Simplismart: The Fastest Inference Engine You Haven't Heard Of -- And Why It Matters for Agent Governance

By James Aspinwall, co-written by Alfred Pennyworth (my trusted AI) – March 7, 2026, 07:01

When enterprises deploy AI agents at scale, two problems dominate: which model runs where, and who controls what. The first is an inference problem. The second is a governance problem. Simplismart solves the first. WorkingAgents solves the second. Together, they address the full stack that enterprises need to trust AI agents with real work.

This article examines Simplismart in detail – what it is, how it works, where it fits in the inference landscape – and explores where complementary services emerge with WorkingAgents and the broader ecosystem.

Part 1: What Is Simplismart?

Origin Story

Simplismart (legally Verute Technologies Private Limited) is a Bengaluru-based startup founded by Amritanshu Jain (CEO) and Devansh Ghatak (CTO), both BITS Pilani alumni with backgrounds at Oracle and Google. The company raised $7 million in Series A led by Accel, with participation from Shastra VC, Titan Capital, and angel investors including Akshay Kothari (co-founder of Notion). The round valued the company at approximately $35 million. The team is roughly 23 people.

With under $1 million in initial funding, Simplismart built what multiple outlets – VentureBeat, Analytics India Magazine, StartupHub – describe as the fastest inference engine in the world, outperforming Together AI and Fireworks AI on public benchmarks. That’s a bold claim from a 23-person team in Bengaluru, and the numbers back it up.

What It Does

Simplismart is a cloud-agnostic, model-agnostic MLOps platform for training, monitoring, and deploying generative AI workloads. The core thesis: inference optimization is not one-size-fits-all. Different use cases – voice agents, document processing, content generation – require different optimization profiles across latency, throughput, and cost. Simplismart builds workload-specific tuning rather than standardized configurations.

The platform operates as an orchestration and abstraction layer on top of GPU infrastructure. You bring your models (or choose from 150+ open-source options), select your hardware, define your performance requirements, and Simplismart optimizes the entire stack from CUDA kernels to serving protocol.

Product Lines

Simplismart packages its inference capabilities into four product categories:

SimpliLLM – Large language model inference. The flagship product. Supports Llama, Mistral, Qwen, Gemma, and custom fine-tuned models across every major GPU class.

SimpliScribe – Speech-to-text. Claims a 30x transcription speed improvement on Whisper, with 30-second audio transcribed in 1 second on a T4 GPU.

SimpliDiffuse – Image generation. Stable Diffusion XL at 1024x1024 in 2.2 seconds.

SimpliSpeak – Text-to-speech. Real-time speech generation with voice bot latency as low as 700ms.

Part 2: The Technical Architecture

Three-Layer Optimization

Simplismart’s performance advantage comes from optimizing at three layers simultaneously, rather than relying on a single technique:

Layer 1: Model Backend

Proprietary backends optimized at the kernel level for matrix multiplication operations. This is where the raw speed comes from – custom CUDA kernels tuned for specific model architectures and GPU configurations. The platform supports multiple quantization strategies (fp16, FP8, BF16, GPTQ, AWQ) and attention mechanisms (Flash Attention 2, Flash Attention 3, paged attention, static/ShadowKV caching). Tensor parallelism from TP1 through TP8 for distributing large models across GPUs.

Layer 2: Serving Protocol

Instead of traditional REST APIs, Simplismart employs gRPC with Protocol Buffers and HTTP/2. This eliminates the serialization overhead of JSON-over-HTTP that most inference providers use. The result: 28% speed improvement over FastAPI-based serving in their benchmarks.

Layer 3: Serving Engine

Built on NVIDIA’s Triton Inference Server with deep integration into vLLM, LMDeploy, and TensorRT. The platform adds Prometheus metrics collection and Grafana dashboards for real-time resource monitoring and observability.

The key differentiator: Simplismart claims these speed gains come with no compromise to quality – they don’t rely solely on aggressive quantization (which degrades output quality) but optimize the serving infrastructure itself.

Infrastructure

Simplismart integrates with 15+ cloud providers and supports deployment in:

Available GPU classes: NVIDIA B200, H200, H100, A100, L40S, A10G, T4 – globally distributed.

Auto-scaling triggers in under 500ms for traffic spikes. Scale-to-zero capability for cost optimization during idle periods. Metric-based scaling (latency thresholds, concurrency, usage) to meet strict SLAs. 99.99% uptime claim.

