Compact fast-mode WAL measured 3,835 KB versus 13,116 KB for Redis AOF on the April 16 storage benchmark.
AhanaFlow by AhanaAI · Controlled Deployment Ready · Branch 33 v1.0
Compressed durable state.
Measured vector retrieval.
One runtime.
AhanaFlow combines UniversalStateServer and VectorStateServerV2 for teams that want compressed durable state and measured vector retrieval in one controlled-deployment runtime. The approved Branch 33 packet stays narrow: repeatable sub-50 ms selective vector p99 on the bounded 10K lane, about 33× maintenance relief on append-only growth, and leader/follower HA packaging for controlled pilots.
Deploy the controlled-deployment baseline in one command — single node or HA pilot.
KV, counters, queues, streams, and integrated vector operations in one deployable package for selected workloads where operational simplicity matters.
47,642 req/s mixed load at 1.884 ms p99 in compact fast-mode. The RESP-compatible lane measured 25,645 req/s at 6.044 ms p99.
Measured selective vector retrieval with transparent p99 reporting on the approved bounded lane. April 9 insert and RAG numbers stay public, but broader mixed-load filtered p99 remains transport-bound.
Authentication, rate limiting, and payload enforcement are built into the runtime, not bolted on later or sold as a premium tier.
Compressed durable WAL
KV · TTL · counters · queues · streams
Exact + HNSW vector operations
API key authentication
Replay-safe crash recovery
RESP-compatible async lane
Self-hosted & edge ready
47.6k req/s mixed load
1.884 ms p99
1.157× Redis pipeline
3.42× smaller WAL
Measured vector p99 reporting
Controlled deployment ready
47.6K
mixed-load req/s
3.42×
smaller WAL vs Redis AOF
1.157×
Redis on compact pipeline
CD
controlled deployment ready
Platform
A single runtime for the data your product depends on every day.
AhanaFlow combines compressed durable state and vector retrieval in a platform built for modern control planes, AI systems, SaaS backends, and embedded deployments.
Keep operational data compact, recoverable, and always within reach.
- Key-value storage with TTL support
- Atomic counters for quotas, metering, and rate control
- Queues and append-only streams for workflow orchestration
- Compressed WAL persistence for recovery and retention
Serve vector search without stitching together another product tier.
- Exact and indexed retrieval modes
- One operational surface for state and search
- Natural fit for RAG memory and semantic lookup
- Simpler local, private, sidecar, and edge deployment paths
Reduce storage pressure without giving up durability.
- Smaller retained state for backups and replication
- Lower bandwidth burden during recovery flows
- More practical embedded persistence
- 3.42× smaller WAL vs Redis AOF on the April 16 compact fast-mode storage benchmark
Deploy with authentication, enforcement, and traffic controls already in place.
- API key authentication with hashed storage
- Rate limiting and payload enforcement
- Connection controls and validated command surfaces
- Security features built into the runtime, not bolted on later
Spend less time gluing systems together and more time shipping product.
- One deployment story for critical state and retrieval
- Fewer moving parts than a fragmented cache-plus-vector stack
- Clear runtime behavior under real benchmark load
- Open repo, reproducible reports, and direct deployment support
Especially strong where retained state matters as much as raw speed.
- AI control planes and agent orchestration
- Usage metering, counters, and per-tenant policy state
- Workflow queues and event streams
- Private or embedded search-backed applications
How It Works
Hybrid mode, compact mode, RESP, and WAL explained in plain English.
Visitors should not have to reverse-engineer our protocol story from benchmark tables. Hybrid mode is the traffic router, compact mode is the faster AhanaFlow-native wire path, RESP is the Redis-readable wire path, and WAL is the on-disk persistence layer they can both write into.
Words + Picture
One listener, two wire paths, one durable state engine.
Read it left to right: the client sends traffic into hybrid mode, hybrid mode decides whether the bytes are compact or RESP, the matching lane handles the request, and successful state changes persist into the WAL. WAL is storage; RESP is protocol.
The listener that makes both client styles possible.
