK · Confluent
🐹
Live
Go
12 templates · Gin · Echo · Fiber · gRPC
☕
Live
Java
12 templates · Spring · Quarkus · Micronaut
⬢
Live
Node.js
13 templates · Express · Fastify · NestJS · Bun
🐍
Live
Python
7 templates · FastAPI · Django · Flask
⚡
Live
Redis
Caching · Pub/Sub · Cluster
🍃
Live
MongoDB
8 templates · Percona · Atlas · Self-managed
Coming soon Already planned across 28 categories — vector DBs, observability, mesh, IaC, security, edge, and more.
Pinecone Snowflake Datadog Istio PagerDuty Snyk Cloudflare Kong Terraform Pulumi Helm DynamoDB Cassandra Stripe Twilio Auth0 Temporal + 130 more

Where we sit

Compared to other deployment platforms

We're the only platform that combines specialist agents per technology with multi-cloud breadth and audit-first IaC delivery.

Capability HiveDeploy Qovery Porter Spacelift Massdriver Bunnyshell
Specialist agent per technology ● ○ ○ ○ ○ ○
Memory-grounded with citations ● ○ ○ ○ ○ ○
6 explicit approval gates ● ◐ ○ ◐ ◐ ○
Stateful workload depth (DB, streaming) ● ◐ ◐ ○ ◐ ◐
Multi-cloud (AWS + GCP + Azure) ● ● ● ● ● ●
Generates IaC, doesn't apply silently ● ○ ○ ○ ◐ ○
Cross-account IAM (external ID) ● ◐ ◐ ◐ ◐ ◐
Conversational + IaC + diagram (tri-modal) ● ○ ○ ◐ ◐ ○
Self-healing deploys (bounded auto-repair) ● ○ ○ ○ ○ ○
Publishes reliability rates with evidence ● ○ ○ ○ ○ ○
● Yes  ·  ◐ Partial  ·  ○ No

Pricing

Predictable platform fee.
Pay only for the AI work you actually use.

Bring your own cloud — your compute is yours, never ours. Each plan includes a monthly platform fee plus a credit pool for AI calls. Beyond the pool, usage flows at a transparent markup with a configurable cap so bills can't surprise you.

How it works: Platform fee + included AI credits + 1.4× markup on overage. Set an overage cap and your agents auto-pause if they hit it — no surprise invoice. Annual billing saves ~15%.

Free

For evaluation

$0 /month

$5 AI credits included

Get started
  • ✓Up to 2 seats
  • ✓Up to 1 active agent
  • ✓1 cloud account
  • ✓All 12 live specialist agents
  • ✓Community support
  • ✓Audit log (30-day retention)
Most popular

Team

For growing teams

$99 /month

$70 AI credits/mo · $1,009.80/yr annual

Start free trial
  • ✓Up to 5 seats
  • ✓Up to 3 active agents
  • ✓Unlimited cloud accounts
  • ✓All current + future agents
  • ✓Cross-account IAM (external ID)
  • ✓Configurable overage cap
  • ✓Email support
  • ·1.4× markup on overage AI use

Business

For scaled orgs

$499 /month

$560 AI credits/mo · $5,089.80/yr annual

Start free trial
  • ✓Everything in Team, plus:
  • ✓Up to 25 seats
  • ✓Up to 15 active agents
  • ✓Priority support
  • ✓Audit log (1-year retention)
  • ✓Spend alerts (50/80/100%)
  • ·1.4× markup on overage AI use

Enterprise

For regulated + air-gapped

Custom

Annual contract · 1.3× overage

Talk to sales
  • ✓Everything in Business, plus:
  • ✓Unlimited seats + agents
  • ✓SAML / SSO / SCIM
  • ✓Self-hosted / air-gapped
  • ✓Cryptographically-signed gates
  • ✓SOC2 / HIPAA evidence pack
  • ✓SLA + dedicated CSM
  • ✓Custom agent development
What counts as an "active agent"?
An agent that ran at least one deployment in the billing month. Idle agents don't count.
What's "AI credits"?
Frontier-model API usage at our raw cost — no markup inside the included pool. The dollar amount in each plan is what we pre-pay our model provider for your team.
Can I cap overage?
Yes — set a hard cap in dollars. We auto-pause agents at 100% and notify at 50/80%.

FAQ

Frequently asked questions

How is HiveDeploy different from Qovery, Porter, or Spacelift? +
Qovery and Porter are generic compute providers — same "deploy" button regardless of stack. Spacelift orchestrates IaC but has no runtime intelligence. HiveDeploy ships a specialist agent per technology (Postgres tunes Postgres, Kafka rebalances itself, Go picks Gin vs Echo) with deep memory packs for each. Plus we generate IaC and let your orchestrator apply it — never silent applies.
Do my workloads run on HiveDeploy infrastructure? +
No — your workloads stay in your own AWS / GCP / Azure / Cloudflare account. We use cross-account IAM with external ID so you're never sharing credentials. HiveDeploy is the orchestrator + intelligence layer; the compute and data are yours.
What "specialist agents" are live today? +

Live: Postgres, MySQL, MongoDB, Redis, Elasticsearch, ClickHouse, Kafka, Python, Java, Go, Node.js, Frontend (Next/React/Angular/Vue) — 12 agents.

Coming soon: 150+ across 28 categories — vector DBs, observability, service mesh, IaC, security, edge, mobile, payments, and more.

Why "generate but don't apply" IaC? +
Compliance teams can't approve agents that mutate infrastructure silently. We emit Dockerfile, Terraform, K8s manifests, and CI workflows into a structured spec. Your orchestrator (Spacelift, ArgoCD, GitHub Actions, in-house) applies them — preserving your existing review and audit pipeline.
What happens when a deployment fails? +
It hits a bounded self-healing ladder: deterministic fixers for known failure classes first (millisecond fixes, no AI call), then AI repair that reads the actual cloud evidence, then redeploy — at most 5 attempts, every one visible in your run log. If the ladder can't converge, you get the final blocking finding and the preserved Terraform, not a shrug. Some failures are correctly yours (a quota on your account, an app with no health route) — those surface immediately with the exact action to take.
How reliable is it, really? +
We don't answer that with adjectives — we publish the numbers at proof.hivedeploy.in: raw first-attempt rates, converged rates after class fixes, every failure root-caused with a public dossier, and costs. Each number is labeled with its exact envelope (agent × cloud × topologies × gate version). If a number isn't on the ledger, we haven't earned it yet.
Is there a self-hosted / air-gapped option? +
Yes — Enterprise plan includes self-hosted and air-gapped deployments for FedRAMP, finance, and healthcare customers. Talk to sales.
When will the private beta open? +
We're onboarding design partners through Q2 2026. Join the waitlist below — we'll reach out with an invite when your stack matches our live agents.

Ship Postgres like a Postgres expert.
Kafka like a Kafka expert.
Without hiring one of each.

Join the private beta. We'll reach out when your stack matches our live agents.

No spam. Unsubscribe anytime.