Use-Case-Specific Optimization Profiles

This is where “one size does not fit all” becomes concrete:

Profile Optimized For Scaling Trigger Latency Target
Voice Agents Streaming STT + TTS Latency thresholds < 100ms
Document Processing VLM/LLM throughput Concurrency High throughput
Content Generation Cost per token Usage volume Cost-optimized

Each profile gets a different combination of quantization, caching strategy, parallelism, and serving configuration. An enterprise running voice agents and document processing on the same models would get two different deployment configurations – same model, different optimization targets.

Part 3: Performance Benchmarks

Raw Numbers

Model GPU Throughput Cost
Llama2-7B A100 11,000 tokens/sec $0.15 per million tokens
Mistral 7B A100 9,000 tokens/sec –
Llama3.1 8B (software only, no HW opt) 501 tokens/sec –
Llama3.1 8B (sustained load) ~350 tokens/sec median –
Whisper (30s audio) T4 1 second –
SDXL 1024x1024 – 2.2 seconds –

The Llama3.1 8B result is the headline: 501 tokens/second without any hardware optimization, purely through software-level tuning. Under sustained load, the median drops to ~350 tokens/second, which Simplismart claims “remains significantly higher than any other inference engine on the market.”

Competitive Context

Simplismart’s primary competitors in the inference space:

Competitor Approach Differentiator
Together AI Inference API + fine-tuning Large model catalog, research partnerships
Fireworks AI Optimized inference + function calling Compound AI system support
Groq Custom LPU hardware Hardware-level speed (but proprietary silicon)
Modal Serverless GPU compute Developer-friendly, pay-per-use
Replicate One-click model deployment Simplicity, open-source model hosting
Baseten GPU inference infrastructure Custom model deployment, Truss framework
Amazon Bedrock Managed foundation model API AWS integration, enterprise trust
Google Vertex AI Managed ML platform GCP integration, TPU support

Simplismart’s angle: they compete on software optimization rather than hardware differentiation (Groq) or cloud lock-in (Bedrock, Vertex). The platform works across any cloud and any GPU, which means enterprises aren’t locked into a single provider’s silicon or cloud.

The Benchmarking Suite

Simplismart built a first-of-its-kind benchmarking framework for GenAI inference in real-world conditions:

Performance Benchmarking measures time-to-first-token (TTFT), time-per-output-token, and output throughput under configurable concurrent user loads and traffic patterns (constant or spiky).

Quality Benchmarking evaluates model accuracy using 50+ preloaded datasets spanning text generation, coding, reasoning, STEM tasks, and multilingual evaluation.

The suite enables multi-model comparison under identical conditions – same dataset, token sizes, load patterns, and metrics – eliminating vendor bias. This is useful not just for Simplismart’s own platform but as an evaluation tool for any inference deployment.

Part 4: Customer Results

InVideo (Video Creation Platform)

Image generation costs dropped from $30,000 to under $1,000 – a 97% cost reduction. Inference time halved. This is the kind of result that makes CFOs pay attention.

1mg / Tata Health (Medical Prescriptions)

Medical prescription processing at 95% accuracy. Healthcare workloads demand both speed and precision – Simplismart delivered both without sacrificing quality through quantization.

Dashtoon (AI Comics)

Peak GPU usage reduced from 15 to 6 units while meeting latency targets. Same throughput, 60% fewer GPUs. This is pure infrastructure cost savings.

Mindtickle (Sales Enablement)

Infrastructure speed improvements with continuous optimization. Simplismart’s model: not a one-time deployment but ongoing performance tuning.

Pipeline

30 enterprise customers in the pipeline including Invideo, Dashtoon, Dubverse, Vodex, and Lica.

Part 5: Enterprise Posture

Security and Compliance

These certifications matter for enterprise buyers who need to run inference within regulated environments – healthcare, finance, government.

NVIDIA Partnership

Simplismart is an NVIDIA Inception Program member with deep integration into NVIDIA’s stack:

The company showcased at NVIDIA’s AI Innovation Pavilion and the India AI Impact Summit 2026. They’re positioning as the optimization layer that cloud providers can offer to their enterprise customers – when a cloud provider adds NVIDIA GPUs, Simplismart provides the MLOps abstraction that makes those GPUs useful for GenAI workloads.