Hybrid mode listens on one server port and inspects the incoming bytes. If the client is speaking RESP, it routes the request into the Redis-readable handler. If the client is speaking compact frames, it routes into the native fast path.
- Best when you want one endpoint for both migration traffic and native traffic
- Lets redis-cli and redis-py talk to the same listener used by native AhanaFlow clients
- Keeps the protocol decision in the server instead of forcing the operator to run two separate ports
The native path for lower overhead and stronger throughput.
Compact mode is not Redis wire protocol. It is our own lighter-weight transport with lower parsing overhead, which is why the April 16 compact fast-mode lane is the one that beats Redis on the official pipelined KV benchmark.
AhanaFlow-native
best current speed lane
not redis-cli wire format
Redis-readable traffic can still land in compressed durable storage.
RESP and WAL solve different problems. RESP is the network format a Redis client speaks. WAL is the on-disk log the server writes for durability. A Redis-shaped request can arrive over RESP and still end up persisted into AhanaFlow's compressed WAL.
RESPwire protocol for clients and toolsWALwrite-ahead log for recovery, replay, and storage efficiencyHybridrouter that decides which protocol handler runsCompetitive Comparison
One runtime for the parts of your stack that benefit from consolidation.
Many AI and SaaS teams end up stitching together Redis, a separate vector system, queues, and persistence glue for selected workloads. AhanaFlow is the tighter option when retained-state cost, operational simplicity, and self-hosted control matter more than chasing every specialized feature across multiple products.
| Capability | 🌺 AhanaFlow | Redis | Pinecone | Qdrant | Weaviate | MongoDB Atlas |
|---|---|---|---|---|---|---|
| KV storage with TTL | ✓ | ✓ | — | — | — | ✓ |
| Atomic counters & rate control | ✓ | ✓ | — | — | — | — |
| Queues & append-only streams | ✓ | ✓ | — | — | — | — |
| Built-in vector retrieval | ✓ | Add-on module | ✓ | ✓ | ✓ | Add-on module |
| State and vector in one runtime | ✓ | Requires modules | — | — | — | Complex setup |
| Compressed WAL storage advantage | ✓ | — | — | — | — | — |
| Self-hosted deployment path | ✓ | ✓ | — | ✓ | ✓ | Limited |
| No per-query billing | ✓ | ✓ | — | ✓ | ✓ | — |
| Auth & rate limiting built-in | ✓ | Partial | Cloud-managed | Partial | Partial | ✓ |
| Embedded & edge friendly | ✓ | — | — | Partial | Partial | — |
| Open repo + reproducible benchmarks | ✓ | ✓ | — | ✓ | ✓ | — |
Performance Deep Dive
The current Redis comparison is real, but it is mode-specific.
The buyer-safe April 16 boundary is narrow and explicit. Compact fast-mode beats managed Redis on the official pipelined KV lane and wins materially on WAL footprint. The RESP-compatible lane remains slower than Redis, and those differences are part of the product story rather than something we hide.
| Operation | 🌺 AhanaFlow | Redis (in-memory) | Winner |
|---|---|---|---|
| Mixed load, compact fast-mode | 47,642.31 req/s @ 1.884 ms p99 | Managed Redis not measured on the same mixed packet | Buyer-safe fast lane |
| KV pipeline, compact fast-mode | 295,183.83 ops/s | 255,109.55 ops/s (managed Redis, no persistence) | AhanaFlow 1.157× |
| KV pipeline, async RESP | 155,096.15 ops/s | 223,117.22 ops/s (managed Redis) | Compatibility lane |
| WAL footprint vs Redis AOF | 3,835 KB | 13,116 KB | AhanaFlow 3.42× smaller |
| RESP lane WAL vs Redis AOF | 3,861 KB | 7,994 KB | AhanaFlow 2.07× smaller |
How To Read The Numbers
The win is strongest on compact fast-mode and storage, not on every protocol path.
The April 16 hotspot report is the canonical public boundary. Compact fast-mode is the performance lead lane for pipelined KV and WAL storage efficiency. The async RESP lane exists to preserve Redis compatibility, not to claim blanket Redis replacement. Vector operations are included, but broad fast-ANN marketing is still intentionally blocked.