Cloud Provider Model

This is a strategic nuance worth understanding: Simplismart doesn’t just serve enterprises directly. They also serve cloud providers as a white-label MLOps layer. When a regional or niche cloud provider wants to offer AI inference capabilities on NVIDIA infrastructure, they can deploy Simplismart’s platform rather than building their own optimization stack.

This creates a multiplier effect – every cloud provider partnership expands Simplismart’s reach without proportional sales effort.

Part 6: Where Simplismart Meets WorkingAgents

The Stack

[Enterprise User / Agent]
        |
[WorkingAgents Control Plane]
  - Permission enforcement
  - LLM routing rules
  - Audit trail
  - MCP tool gateway
        |
[LLM Router Decision]
  - Which model for this task?
  - Which provider for this user?
  - Cost vs latency tradeoff?
        |
[Simplismart Inference Layer]    or    [Provider API]
  - Workload-specific optimization       (Anthropic, OpenAI, etc.)
  - GPU allocation
  - Auto-scaling
  - Performance monitoring
        |
[GPU Infrastructure]
  (Any cloud, any GPU)

Complementary Capabilities

WorkingAgents routes LLM requests through a multi-provider system – Anthropic, OpenRouter (100+ models), Perplexity, Gemini, Gemini CLI. Each provider is an API endpoint with different cost/performance characteristics.

Simplismart adds a new dimension: self-hosted inference with performance that matches or exceeds the hosted APIs, at dramatically lower cost. An enterprise could:

  1. Use Anthropic’s Claude for complex reasoning tasks (via WorkingAgents’ LLM router)
  2. Use Simplismart-hosted Llama 3.1 70B for routine tool-use tasks (at $0.15/M tokens vs $3/M tokens)
  3. Use Simplismart-hosted Whisper for voice agent transcription
  4. Use Simplismart-hosted SDXL for image generation

All routed through WorkingAgents’ permission system, so the user never knows which backend is handling their request – they just get the right model for the right task at the right cost, with full audit trail.

The Governance Gap in Inference

Simplismart optimizes the engine. It does not control who drives or where. Their platform has:

These are precisely what WorkingAgents provides. The combination:

Capability Simplismart WorkingAgents
Model deployment Yes No
Inference optimization Yes No
Auto-scaling Yes No
GPU management Yes No
LLM routing by user/policy No Yes
Per-user permissions No Yes
MCP tool gateway No Yes (60+ tools)
Audit trail No Yes
Multi-provider routing No Yes (5 providers)
A2A agent gateway No Planned

Integration Scenarios

Scenario 1: Cost-Optimized Agent Deployment

An enterprise wants to run AI agents for 500 employees. Claude Sonnet for complex tasks costs ~$15/M input tokens. For the 90% of requests that are routine (task queries, CRM lookups, simple summaries), a Simplismart-hosted Llama 3.1 70B at $0.15/M tokens would reduce costs by 100x on those requests. WorkingAgents’ router decides which requests go where based on complexity, user tier, or department budget.

Scenario 2: Regulated Industry Deployment

A healthcare company needs AI agents that process patient data. Data cannot leave their VPC. Simplismart deploys models in the customer’s private infrastructure. WorkingAgents enforces that only authorized clinicians can access patient-facing tools, that every interaction is logged, and that the models used meet compliance requirements (no data sent to external APIs).

Scenario 3: Voice Agent Infrastructure

A customer service operation needs sub-100ms voice agent responses. Simplismart provides the optimized STT (SimpliScribe) and TTS (SimpliSpeak) with latency-optimized deployment profiles. WorkingAgents manages the agent’s access to business tools (CRM, ticketing, knowledge base) and ensures the agent can only perform actions the authenticated user is authorized for.

Part 7: The Broader Inference Landscape

Where Simplismart Sits Among GTC Exhibitors

NVIDIA GTC 2025 and 2026 featured the full inference stack:

NVIDIA’s Own Stack: NIM (NVIDIA Inference Microservices), TensorRT-LLM, Triton Inference Server, Dynamo (open-source GPU orchestration). Simplismart builds on top of all of these.

NVIDIA AgentIQ (NeMo Agent Toolkit): Framework-agnostic agent coordination with native MCP support. This is the agent orchestration layer – it would sit between WorkingAgents’ governance plane and Simplismart’s inference layer.