State, counters, queues, and compressed persistence with a narrower claim surface.
Redis is still the reference point for mature in-memory data structures. AhanaFlow is the better fit when you value retained-state cost, self-hosted simplicity, and one runtime for operational state plus integrated vector operations.
- 3.42× smaller WAL than Redis AOF on the current compact fast-mode storage benchmark.
- 1.157× Redis on the official compact fast-mode pipelined KV lane.
- Includes KV, TTL, counters, queues, and streams in the same runtime.
- RESP compatibility exists, but that lane is slower today and presented honestly.
- Reduces backup, replication, and retained-state costs materially.
Integrated vector operations when you do not want a separate managed vector bill.
Pinecone is optimized for dedicated vector infrastructure at scale. AhanaFlow is for teams that want retrieval adjacent to operational state inside one deployable runtime.
- AhanaFlow is fully self-hosted — deploy privately, on-prem, or at the edge.
- Covers KV, state, queues, and streams that Pinecone does not target.
- Fits embedded, sidecar, and customer-controlled environments.
- Best where you value operational consolidation over specialist vector scale.
Operational state and retrieval together, with storage efficiency on retained data.
Qdrant is a focused vector engine. AhanaFlow is the broader control-plane runtime when you want vectors, counters, queues, and durable state handled together.
- Eliminates the need to stitch a separate cache or queue service into the same deployment for selected workloads.
- Compressed WAL creates a measurable storage advantage on retained state.
- Rate limiting and auth built into the runtime, not layered on externally.
- Single deployment story for AI control planes and internal product infrastructure.
Purpose-built operational state with integrated retrieval, not retrieval first.
Weaviate is a capable search-oriented platform. AhanaFlow is stronger where counters, queues, TTL state, and replay-safe persistence are part of the core workload, not an afterthought.
- AhanaFlow was designed as a control-plane database from the ground up.
- Atomic counters, TTL-expiring keys, queues, and streams are first-class primitives.
- Storage efficiency is a measured differentiator on retained state.
- Self-hosted with a clean single-runtime deployment story.
The simplicity of a focused runtime versus a general-purpose document platform.
MongoDB Atlas is broader and more feature-rich. AhanaFlow is the narrower choice when your application needs durable operational state, compact persistence, and retrieval without adopting a full document platform footprint.
- AhanaFlow is purpose-built for AI control-plane workloads: lean, durable, and compressed.
- No schema management, no aggregation pipelines, no cluster topology to reason about.
- Measured WAL efficiency favors AhanaFlow in retained-state-heavy workloads.
- Genuinely embedded and edge friendly; Atlas requires separate Atlas Edge configuration.
The Core Advantage
Storage efficiency and consolidation are the core differentiators, with the qualifiers included.
AhanaFlow's strongest public advantages today are mode-qualified Redis pipeline performance, measured WAL storage reduction, and one runtime for control-plane state plus integrated vector operations. Those wins do not erase every tradeoff: the RESP lane is slower than Redis today, and broad fast-ANN claims are intentionally off the page.
Pricing
Simple pricing for teams evaluating one runtime for state, retrieval, and retained-state efficiency.
Start with the self-hosted core. Move to a commercial plan when you want licensing, support, and a faster rollout path. Paid plans route through Stripe-hosted checkout, and existing customers can recover keys through the license portal.
Self-Hosted Core
Open-source runtime for teams running AhanaFlow inside their own infrastructure.
Freeforever
- Unlimited self-hosted deployments
- KV, queues, streams, counters, and vector retrieval
- Compressed WAL and integrity verification
- Best fit for private infra and pilot validation
Pro
For production teams that want managed AhanaFlow capacity without stitching together extra services.
$9.99/ month
- 1 API key issued on this monthly plan
- 10 GB durable WAL footprint
- Email support and deployment help
- Ideal for agent backends, RAG memory, and SaaS metering
Team
For larger internal platforms that need more headroom, more support, and less operational drift.