Dify.AI (GTC 2025, Booth #3226): Open-source visual agent builder with NVIDIA NIM integration. A visual design tool that could define workflows, which WorkingAgents governs and Simplismart powers.

Fortanix (GTC 2026): Confidential AI with hardware-level encryption. The data security layer beneath inference – complementary to both Simplismart’s deployment and WorkingAgents’ governance.

Inference Provider Comparison

Provider Speed Cost Self-hosted Cloud-agnostic Custom Models
Simplismart Fastest (software opt) Lowest Yes Yes (15+ clouds) Yes
Groq Fastest (hardware) Low No (proprietary LPU) No Limited
Together AI Fast Moderate No No Yes
Fireworks AI Fast Moderate No No Yes
Modal Variable Pay-per-use No No Yes
Replicate Variable Pay-per-use No No Yes (community)
Baseten Fast Moderate Yes (Truss) Limited Yes
AWS Bedrock Variable Premium Within AWS No Limited

Simplismart’s unique position: fastest on software optimization alone, deployable anywhere (including customer VPC), with workload-specific tuning profiles. The only comparable self-hosted option is Baseten with Truss, but without Simplismart’s kernel-level optimizations.

Groq is the speed benchmark, but it requires proprietary LPU hardware – you can’t run Groq on your own GPUs. Simplismart achieves competitive speeds on standard NVIDIA hardware, which any enterprise already has or can procure.

The Inference Layer as Commodity vs. Differentiation

A question for the market: does inference become a commodity (undifferentiated, price-compressed) or a differentiation layer (workload-specific optimization creates lasting value)?

Simplismart bets on differentiation. Their thesis: the same model deployed for a voice agent vs. a document processor vs. a content generator should have fundamentally different optimization profiles. Generic inference APIs (Together, Fireworks, Bedrock) give you one configuration. Simplismart gives you a profile tuned to your specific workload.

This aligns with how WorkingAgents thinks about LLM routing: the right model for the right task. Simplismart extends that to: the right optimization profile for the right model for the right task. Two layers of routing intelligence.

Part 8: Service Opportunities

For AI Consulting Firms

The Simplismart + WorkingAgents combination creates a consultable stack:

Assessment Phase: Profile the client’s AI workloads – what models, what use cases, what latency/throughput/cost requirements. Benchmark using Simplismart’s Benchmarking Suite.

Design Phase: Map out which workloads go to hosted APIs (Anthropic, OpenAI) vs. self-hosted inference (Simplismart). Define permission boundaries in WorkingAgents.

Deployment Phase: Deploy Simplismart in the client’s VPC or cloud. Configure WorkingAgents’ LLM router to distribute requests. Set up MCP tools for internal systems.

Operations Phase: Monitor via Simplismart’s Grafana dashboards (inference performance) and WorkingAgents’ audit trail (agent behavior). Continuous optimization.

For Cloud Providers

Regional cloud providers wanting to offer AI services can white-label Simplismart for inference and WorkingAgents for governance. The value proposition to their enterprise customers: “Run AI agents on your data, in your region, with full audit trail and compliance – no data leaves your infrastructure.”

For Regulated Industries

Healthcare, financial services, government, and defense need:

This is a package deal. Neither product alone solves the regulated industry problem. Together, they do.

Summary

Simplismart is a 23-person team from Bengaluru that built the fastest inference engine in the world through kernel-level software optimization, beating Together AI and Fireworks AI on benchmarks. They raised $7M from Accel at a $35M valuation. Their platform deploys on any cloud, any GPU, with workload-specific tuning profiles that reduce costs by 40-97% while maintaining or improving latency. NVIDIA Inception member. SOC 2 and ISO 27001 certified. 30 enterprise customers in pipeline.

For WorkingAgents, Simplismart represents the missing inference layer: self-hosted, cloud-agnostic model deployment that slots beneath the governance control plane. WorkingAgents routes and governs. Simplismart optimizes and serves. The enterprise gets speed, control, and a paper trail.

The inference market is crowding. Groq leads on hardware speed, Together AI and Fireworks AI lead on developer experience, Bedrock and Vertex lead on cloud integration. Simplismart’s wedge is software-level optimization on standard hardware, deployable anywhere – including behind the enterprise firewall where governance matters most.

That’s exactly where WorkingAgents operates.

Sources