$19.99/ month
- 3 API keys issued on this monthly plan
- 100 GB WAL footprint
- Priority support and rollout guidance
- Best fit for multi-tenant AI products and heavier control planes
Enterprise
Self-hosted AhanaFlow for customer-controlled environments that need commercial access control and a bounded API-key footprint.
$49.99/ month
- Self-hosted deployment license
- Up to 10 API keys
- Commercial access for customer-controlled environments
- Best fit for teams that need tighter operator control without custom contracting
Continue to secure Stripe checkout
Choose a paid plan above, enter a work email, and AhanaFlow will hand you off to a Stripe-hosted subscription checkout.
Selected plan: Pro. All paid plans use Stripe-hosted checkout.
Existing customers can recover or reissue their API key and license at ahanaflow.com/license-portal.
Use Cases
Built for the workloads where compression and simplicity create the most leverage.
AhanaFlow is strongest where durable operational state, integrated retrieval, and retained-state cost matter more than broad specialist feature depth.
AI Agent Control Planes
Store agent memory, session context, tool-call history, and retrieval indexes in one compressed runtime for selected workloads that benefit from fewer moving parts.
SaaS Metering & Quotas
Atomic counters, per-tenant rate limits, usage watermarks, and billing checkpoints — the exact primitives SaaS backends need, durable and compressed by default.
RAG & Semantic Memory
Store document embeddings alongside their operational metadata in one platform. Retrieve by vector similarity while keeping session state and retrieval history adjacent and consistent.
Event Streams & Workflows
Append-only event logs for audit trails, job queues for async work, and durable workflow checkpoints — all in one platform with guaranteed replay-safe recovery after crashes.
Private & Air-Gapped Deployments
Deploy inside customer environments with no external cloud dependency. Compact WAL makes on-prem and edge deployments operationally viable even on constrained storage hardware.
Embedded Product Backends
Ship a complete data layer with your product instead of asking customers to stand up five services. One binary, one connection, one monitoring surface to manage.
Decision Guide
When to choose AhanaFlow over Redis (and vice versa).
Both platforms excel at different things. Here's an honest comparison to help you choose the right tool for your workload.
When storage footprint and platform simplicity matter
- State + vector in one runtime: You need KV, counters, queues, and integrated vector operations without managing two products
- Storage costs matter: Backups, replication, retention, and egress benefit from 3.42× smaller WAL versus Redis AOF in the current compact fast-mode benchmark
- Embedded/edge deployments: Compressed persistence makes constrained-storage environments viable
- Private/air-gapped installs: Single-node simplicity for customer-controlled environments
- Control-plane workloads: AI agents, SaaS metering, RAG memory, workflow orchestration
- No cluster complexity: Your workload fits on one node with compression (< 1TB hot state)
When ecosystem maturity and specialized features are required
- Pub/Sub messaging: You need broadcast patterns (one message → many subscribers)
- Sorted sets, HyperLogLog, bitmaps: Specialized data structures we don't offer
- Horizontal sharding: You need cluster mode for > 1TB hot state or > 10M ops/s aggregate
- Battle-tested at scale: Decades of production validation across thousands of companies
- Rich module ecosystem: RedisJSON, RedisGraph, RedisTimeSeries, RedisBloom
- Commercial support: Redis Enterprise with 24/7 support and SLA guarantees
Honest Assessment
Different products for different priorities.
Redis is still the gold standard for mature in-memory data structures and ecosystem depth. AhanaFlow is the better choice when your workload fits one node, your retained state is meaningful enough that WAL size matters, and you want integrated vector operations with the qualifiers stated up front. Both are valid choices — the right one depends on your workload, scale, and operational priorities.
Proof
Verified numbers, with the April 16 claim boundary included.
Every benchmark is reproducible. All artifacts are in the repository. The numbers below reflect the official April 16, 2026 hotspot packet, the frozen approved vector lane, and the current buyer-safe boundary: compact fast-mode leads on pipelined KV and storage, the RESP lane is compatibility-first, and vector operations are included without pretending we have a broad fast-ANN claim today.
47,642.31 req/s mixed load at 1.884 ms p99.
On the official April 16 harness, compact fast-mode also measured 295,183.83 ops/s on pipelined KV — 1.157× Redis versus managed no-persistence Redis and 1.337× Redis versus Redis appendfsync everysec.
25,644.85 req/s mixed load at 6.044 ms p99.
The async RESP lane measured 155,096.15 ops/s on pipelined KV versus 223,117.22 ops/s for managed Redis. It is the compatibility path, not the performance lead path.
Measured selective vector retrieval with transparent p99 reporting.
On the approved M=12 / ef_construction=32 / ef_search=24 baseline with adaptive exact-filtered planner, the selective 8-bucket filtered lane achieves repeatable sub-50 ms p99 (best 46.966 ms) with fallback 0. Mixed-load maintenance relief reaches ~33× versus full rebuilds on the 8K→10K append-only lane. All results are replay-safe, measured with transparent telemetry, and preserve the 88.7% compression advantage.
Current controlled-deployment performance: sub-50 ms selective filtered p99 and ~33× maintenance relief on the approved bounded lane. Broader mixed-load filtered p99 remains transport-bound.
WAL Storage Comparison
Lower is better for retention, backup, and replication cost.
WAL size is one of AhanaFlow's clearest present-day advantages in deployments that retain meaningful operational state.
Validated Product Signals
Measured proof from the official April 16 hotspot packet.
Use the production readiness packet, ../../reports/vector_hotspots_10k_round9_client_pipelining_summary.json, ../../reports/vector_hotspots_10k_round10_line_drain_summary.json, and ../../VECTOR_STATE_SERVER_V2_CLAIM_BOUNDARY.md as the canonical public claim sources.
Security
Security controls built for production-facing deployments.
AhanaFlow includes practical protections that matter in real environments: authentication, rate enforcement, payload limits, validated command surfaces, and focused test coverage that supports customer deployment reviews.
API key validation with hashed key storage.
- Per-request authentication support
- Hashed storage for API credentials
- Clear fit for authenticated internet-facing services
- Simple operator model for teams deploying privately or publicly
Rate limits, payload enforcement, and connection boundaries are already part of the stack.
- Protects the runtime before expensive work starts
- Supports per-key and per-IP control strategies
- Helps shape more predictable production behavior
- Reduces the need for custom protective layers around the service
Focused suites confirm security and runtime stability.
- 13 of 13 stress tests passed
- 3 of 3 security tests passed
- Strong confidence for controlled rollouts and pilots
- Helpful proof for technical due diligence and platform review
Deploy
Adopt AhanaFlow in the way that best fits your product.
Start with the open platform, bring the runtime into your own environment, and work from a deployment story that keeps state, retrieval, and security aligned.
Evaluate the platform
Review the repository, benchmark artifacts, and product surface to confirm fit for your control-plane or retrieval workload.
Run it in your environment
Deploy AhanaFlow inside private infrastructure, edge systems, or customer-controlled environments where footprint and operational clarity matter. Controlled-deployment HA packaging is included for bounded pilots with leader, follower, sentinel, USS, and VSS layouts.
Scale with support
Use direct deployment support to shape architecture, benchmark fit, and rollout strategy around your product and customer needs.
Ready To Evaluate
Bring one runtime into the workloads where retained-state cost and operational simplicity matter.
AhanaFlow is ready for controlled deployment in the workloads where you want compressed durable state, integrated vector operations, and a public claim surface that matches the measured reality instead of glossing over it.
- 3.42× smaller WAL: compact fast-mode measured 3,835 KB versus 13,116 KB for Redis AOF
- KV + counters + queues + streams + vector: one runtime for control-plane workloads that benefit from consolidation
- 1.157× Redis pipeline: compact fast-mode on the official April 16 pipelined KV lane
- RESP-compatible path: async RESP lane present for Redis-style integration, with slower throughput stated honestly
- Vector operations included: exact + HNSW for sidecar and embedded use, without a broad fast-ANN claim
- HA pilot packaging: leader/follower/sentinel layouts with USS + VSS are ready for controlled pilots, while automatic promotion remains in progress
- Security included: authentication, rate limiting, payload enforcement out of the box